Jim Storer
CS Department Information
How to Contact Me
Biographical Sketch and Research Interests
Books
Patents
Data Compression Conference
Brandeis Data Compression Group
Other Research
Lunar Landing Game
Useful Advice
The Brandeis Computer Science Department
The Brandeis Computer Science department home page is:
http://www.cs.brandeis.edu
For questions, contact our Department Office: 7817362700
Contact Information
Prof. James A. Storer
Computer Science Department
Brandeis University
Waltham, MA 02454
email: storer@brandeis.edu
web: www.cs.brandeis.edu/~storer
Biographical Sketch and Research Interests
James A. Storer
received
his B.A. in Mathematics and Computer Science from Cornell University in 1975,
his M.A. in Computer Science from Princeton University in 1977,
and his Ph.D. in Computer Science from Princeton University in 1979.
From 1979 to 1981 he was a researcher (MTS) at Bell Laboratories
in Murray Hill, New Jersey.
In 1981 he came to Brandeis University,
where he served as Chair of the Computer Science Department from 1992 to 2002
and is currently Full Professor of Computer Science
and member of the Brandeis Center for Complex Systems.
Dr. Storer's research interests include
computer algorithms,
communications and internet related computing,
data compression and archiving (including text, images, video, and multimedia),
storage and processing of large data sets,
image retrieval,
object recognition,
text, image, and video processing,
parallel computing,
machine learning.
He has obtained U.S. patents,
does computing technology consulting,
including serving as an expert witness in patent litigation,
and has served as Chair of the annual Data Compression Conference
(proceedings published by the IEEE Computer Society Press).
Books
Hyperspectral Data Compression
Giovanni Motta, Francesco Rizzo, James A. Storer, Editors
This book provides a survey of recent results in the field of compression of
remote sensed 3D data, with a particular interest in hyperspectral imagery.
Remote sensed data present special challenges in the acquisition, transmission,
analysis, and storage process. Perhaps most significant is the information
extraction process. In most cases accurate analysis depends on high quality data,
which comes with a price tag: increased data volume. For example, the NASA JPL's
Airborne Visible/Infrared Imaging Spectrometer (AVIRIS,
http://aviris.jpl.nasa.gov) records the visible and the near infrared spectrum of
the reflected light of an area 2 to 12 kilometers wide and several kilometers long
(depending on the duration of the flight) into hundreds of non overlapping bands.
The resulting data volume typically exceeds 500 Megabytes per flight and it is
mainly used for geological mapping, target recognition, and anomaly detection. On
the other hand, ultraspectral sounders such as the NASA JPL's Atmospheric Infrared
Sounder (AIRS, http://www airs.jpl.nasa.gov), which has recently become a
reference in compression studies on this class of data, records thousands of bands
covering the infrared spectrum and generates more than 12 Gigabytes of data daily.
Chapter 1 addresses compression architecture and reviews and compares compression
methods. Chapter 2 through 4 focus on lossless compression (where the decompressed
image must be bit for bit identical to the original). Chapter 5 (contributed by
the editors) describes a lossless algorithm based on vector quantization with
extensions to near lossless and possibly lossy compression for efficient browsing
and pure pixel classification. Chapters 6 deals with near lossless compression
while Chapter 7 considers lossy techniques constrained by almost perfect
classification. Chapters 8 through 12 address lossy compression of hyperspectral
imagery, where there is a tradeoff between compression achieved and the quality of
the decompressed image. Chapter 13 examines artifacts that can arise from lossy
compression.
SpringerVerlag, 2005
(425 pages, 6" x 9", hardbound)
ISBN: 0387285792
www.springer.com
An Introduction to Data Structures and Algorithms
James A. Storer
Data structures and algorithms are presented at the undergraduate and
firstyear graduate level. The 13 chapters are:
RAM Model, Lists, Induction and Recursion, Trees, Algorithm Design,
Hashing, Heaps, Balanced Trees, Sets Over a Small Universe, Graphs,
Strings, Graphs, Discrete Fourier Transform, Parallel Computation.
Except for the introduction and bibliographic notes for each chapter, all
page breaks have been put in manually and the format corresponds to a
lecture style with ideas being presented in "digestible" pagesize
quantities. Chapters 1 through 4 cover elementary concepts and data
structures at a slower pace than the remainder of the book. A more
advanced course can essentially start with Chapter 5 (algorithm design).
Although it is not clear what parallel architectures will prevail in the
future, the last chapter further teaches fundamental concepts in algorithm
design by exploring classic models of parallel computation, including the
PRAM, generic PRAM simulation, HC/CCC/Butterfly, the mesh, and parallel
hardware areatime tradeoffs.
SpringerVerlag, 2002)
(600 pages, 7" x 10", hardbound)
ISBN 0817642536, ISBN 3764342536
www.springer.com
More information, including table of contents and errata.
Data Compression: Methods and Theory
James A. Storer
The first two chapters contain introductory material on information and
coding theory. The remaining four chapters cover some of my data
compression research performed in the period 1977  1985 (including
substantial material that has not been reported elsewhere). Chapter 3
considers online textual substitution methods that employ "learning"
heuristics to adapt to changing data characteristics. Chapter 4
considers massively parallel algorithms for online methods and their
VLSI implementations. Chapter 5 considers offline methods (including
the NPcompleteness of certain methods). Chapter 6 addresses program
size (Kolmogorov) complexity. The appendices present source code and
empirical results.
Computer Science Press (a subsidiary of W. H. Freeman Press), 1988
(419 pages 6" x 9", hardbound)
ISBN 0881751618
Image and Text Compression
James A. Storer, Editor
This is an edited volume of papers by researchers in the field;
topics include: vector quantization, fractals, optical algorithms,
massively parallel hardware, and coding theory. Also included is a 75
page bibliography of data compression research compiled
specifically for this book.
Kluwer Academic Press (part of Springer), 1992
(354 pages, 6" x 9", hardbound)
ISBN 0792392434
Proceedings Compression and Complexity of Sequences
Bruno Carpentieri, Alfredo De Santis, Ugo Vaccaro, and James A. Storer, Editors
This is an edited volume of the papers presented at the International
Conference on Compression and Complexity of Sequences, held in Positano,
Italy in 1997. Papers address the theoretical aspects of data
compression and its relationship to problems on sequences, and include
contributions from the editors.
IEEE Computer Society Press, 1998
(400 pages, 6" x 9", hardbound)
ISBN 0818681322
Proceedings, Data Compression Conference
James A. Storer, Chair 19912012, CoChair 2013present
Proceeding from the
Annual Data Compression Conference
are published each year;
they are edited by the conference chair(s) and the program committee chair(s).
Each volume has ten page
extended abstracts of the presentations at technical sessions and one page
abstracts of presentations at the posters session.
Research areas addressed at DCC:
Lossless and lossy compression algorithms for specific types of data
(text, images, multispectral and hyperspectral images, palette images,
video, speech, music, maps, instrument and sensor data, space data,
earth observation data,
graphics, 3D representations, animation, bitmaps, etc.),
source coding,
joint sourcechannel coding,
multiple description coding,
quantization theory,
vector quantization (VQ),
multiple description VQ,
compression algorithms that employ transforms
(including wavelet transforms),
bilevel image compression,
gray scale and color image compression,
video compression,
geometry compression,
speech and audio compression,
compression of multispectral and hyperspectral data,
compression of science, weather, and space data,
source coding in multiple access networks,
parallel compression algorithms and hardware,
fractal based methods,
error resilient compression,
adaptive compression algorithms,
string searching and manipulation used in compression applications,
closestmatch retrieval in compression applications,
browsing and searching compressed data,
content based retrieval employing compression methods,
minimal length encoding and applications to learning,
system issues relating to data compression
(including error control, data security, indexing, and browsing),
medical imagery storage and transmission,
compression of web graphs and related data structures,
compression applications and issues for computational biology,
compression applications and issues for the internet,
compression applications and issues for mobile computing,
applications of compression to file distribution and software updates,
applications of compression to files storage and backup systems,
applications of compression to data mining,
data compression standards
(including the JPEG, MPEG, G.xxx, and H.XXX families).
1991  present
(6 by 9 inch, approximately 500 pages hardbound)
IEEE Computer Society Press
10662 Los Vaqueros Circle
Los Alamitos, CA 90720
phone: 7148218380
tollfree phone: 8006784333
fax: 7148214641
Patents
 Computational Modeling of Rotation and Translation Capable Human Visual Pattern Recognition

U.S. Provisional Patent Application.
INVENTORS: John Lisman, James A. Storer, Martin Cohn, Antonella DiLillo
FILED: August 29, 2005
(assigned to Brandeis University)
Addresses rotation and translation invariant recognition.
 InPlace Differential File Compression

United States Patent 7,079,051
INVENTORS: James A. Storer and Dana Shapira
FILED: March 18, 2004
GRANTED: July 18, 2006
(23 claims, 6 of them independent)
Addresses inplace differential file compression methods that can be used
in software update and backup systems.
 Method and Apparatus for Data Compression

United States Patent 5,379,036
INVENTOR: James A. Storer
FILED: April 1, 1992
GRANTED: January 1, 1995
(23 claims, 3 of them independent)
Addresses high speed parallel algorithms and hardware
for data compression.
 System for Dynamically Compressing and Decompressing Electronic Data

United States Patent 4,876,541
INVENTOR: James A. Storer
FILED: October 15, 1987
GRANTED: October 24, 1989
(55 claims, 5 of them independent)
Addresses dictionary based adaptive data compression.
 Mechanical puzzle with hinge elements, rope elements, and knot elementsn

United States Patent 8,393,623
INVENTOR: James A. Storer
FILED: October 29, 2009
GRANTED: March 12, 2013
(8 claims, 3 of them independent)
Addresses designs for a mechanical puzzle that may be realized as a puzzle game.
Data Compression Conference
Brandeis Data Compression Group
Data compression
is the encoding of data that is typically used to reduce storage and
communication bandwidth requirements.
Other applications that employ minimal size representations include machine learning and information retrieval.
Lossless
compression is when the
decompressed data must be identical to the original;
lossy
compression is
when the decompressed data may be an acceptable approximation.
Here we use the term "data" broadly to refer to most anything
that is represented in digital format (text, images, video, audio, etc.).
We are also interested in related issues such as file similarity,
image retrieval,
and texture analysis.
Past work of the Brandeis Data Compression Group includes:
Motion Sprites
Protcting Images from Adversarial Attacks
Semantic Perceptual Image Compression using Deep Learning
Visual Lecture Summarization
Object Recognition
Content Based Image Retrieval
Texture Analysis
Hyperspectral Image Compression and Processing
String Similarity, Differential Compression, and Incremental Backup
Error Resilient Communication
Adaptive Image Compression
Lossless Image Compression
Video Compression
SubLinear Algorithms
Systolic Algorithms
Text Compression
 Motion Sprites:

We have extended the idea of sprite coding to videos containing a wide variety of naturally occurring background motion,
which could potentially be incorporated into existing and future video standards.
The existing MPEG4 part 2 standard,
now almost 20 years old,
provides the ability to store objects in separate layers,
and includes a sprite mode where the background layer is generated by
cropping a still image based on framewide global motion parameters,
but videos containing more general background motion cannot be effectively encoded with sprite mode.
We have developed a perceptually motivated lossy compression algorithm,
where oscillatory background motion can be compactly encoded.
Our model achieves a low bit rate by referencing a timeinvariant representation of the
optical flow with only a few added parameters per frame.
At very low bit rates,
our technique can provide dynamic backgrounds at a visual quality that may not be achievable
by traditional methods which are known to produce unacceptable blocking and ringing artifacts.
In the figure below,
(left) Sample still frames taken from decompressed video as encoded in camera (click to see full size);
(middle) complex valued mode of horizontal motions;
(right) vertical motions, taken from a still frame of filtered motions in the time domain.
To represent complex numbers using color,
complex valued images are decoded as though they were in HSV format,
with phase mapped to hue and amplitude mapped to value.
Here is a depiction of our video encoding pipeline.
On the left modal parameters are estimated from a short video clip,
and on the right;
motion vectors are filtered and projected onto the modal basis to generate global motion parameters.
The figure below compares snapshots from video clips.
Figure 5: Left column: Sample frames from the original MP4 videos as encoded by the camera.
Middle Left column: Expanded view of the box portion shown in the left column taken from AVC encodings.
Middle Right Column: Expanded view of the box portion shown in the left column taken from HEVC encodings.
Right column: Expanded view of the box portion shown in the left column taken from our decoded videos.
Numbers below each frame represent file size (left) and PSNR compared with the original video as compressed in camera (right).
PSNR numbers are an average over the video frames of the luminance channel as computed by ffmpeg.
Her is a reference to a paper presents this work in more detail;
and by clicking on the paper title,
a pdf can be uploaded that has the figure above,
and where each of its images can be clicked on
to see the corresponding video clip.
 Protecting Images from Adversarial Attacks:

As deep neural networks (DNNs) have been integrated into many
commercial systems, methods have been developed to attack images
that are subjects of classification or object recognition by
such systems. An attacker can carefully manipulate pixel values
with imperceptible perturbations to cause a model to misclassify
images. In fact, the misclassification can be targeted to cause a
modelN to missclassify an image to a particular chosen class.
The figure below shows on the left the original image, in the
center adversarial noise, and on the right an image produced by
the adversary that looks to the eye the same as the original but
gets misclassified by a stateoftheart classifier. The left
image is classified as elephant, whereas the right image is
classified as guacamole.
We present an algorithm to process an image so that
classification accuracy is significantly preserved in the
presence of such adversarial manipulations. Image classifiers
tend to be robust to natural noise, and adversarial attacks tend
to be insensitive to object location. We leverage model
robustness to defend against adversarial perturbations by forcing
the image to match natural image statistics. With only a modest
increase in encoding time our method produces images with high
visual quality while greatly reducing the potency of
stateoftheart attacks. For the case of jpeg compressed images,
a version of our method produces a new protected JPEG image that
can be decoded by any offtheshelf JPEG decoder.
For further information and references see:

A. Prakash, N. Moran, S. Garber, A. DiLillo, and J. A Storer [2018].
"Deflecting Adversarial Attacks with Pixel Deflection",
CVPR.

A. Prakash, N. Moran, S. Garber, A. DiLillo, and J. A Storer [2018].
"Protecting JPEG Images Against Adversarial Attacks",
IEEE Data Compression Conference, 139148.
 Semantic Perceptual Image Compression using Deep Learning:

It has long been considered a significant problem to improve the visual
quality of lossy image and video compression. Recent advances in
computing power together with the availability of large training data
sets has increased interest in the application of deep learning CNNs to
address image recognition and image processing tasks. We have employed a
powerful CNN tailored to the specific task of semantic image
understanding to achieve higher visual quality in lossy compression. A
modest increase in encoding complexity allows a standard offtheshelf
JPEG decoder to be used for decoding. While JPEG encoding may be
optimized for generic images, the process is ultimately unaware of the
specific content of the image to be compressed. Our technique makes JPEG
contentaware by designing and training a model to identify multiple
semantic regions in a given image. With the same compression ratio and
same PSNR, our algorithm can provide better quality in areas of interest
in an image. For example, the figure below compares closeup views of a
salient object in a standard JPEG and our new contentaware method:
Unlike object detection techniques, our model does not require labeling
of object positions and is able to identify objects in a single pass.
Our a new CNN architecture directed specifically to image compression,
generates a map that highlights semanticallysalient regions so that
they can be encoded at higher quality as compared to background regions.
For example, the figure below shows a comparison of our model (MSROI)
with CAM and human fixation; our model identifies the face of the boy on
the right, as well the hands of both children:
By adding a complete set of features for every class, and then taking a
threshold over the sum of all feature activations, we generate a map
that highlights semanticallysalient regions so that they can be encoded
at a better quality compared to background regions. In experiments with
the Kodak PhotoCD dataset and the MIT Saliency Benchmark dataset, our
algorithm achieves higher visual quality for the same compressed size
(and same PSNR). For example, the figure below shows a sample of five of
these images, along with the corresponding heat maps generated by our
algorithm; the first four show typical results which strongly capture
the salient content of the images, while the fifth is a rare case of
partial failure, in which the heat map does not fully capture all salient
regions.
Our variable compression improves upon visual quality without
sacrificing compression ratio. Encoding requires a single inference over
the pretrained model, the cost of which is reasonable when performed
using a GPU card, along with a standard JPEG encoder. The cost of
decoding, which employs a standard, offtheshelf JPEG decoder remains
unchanged. We believe it will be possible to incorporate our approach
into other lossy compression methods such as jpeg 2000 and vector
quantization, a subject of future work.
For further information and references see:

A. Prakash, N. Moran, S. Garber, A. DiLillo, and J. A Storer [2017].
"Semantic Perceptual Image Compression using Deep Convolution Networks",
IEEE Data Compression Conference, 250259.

A. Prakash and J. A. Storer [2016].
"Highway Networks for Visual Question Answering",
VQA Workshop,
CVPR 2016.
 Visual Lecture Summarization:

We are interested in automatic techniques for creating a video summary of a
chalkboard lecture with the speaker removed, and for generating a set of slides
containing all of what was written but without the lecturer present. Our system
works by continuously subtracting the speaker from the video feed using a region
based correlation feature. From this new version of the video that no longer
contains the speaker the final presentation slides are extracted.
As shown in the figure on the left below,
in the sampling stage, we take a nonuniform sampling from the input feed based
on absolute frame difference. Next, in the foreground subtraction stage, we
calculate the localized correlation coefficient computed over a sliding window,
and apply a threshold to the results to obtain a foreground mask. Once the mask
is obtained we use it to update coefficients of a background image pyramid. This
pyramid is then reconstructed and added to the output stream. The bottom level
of this pyramid, containing high frequency edge components over the whole frame,
is weighted using a Gaussian window and then the L1 norm is taken over the
weighted frame for further processing in the key frame detection stage.
We have tested our system on blackboard and whiteboard videos gathered from
YouTube. The output of the foreground subtraction stage reliably contained all
content written on the board during the lecture with minimal ghost artifacts.
The final slideshows generated by the peakfinding stage contained the desired
slides. For example, the figure on the right above shows at the top one of the
original frames of a 50 minute video of a professor lecturing on a blackboard,
and below that 7 summary slides constructed by our system which contain all of
the content written on the blackboard with the lecturer removed. Note the cloudy
areas of the board in slides 5 and 6 are due to natural sunlight that came in
during the lecture, and are not an artifact of our process. In addition to these
7 slides the system also produced three redundant slides. Although it is easy to
flip through the final slides and discard the redundant ones, we are currently
working on an automatic method.
For further information and references see:

S. Garber, L. Milekic, A. Prakash, N. Moran, A. Di Lillo, and J. A. Storer [2017].
"A Two Tier Approach To Blackboard Video Lecture Summary",
IMVIP  Irish Machine Vision and Image Processing Conference, Maynooth University, Ireland,
pages 6875; see http://eprints.maynoothuniversity.ie/8841/1/IMVIP2017_Proceedings.pdf.

S. Garber, L. Milekic, A. Prakash, N. Moran, A. DiLillo, and J. A. Storer [2017].
"Visual Lecture Summary Using Intensity Correlation Coefficient",
FIE  Frontiers in Education Conference, Indianapolis, IN.
 Object Recognition:

We have addressed the problem of retrieving the silhouettes of objects
from a database of shapes with a translation and rotation invariant
feature extractor successfully used by the same authors to classify
textures. We retrieve silhouettes by using a "soft" classification
based on the Euclidean distance. Experiments show significant gains in
retrieval accuracy over previous work when applied
to the MPEG7 CEShape 1 database. An interesting aspect of this
approach is the use of a unique descriptor for both texture and shape
recognition.
We are interested to applying object recognition algorithms to
location and assistance algorithms.
We have developed tools that can be used within a larger system referred to as a
passive assistant.
The system receives information from a mobile device,
as well as information from an image database such as
Google Street View,
and employs image processing to provide useful information about a local
urban environment to a user who is visually impaired. The first stage
acquires and computes accurate location information, the second stage
performs texture and color analysis of a scene, and the third stage
provides specific object recognition and navigation information. These
second and third stages rely on compressionbased tools (dimensionality
reduction, vector quantization, and coding) that are enhanced by
knowledge of (approximate) location of objects.
In a preprocessing phase (that is done only once at
image acquisition time) a set of key points with associate feature vectors is calculated for each relevant database image, and then a small
set of corresponding points in two overlapping database images (e.g.,
successive images in Google Street View) are identified. The intersection
of the two lines of sight from the acquisition coordinates to a pair of
corresponding key points provides their coordinates. Appropriate ranking
and weighted averaging of matching pairs can be used to refine accuracy
and confidence.
The preprocessing step creates geolocated key points along with their
associated feature vectors. When a user image has at least three points
that can be matched with these database key points, a resection procedure
like that used in land surveying can be employed: that is, from the
apparent angles between these three points, as seen by the user, and the
(already computed) locations of the points, we compute the location of
the user. In the figure below, the top and bottom images are two images from the
Google Street View database, and the middle image is the user image.
Feedback between this computation and revised estimates of position based
on object recognition allows for a precise location of the user
that can be the basis for user assistance
(e.g., at doorways and intersections).
For further information and references see:

A. DiLillo, A. Daptardar, G. Motta, K. Thomas, and J. A. Storer [2012].
"CompressionBased Tools for Navigation with and Image Database",
Algorithms 5, 117.

A. DiLillo, A. Daptardar, G. Motta, K. Thomas, and J. A. Storer [2012].
"Applications of Compression to Content Based Image Retrieval and Object Recognition",
Proceedings International Conference On Data Compression, Communication, and Processing (CPP 2011),
Palinuro, Italy, 179  189.

A. DiLillo, G. Motta, and J. A. Storer [2010].
"A Rotation And Scale Invariant Descriptor For Shape Recognition",
Proceedings International Conference On Image Processing (ICIP),
Hong Kong, MAL9:1, 257260.

A. DiLillo, G. Motta, K. Thomas, and J. A. Storer [2010].
"Shape Recognition, With Applications To A Passive Assistant",
Proceedings PETRA  PErvasive Technologies Related to Assistive Environments,
Samos, Greece, June 2325;
also in ACM International Conference Proceedings Series.

A. Di Lillo, G. Motta, and J. A. Storer [2010].
"Shape Recognition Using Vector Quantization",
Proceedings Data Compression Conference,
IEEE Computer Society Press, 484493.
 Content Based Image Retrieval:

We have investigated a low complexity approach for contentbased image
retrieval (CBIR) using vector quantization (VQ). The hope is that
encoding an image with a codebook of a similar image will yield a better
representation than when a codebook of a dissimilar image is used. Our
most simple method tags each image with a thumbnail and a small VQ
codebook of only 8 entries, where each entry is a 6 element color
feature vector. If we think of a web crawler where the image database is
being dynamically updated, the preprocessing can be thought of as a
tagging of images as they are gathered. We use a simple mean squared
error computation for the distance function for retrieval.
To test our method, we have used for our database a subset of the COREL
collection consisting of 1,500 JPEG images, 100 in each of 15 classes;
three typical images from each class are shown below. These images are
already relatively small, but still larger than is needed for a
thumbnail. To create the database thumbnails, the system retains the
central region (256 x 256 for these images) and scales it to 128 x 128
for each image (although it is possible to use smaller thumbnails, this
size has allowed us to make direct comparisons with previous work).
The graph shown below compares the precisionrecall tradeoff of our
simple VQ algorithm to that of a more complex method based on GMVQ from
the previous literature. Using the same code, with the appropriate
places modified (distance function, etc.), VQ retrieval used on the
average 1/3 of the time of GMVQ (that is the total time to perform a
retrieval of each of the 1,500 images on the entire 1,500 image set was
more than three times greater for GMVQ than VQ), while achieving the
same or slightly better performance.
We have also considered extending our basic VQ approach presented in
the previous section by adding positional information to each feature
vector. That is, the mean XY coordinates of all blocks associated with a
particular codebook entry are added to that entry (so the dimension of
codebook entries is increased from 6 to 8). The figure below shows some
database images with the code vector positions marked on the image. The
color within each black bordered box represents the color of the code
vector.
In general, the addition of XY coordinate features gives an overall
modest improvement in retrieval quality. However, the real benefit of
adding XY to the feature vector comes as "insurance" on classes with
highly contrasting regions. For example, the first figure below shows
the first 10 images retrieved for one of the dinosaur images with color
only and the second figure below shows the first 10 for color + XY.
Without XY features:
With XY features:
We have performed some additional experiments to incorporate
positional information implicitly. The system computes similarity between
corresponding image regions, where separate VQ codebooks trained on color
feature vectors are associated with each region. To compare a query image with
a database image, the total similarity score is the sum of the scores for each
region, where the score for a region is the MSE when the query image features
for that region are encoded using a database image's VQ codebook for that
region. The size of each region codebook is varied by query and region by
setting the query threshold. This parameter will determine how many codewords
to use when encoding a query. Since the database codebook size needs to be
varied based on the query image statistics, we use treestructured codebooks
(TSVQ) for the database images. After partitioning the image into regions, we
train relatively large TSVQ codebooks by successively splitting nodes with the
largest distortion until we arrive at the desired number of leaf codewords. That
is, before codebooks of a given database image are used to encode the regions of
the query image (to test similarity with that database image), each codebook is
pruned to the same size as the codebook for that region associated with the
query image. The figure below shows two images from the database and the number
of codebook entries for each region using a threshold of 1,500.
This region based approach yields comparable performance to
our previous methods with further reduced computational cost.
For further information and references see:

A. H. Daptardar and J. A. Storer [2008].
"VQ Based Image Retrieval Using Color and Position Features",
Proceedings Data Compression Conference,
IEEE Computer Society Press,
432441.

A. H. Daptardar and J. A. Storer [2006].
"Reduced Complexity ContentBased Image Retrieval using Vector Quantization",
Proceedings Data Compression Conference,
IEEE Computer Society Press, 342351.

A. H. Daptardar and J. A. Storer [2005].
"ContentBased Image Retrieval Via Vector Quantization",
In Advances in Visual Computing  Springer Lecture Notes in Computer Science 3804/2005
(ISBN 3540307508), 502509;
also appeared in International Symposium on Visual Computing, December 57, 2005.
Click here for 2008 Dcc paper experiments.
 Texture Analysis:

We have developed a prototype texture analysis algorithm that achieves state of
the art performance. While there is no agreement on a formal definition of
texture, it is commonly accepted that textures are naturally extracted and
recognized as such by the human visual system. Furthermore, human experiments have
shown that the visual cortex contains orientation and scale bandpass filters for
vision analysis. The vast majority of the methods proposed in the literature are
translation invariant, but assume that textures are presented or acquired at the
same orientation and scale. For many types of image acquisition, this assumption
is unrealistic. Scale is often determined by metadata (e.g., camera settings
recorded in a standard JPEG / EXIF file), but there can be little control over
information about rotation. Rotation invariance can be key to practical texture
analysis. Furthermore, human experiments have shown that images subject to
rotation (and other affine transforms such as scaling and translation) are still
perceived as the same image. It is also widely believed that the visual system
extracts relevant features in the frequency domain, independently of their
orientation.
The basic steps of our feature extractor are shown in the figure below. The
features for an image pixel are collected from a square window around the pixel.
This window is weighed by a 2dimensional Hamming window
before applying a bidimensional Fourier transform, after which the magnitude of
the coefficients is further transformed into polar coordinates. Taking the
magnitude of the Fourier coefficients provides translational invariance. The
representation in the polar coordinates of the Fourier coefficients provides
already an excellent characterization of the textural features. Preliminary
results on the supervised segmentation of a standard database show that this
method matches the performance of the stateoftheart.
Rotating the input image by an angle also rotates the
transformed Fourier coefficient by the same angle, so when the magnitude of the
Fourier transform is mapped to polar coordinates, a rotation becomes a translation
along the angle axis. The application of a second Fourier transform along the
angle axis is sufficient to extract a set of features that are rotationally
invariant.
Capturing textural features requires a large window and results in feature vectors
having many components. If a multiscale method is being used, the number of
components will grow linearly with the number of resolutions used. Besides the
obvious issues of time and space complexity, the components of these vectors are
often highly correlated and/or not useful to discriminate among textures. To
reduce the dimension of the feature vectors we have employed Principal Component
Analysis (PCA). PCA performs an orthonormal transformation on the feature space
that retains only significant eigenvectors of the whole dataset, called principal
components. The feature space is transformed and information is compacted into the
smallest number of dimensions by discarding redundant features. PCA reduces the
number of dimensions without considering their usefulness to the classification.
Feature vectors so constructed are classified using vector quantization. A simple
postprocessing filter inspired by the Kuwahara filter divides a square
neighborhood of each pixel into four overlapping quadrants, each containing the
central pixel. The histogram of the indices and the entropy of the values is
computed in each quadrant, and replaces the central index with the
highestfrequency index belonging to the quadrant with the smallest entropy.
We have compared the performance of our texture analysis algorithm for
both classification and image segmentation problems to the work of others.
For classification on texture databases used by others,
we have achieved 99% overall accuracy,
including rotations of 5, 10, 15, 30, 45, 60, 75, and 90 degrees.
We have also performed experiments on compositions of
texture images from these databases.
For example, consider the two texture problems shown below.
The one on the left is composed of the first 8 images of the
Outex_TC_00010
set of 24 textures (arranged in row major order) and the one on the right is composed of
every third image of this set (arranged in row major order). Rather than
trying to rotate these two problems at different angles, we made 6 different
classifiers, each trained on the 8 textures rotated at a particular angle
(5, 10, 15, 30, 45, 60, 75, and 90 degrees),
and then applied these 6 classifiers to the nonrotated
problems. The performance for each of these angles (percentage of pixels correctly
classified) given in the table below the figure; these figures represent a
significant improvement over the state of the art represented by the work of
previous authors mentioned above.
For another example, the figure below shows on the left one of the more complex
image problems from the database, in the middle the perfect partitioning (random
colors are used) and on the right the partitioning achieved by our algorithm at 0
degrees of rotation. Here the percentage of correctly classified pixels is 82%,
less than what is typical in the example above, but quite reasonable considering
the extent of boundary areas in this problem.
For further information and references see:

A. DiLillo, G. Motta, and J. A. Storer [2008].
"Multiresolution RotationInvariant Texture Classification
Using Feature Extraction in the Frequency Domain and Vector Quantization",
Proceedings Data Compression Conference,
IEEE Computer Society Press,
452461.

A. DiLillo, G. Motta, and J. A. Storer [2007].
"Texture Classification Based on Discriminative Feature Extracted in the Frequency Domain",
Proceedings IEEE International Conference on Image Processing (ICIP),
II.53II.56.

A. DiLillo, G. Motta, and J. A. Storer [2007].
"Supervised Segmentation Based on Texture Signature Extracted in the Frequency Domain",
Third Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA).

A. DiLillo, G. Motta, and J. A> Storer [2007].
"Texture Classification Using VQ with Feature Extraction Based on Transforms Motivated by the Human Visual System",
Proceedings Data Compression Conference,
IEEE Computer Society Press, 392.
 Hyperspectral Image Compression and Processing:

Remote
sensing technology has shifted from panchromatic data (a wide range of
wavelengths merged into a single response), through multispectral (a few
possibly overlapping bands in the visible and infrared range with
spectral width of 100 200nm each), to hyperspectral imagers and
ultraspectral sounders, with hundreds or thousands of bands. In
addition, the availability of airborne and spaceborne sensors has
increased considerably, followed by the widespread availability of
remote sensed data in different research environments, including
defense, academic, and commercial.
In hyperspectral photography,
each pixel records the spectrum (intensity at different
wavelengths) of the light reflected by a specified area, with
the spectrum decomposed into many adjacent narrow bands.
Data compression plays a key role in
the original transmission of imagery
to a central location,
in the archiving of data sets,
and in the distribution of data to users.
We have considered a number of approaches to hyperspectral image compression,
including the ones where
vector quantization is applied independently to each pixel in a
way that preserves uniform quality across all vector components.
That is, high dimensional source vectors are partitioned into a
number of subvectors of (possibly) different length and then
each subvector is individually encoded with an appropriate
codebook and distortion measure. Then an entropy coder that may
exploit spatial correlations is employed for both the VQ indices
and residual. This scheme allows efficient lossless, near
lossless, or lossy compression of high dimensional data sources
such as hyperspectral imagery in which each vector component is
allowed to have different alphabet and distribution. A key
advantage of our approach is the use of independent small VQ
codebooks that allow fast encoding and decoding. Motivated by
the need of maintaining uniform quality across all vector
components, especially for applications needing target
identification, our experiments have used the Percentage
Maximum Absolute Error (PMAE) as the distortion measure.
For further information and references see:

G. Motta, F. Rizzo, and J. A. Storer [2006], editors.
Hyperspectral Data Compression,
Springer
(hard cover, 425 pages).

F. Rizzo, G. Motta, and J. A. Storer [2005].
"Compression of Hyper/UltraSpectral Data",
Proceedings of SPIE,
Optics and Photonics,
Satellite Data Compression,
Communication and Archiving,
Vol. 5889, pp. 588908158890810.

F. Rizzo, B. Carpentieri, G. Motta, and J. A. Storer [2005].
"Low Complexity Lossless Compression of Hyperspectral Imagery via Linear Prediction",
IEEE Signal Processing Letters 12:2, 138141.

F. Rizzo, B. Carpentieri, G. Motta, and J. A. Storer [2004].
"Compression of Hyperspectral Imagery",
Proceedings International Conference on EBusiness and Telecommunications (ICETE),
Sebutal, Portugal, 2004;
an extended version is a chapter in the book EBusiness and Telecommunication Networks,
Edited by J. Ascenso, L. Vasiu, C. Belo, and M. Saramago, Kluwer / Springer (2005).

F. Rizzo, B. Carpentieri, G. Motta, and J. A. Storer [2004].
"RealTime Software Compression and Classification of Hyperspectral Images",
11th SPIE International Symposium on Remote Sensing Europe,
Maspalomas,
Gran Canaria,
Spain, Sept. 1317, 557317.

F. Rizzo, B. Carpentieri, G. Motta, and J. A. Storer [2004].
"Lossless Compression of Hyperspectral Imagery: A Real Time Approach",
11th SPIE International Symposium on Remote Sensing Europe,
Maspalomas,
Gran Canaria,
Spain,
Sept. 1317, 2004, 557325.

G. Motta, F. Rizzo, J. A. Storer, and B. Carpentieri [2004].
"High Performance Compression of Hyperspectral Imagery
with Reduced Search Complexity in the Compressed Domain",
Proceedings Data Compression Conference, IEEE Computer Society Press,
479488.

G. Motta, F. Rizzo, and J. A. Storer [2003].
"Partitioned Vector Quantization: Application to Lossless
Compression of Hyperspectral Images",
Proceedings International Conference on Acoustics, Speech, and Signal Processing (ICASSP);
presented at
IEEE International Conference on Multimedia and Expo (ICME2003).

G. Motta, F. Rizzo, and J. A. Storer [2003].
"Compression of Hyperspectral Imagery",
Proceedings Data Compression Conference,
IEEE Computer Society Press,
333342.
 String Similarity, Differential Compression, and Incremental Backup:

Identifying similar strings is a problem that arises in many applications.
Internet search algorithms and database retrieval algorithms, including tasks in
bioinformatics, can employ effective string similarity testing to process queries.
The completion of the human genome as well as that for other species has produced
large strings in which researchers are looking for structure. Differencing
algorithms used by software updates and backup systems can be improved with string
similarity algorithms. The traditional editdistance problem is to find the
minimum number of insertcharacter and deletecharacter (and sometimes change
character) operations required to transform a string S (the source string) to
become a string T (the target string); that is, starting with S, repeatedly
perform operations until we have T, and do so using a minimum number of
operations. However, there are many other natural operations that can be used to
transform S to become T, including copies, moves, deletes, and reversals of blocks
of characters. However the problem complexity is greater (NPhard in many cases)
and efficient approximation algorithms are needed. If the copy operation is
allowed, then efficient approximation algorithms that employ data compression
techniques can be employed, with applications to differential file compression. In
fact, in many practical applications, differential file compression can be
performed inplace with performance comparable to the best methods that work with
unrestricted memory. Inplace differential file compression has the advantage that
the sender need not make any assumptions about the resources available to the
receiver, or in the case of file backup systems that work in the background,
fewer resources must be claimed. That is, even if other operations are allowed as
well, the copy operation is sufficiently powerful, that it alone can achieve
efficient approximation. We have considered just the move operation (or the move
operation in combination with the insertcharacter and deletecharacter
operations), just delete operations (possibly with insertcharacter operations 
which is not NPhard), or combinations of move and delete operations (and possibly
insertcharacter operations). One way to model the move operation is to view
strings as linked lists (one character per vertex) and allow the insert and delete
operations to be applied to both vertices and links. Although one would not likely
represent strings this way in practice, it points out a fundamental difference
between these operations and the copy operation; that is, move and delete
operations can be done in O(1) time on linked lists whereas copy operations
cannot. For many applications, the rearrangement implied by move operations
(possibly together with delete and insertcharacter operations) may better reflect
string similarity than the ability to compress the string with respect to another,
as is in some sense implied by the copy operation.
Because optimal differential compression and generalized edit distance
is in general NPhard, in past work we have studied approximation
algorithms and their efficient implementation. Shapira and Storer [2002]
consider approximation algorithms for generalized edit distance where
blockmoves are allowed. Shapira and Storer [2003] present the IPSW
algorithm for inplace differential file compression (no more than
MAX(m,n) + O(1) space is used where m is the size of S and n is the size
of T). IPSW uses a sliding window method to decompress T by initializing
the window to S, and restricting its maximum size to max(S,T);
except for the beginning, copies cannot reach all the way back to the
start of S, and while decoding inplace, the no longer accessible
portion of S is overwritten from left to right.
IPSW is fast and the compression achieved compares well with other
existing methods, including those that are not inplace. IPSW is most
effective when there is a reasonable degree of alignment between S and
T. That is, large matches between S and T occur in approximately the
same relative order. Such alignment is typical for many applications,
such as subsequent backups of the same data, where when new data is
inserted, and other data is deleted, the remaining data often comprises
aligned common substrings. We have also considered methods with good
performance inplace even for those
(rare) cases where S and T are not sufficiently well aligned. This could
happen, for example, when for some reason large blocks of the file have
been moved around to form a new version of the file. An extreme case is
when the first and second halves of S have been exchanged to form T (S =
uv and T = vu); to decompress T the IPSW algorithm overwrites u as it
copies v, and then the ability to represent v with a single copy is
lost. Rather than modify IPSW, a very fast and practical method that
suffices for many and perhaps most practical inputs, is a preprocessing
stage for IPSW that moves substrings within S to better align S with T.
Compressed files can be preceded with an initial bit indicating whether
preprocessing has occurred. The encoder can compress T with IPSW and
compare that to compressing T with respect to S not in place (so at all
times all substrings of S are available to be copied). If the difference
is significant, this initial bit can be set, alignment preprocessing
performed, and a list of moves prepended the normal IPSW encoding. The
decoder can perform the moves (inplace) and then proceed inplace with
normal IPSW decoding. For move preprocessing, we distinguish employ two
different kinds of rearrangements of the blocks. Moves rearrange the
blocks so that they occur in S and T at the same order.
"Jigglings" move the junk blocks of S backwards in the file, so that, as a
side affect, the matched blocks are moved forwards.
Move Preprocessing Algorithm:
Step 1: Find Non Overlapping Common Substrings (NOCS) from longest to shortest
down to length of a constant K.
Step 2: Compute the minimum number of rearrangements (moves and jigglings) in S
so that the blocks are left copies within S and T.
Step 3: Generate S' according to step 2.
Step 4: Compute IPSW(S',T).
Under reasonable assumptions, all of the preprocessing steps 1 through 4 can be
done in linear time.
For further information and references see:

D. Shapira and J. A. Storer [2011].
"Edit Distance With Block Deletions",
Algorithms 4, 4060.

D. Shapira and J. A. Storer [2007].
Edit Distance with Move Operations",
Journal of Discrete Algorithms 5:2,
380392.

D. Shapira and J. A. Storer [2005].
"InPlace Differential File Compression",
Computer Journal 48:6,
677691.

D. Shapira and J. A. Storer [2004].
"InPlace Differential File Compression of NonAligned Files
With Applications to File Distribution and Backups",
Proceedings Data Compression Conference (DCC),
IEEE Computer Society Press, 8291.

D. Shapira and J. A. Storer [2003].
"InPlace Differential File Compression",
Proceedings Data Compression Conference (DCC),
IEEE Computer Society Press,
263272.

M. Meyerovich, D. Shapira, and J. A. Storer [2003].
"Finding NonOverlapping Common Substrings in Linear Time",
Proceedings Symposium on String Processing and Information Retrieval (SPIRE),
369377.

D. Shapira and J. A. Storer [2002].
"Generalized Edit Distance with Move Operations", Thirteenth Annual
Symposium on Combinatorial Pattern Matching (CPM),
8598.

F. Mignosi, A. Restivo, M. Sciortino, and J. A. Storer [2000].
"On Sequence Assembly",
Technical Report,
Computer Science Department,
Brandeis University.
 Error Resilient Communication:

There has been much past work concerning the detection and correction of errors on
a communication line. However, with online data compression where a sender and
receiver are in lockstep maintaining constantly changing identical local
memories, a single error on the communication channel or storage medium could
cause the dictionaries of the encoder and decoder to differ, which in the worst
case could corrupt all data to follow. That is, since decoding is only guaranteed
to be correct when the dictionary remains the same as the one used to encode the
data, there is no way to bound the effects of a single error in the worst case
(and, in fact, the expected case is very bad because a single error can corrupt
the mapping between codewords and dictionary entries). With data storage devices
or oneway communication channels, where the decoder cannot send messages to the
encoder, the problem becomes even more critical. In addition, with many existing
communication systems where the full bandwidth of the channel is consumed, even if
a twoway channel is available to let the encoder know that an error has occurred,
retransmission of data can often be tantamount to losing new data that could have
been transmitted during the time used to retransmit the old data. Retransmission
can also introduce further error propagation problems. Even if error detection and
correction methods are available that make the probability of an error getting
through sufficiently low, error propagation prevention may be all that is needed
for noncritical applications such as web browsing, email processing, etc. We have
developed the kerror protocol, a technique for protecting a dynamic
dictionary method from error propagation as the result of any k errors on the
communication channel or compressed file. In addition theoretical results show
that at no asymptotic loss of compression, high probability protection against
error propagation resulting from a sustained error rate is also a benefit of the
protocol. Experiments show that this approach is highly effective in practice with
a low overhead. For LZ2based methods that "blow up" as a result of a single
error, with the protocol in place, high error rates can be sustained without error
propagation (the only corrupted bytes decoded are those that are part of the
string represented by a pointer that was corrupted).
For further information and references see:

J. A. Storer [2000].
"Error Resilient Dictionary Based Compression",
Proceedings of the IEEE 88:11,
17131721.

J. A. Storer and J. H. Reif [1997].
"LowCost Prevention of ErrorPropagation for Data Compression
with Dynamic Dictionaries",
Proceedings1997 IEEE Data Compression Conference,
171180.

J. A. Storer and J. H. Reif [1997].
"Error Resilient Optimal Data Compression",
SIAM Journal of Computing 26:3,
934939.
 Adaptive Image Compression:

We are studying
adaptive
vector quantization
where the codebook is learned as the image is processed in a single pass (i.e.,
the codebook contains a changing set of variablesize vectors). Experiments with
a variety of data types (gray scale and color magazine photos, medical images,
space images, finger prints, handwriting, etc.) show that with no prior knowledge
of the data and no training, for a given fidelity this approach compares
favorably with traditional VQ that employs codebooks that have been trained on
the type of data being compressed (it typically at least equals and often greatly
exceeds the amount of compression achieved by the JPEG standard). We have
developed efficient parallel algorithms and architectures as well as fast
sequential implementations using KDtrees. Key advantages of this approach are
that it is adaptive (no advance knowledge of the data or training is required),
the tradeoff between the fidelity and the amount of compression can be
continuously varied (and automatically controlled), precise guarantees on the
amount of distortion on any small area of the image (e.g., 4x4 array of pixels)
can be provided, and decoding is extremely fast because it involves primarily
table lookup like traditional VQ (encoding is on the same order of complexity as
for traditional VQ). The figure below shows an image of a brain on the left and a
"map" of the decompressed image on the right (each rectangle has been colored
with a random solid color).
For further information and references see:

F. Rizzo, J. A. Storer, and B. Carpentieri [2001].
"LZBased Image Compression",
Information Sciences 135,,
107122.

F. Rizzo and J. A. Storer [2001].
"Overlap in Adaptive Vector Quantization",
Proceedings Data Compression Conference,
IEEE Computer Society Press,
401410.

G. Motta, F. Rizzo, and J. A. Storer [2000].
"Image Compression",
Encyclopeda of Computer Science,
Nature Publishing Group, London, 836840.

F. Rizzo, J. A. Storer, and B. Carpentieri [1999].
"Improving SinglePass Adaptive VQ",
IEEE International Conference on Acoustics, Speech, and Signal Processing,
IMDSP2.10;
also see the article by the same authors:
"Experiments with SinglePass Adaptive Vector Quantization",
Proceedings IEEE Data Compression Conference,
546.

C. Constantinescu and J. A. Storer [1994].
"OnLine Adaptive Vector Quantization with Variable Size Codebook Entries",
Information Processing and Management 30:6,
745758.

C. Constantinescu and J. A. Storer [1994b].
"Improved Techniques for SinglePass Vector Quantization",
Proceedings IEEE (June),
933939.

J. Lin and J. A. Storer [1994].
"Design and Performance of TreeStructured Vector Quantizers",
Information
Processing and Management 30:6,
851862.
Lin).

M. Cohn, E. Lin, and J. A. Storer [1992].
"Optimal Pruning for TreeStructured Vector Quantization",
Information Processing and Management 28:6,
723733.

J. Lin and J. A. Storer [1992].
"Improving Search for TreeStructured Vector Quantization",
Proceedings IEEE Data Compression Conference,
339348.

J. Lin and J. A. Storer [1991].
"Resolution Constrained Tree Structured Vector Quantization for Image Compression",
Proceedings IEEE Symposium on Information Theory,
Budapest, Hungary.

J. A. Storer [1990].
"Lossy OnLine Dynamic Data Compression",
Combinatorics, Compression, Security and Transmission,
SpringerVerlag,
348357.
 Lossless Image Compression:

Lossless image compression, particularly bilevel image compression (where pixels are
0 or 1) has often employed techniques quite separate from those used for text
compression or lossy image compression; most standards today employ modeling followed
by coding (e.g., the JBIG standard, the IBM Qcoder, CCITT Group 4). Constantinescu
and Storer [1994] present a lossy image compression scheme that can be viewed as a
generalization of lossless dynamic dictionary compression ("LZ2" type methods) to two
dimensions with approximate matching; Constantinescu and Storer [1995] have
experimented with this approach for lossless image compression with great success. In
this research we have generalized "LZ1" type methods (that employ displacement/length
pointers to the previously seen text) to lossless image bilevel compression. We make
use of a generalization of the suffix trie data structure for two dimensions that was
introduced by Giancarlo [1995]. In a two dimensional suffix trie, the suffix at a
position (i,j) in the two dimensional input array is just the portion of the array
going to the right and down from that position. We list the characters of a suffix in
the order depicted on the left in the figure below. Similar to the standard convention
used in the one dimensional case, we pad the right and bottom sides of the array with
a special character $ as a notation convenience. On the right in the figure below is
an example of a 3x3 array (padded to a 4x4 array with $'s) and its corresponding 2D
suffix trie. We have generalized the LZ1 approach to two dimensions and confirmed
that, like the 1dimensional case, the complexity of the general problem is
NPcomplete. Motivated by this fact and that 2D suffix tries can only give information
about square shaped matches, we have considered the performance of greedy parsing with
square matches, and have introduced a modified parsing strategy that searches for
rectangular matches based on the best square match. We have also introduced
multishape 2D suffix tries; they are more space consuming but have the ability to
store information about arbitrary rectangular shaped matches at any position.
For further information and references see:

G. Motta, J. A. Storer, and B. Carpentieri [2000].
"Lossless Image Coding via Adaptive Linear Prediction and Classification",
Proceedings of the IEEE 88:11,
17901796.

J. A. Storer and H. Helfgott [1997].
"Lossless Image Compression by Block Matching",
The Computer Journal 40:2/3, 137145.

J. A. Storer and H. Helfgott [1997].
"Generalized Node Splitting and BiLevel Image Compression",
Proceedings IEEE Data Compression Conference, 443.

J. A. Storer [1996].
"Lossless Image Compression using Generalized LZ1Type Methods",
Proceedings IEEE Data Compression Conference,
290299.

C. Constantinescu and J. A. Storer [1995].
"Application of SinglePass Adaptive VQ to BiLevel Images",
Proceedings IEEE Data Compression Conference,
423.

See also the paper mentioned earlier that surveys this approach
as well as lossy approaches:
F. Rizzo, J. A. Storer, and B. Carpentieri [2001].
"LZBased Image Compression",
Information Sciences.
 Video Compression:

Digitized video can require over 1 billion bits per second in uncompressed form.
We have developed adaptive algorithms for block displacement estimation, which
when combined with individual frame compression and lossless postprocessing can
yield high compression while maintaining acceptable fidelity. Our approach is to
adaptively grow and shrink variable size and shaped collections of blocks, called
"superblocks". We also consider how parallel mesh architectures can be employed
for high speed real time encoding. The first figure below shows the first and last
frame of a movie clip and the second figure a "map" (each superblock has been
colored with a random color) of how it was partitioned into regions (with a course
fidelity setting); the hatched blocks are areas where due to motion in the image,
portions of superblocks are splitting off. We have also used image classification
algorithms that work offline on a subsampling of the frames to provide
information to enhance our online algorithm. In addition, we have considered the
optimization of codings of this type.
For further information and references see:

G. Motta and J. A. Storer [2000].
"An Optimal Frame Skipping Algorithm for H263+ Video Encoding",
Proceedings 11th Annual International Conference on Signal Processing
Applications and Technology,
Dallas, TX.

G. Motta and J. A. Storer [2000].
"Improving Scene Cut Quality for RealTime Video Decoding",
Proceedings IEEE Data Compression Conference,
470479.

B. Carpentieri and J. A. Storer [1996].
"A Video Coder Based on SplitMerge Displacement Estimation",
Journal of Visual Communication and Visual Representation 7:2,
137143.

B. Carpentieri and J. A. Storer [1996].
"Classification of Objects in a Video Sequence",
Proceedings SPIE Symposium on Electronic Imaging,
San Jose, CA.

B. Carpentieri and J. A. Storer [1994].
"SplitMerge Video Displacement Estimation",
Proceedings of the IEEE (June),
940947.

B. Carpentieri and J. A. Storer [1994].
"Optimal InterFrame Alignment for Video Compression",
International Journal of Foundations of Computer Science 5:2,
65177.
 SubLinear Algorithms:

Although adaptive dictionary compression via textual substitution is Pcomplete
(DeAgostino [1994], there are many interesting and practical issues that can be
addressed. We have investigated polylog algorithms for the staticdictionary
case. In addition, we have developed fast algorithms for what is perhaps the most
practical model of massively parallel computation imaginable: a linear array of
processors where each processor is connected only to its left and right neighbors,
together with a parallel I/O facility (see the figure below). In other words if
one views the PRAM model as having two types of complexity, the inter processor
communication and the inputoutput mechanism, one can view this architecture as
having virtually eliminated the complexity of interprocessor communication.
Parallel I/O is inherent in any sublinear algorithm (and makes this model much
more powerful than the systolic arrays used for our research on realtime
compression).
For further information and references see:

D. Belinskaya, S. DeAgostino, and J. A. Storer [1995].
"Near Optimal Compression with Respect to a Static Dictionary on a Practical
Massively Parallel Architecture",
Proceedings IEEE Data Compression Conference,
72181.

S. DeAgostino and J. A. Storer [1992].
"Parallel Algorithms for Optimal Compression
Using Dictionaries with the Prefix Property",
Proceedings IEEE Data Compression Conference,
15261.
 Systolic Algorithms:

We have studied algorithms for data compression for a
systolic pipe;
that is,
a linear array of processors, each connected only to its left and right
neighbors. A reallife example of a systolic pipe is an automobile assembly
line that may produce a new car every few minutes even though each car is in
the assembly line for a day. Although each station in the automobile assembly
line performs a different task, the stations are at least conceptually
identical, if we view them all as taking as input a partially built car,
performing an elementary operation (such as welding), and then outputting a
partially completed car to the next station. From the point of view of
practical VLSI implementation, a systolic pipe is very desirable  all
processors are identical, connections between adjacent processors are short,
the structure can be laid out in linear area, power and ground can be routed
without crossing wires, and the layout strategy can be independent of the
number of chips used (a larger pipe can be obtained by placing as many
processors as possible on a chip and then, using the same layout strategy,
placing as many chips as possible on a board). In addition, it is typical for
processors to be extremely simple (e.g., a few registers, a comparator, and
some finitestate logic). For a variety of textual substitution methods, and
have designed and fabricated massively parallel systolic hardware for high
bandwidth realtime adaptive dictionary compression. The first figure below
shows the layout of an individual processor and the second shows on the left
a prototype chip containing 128 processors and on the right 30 chips on a VME
board (30 instead of 32 are needed for a 4K dictionary because 256 virtual
entries correspond to the raw byte values). The design is inherently fast
because all processors compute simultaneously; a clock cycle needs to only be
long enough for a individual processor to receive 12 bits and make a 24bit
comparison (there is no global memory or communication). With year 2000
technology, 4K processors can be placed on a single chip that forms
a 4K array that can compress or decompress at over a billion bits per second
with only an 8bit data path.
For further information and references see:

J. A. Storer and J. H. Reif [1992].
"A Parallel Architecture for High Speed Data Compression",
Journal of Parallel and Distributed Computing 13 ,
222227.

D. M. Royals, T. Markas, N. Kanopoulos, J. A. Storer and J. H. Reif [1993].
"On the Design and Implementation of a Lossless Data Compression
and Decompression Chip",
IEEE Journal of SolidState Circuits (September) ,
948953.

J. A. Storer [1992].
"Massively Parallel Systolic Algorithms for RealTime DictionaryBased Text
Compression",
Image and Text Compression,
Kluwer Academic Press,
159178.

J. A. Storer, J. H. Reif, and T. Markas [1990].
"A Massively Parallel VLSI Design for Data Compression
using a Compact Dynamic Dictionary",
Proceedings IEEE VLSI Signal Processing Conference, San Diego, CA,
329338.

J. Storer and J. H. Reif [1990].
"A Parallel Architecture for High Speed Data Compression",
Third Symposium on the Frontiers of Massively Parallel Computation,
College Park, Maryland, IEEE Press,
238243.

M. Gonzalez and J. A. Storer [1985].
"Parallel Algorithms for Data Compression",
Journal of the ACM 32:2,
344373.
 Text Compression:

Textual substitution methods
parse a string into phrases and replace those phrases with
pointers to copies, called targets of the pointers, that are stored in a
dictionary.
The encoded string is a sequence of pointers (some of which may
represent single characters).
Static
methods are when the dictionary is known in advance. By contrast, with
dynamic
or
adaptive
methods, the dictionary may be constantly changing as the data is processed
(these methods are often called "LZ2" methods due to the work of Ziv and Lempel
[1978], and are somewhat different from sliding window methods, which are often
called "LZ1" methods due to the work of Lempel and Ziv [1977]). Ziv and Lempel
[1978] investigated the encoding power of a finite state oneway head machine
with an unrestricted decoder and showed for each individual sequence an
asymptotically attainable lower bound on the achievable compression ratio.
Furthermore, they achieved this lower bound with an encoder/decoder pair where
both are finite state oneway head machines (the LZ2 algorithm), proving that
imposing this condition only on the encoder does not lead to better compression.
Sheinwald, Lempel and Ziv [1995] show that the same asymptotically attainable
lower bound holds when the encoder is unrestricted and the decoder is a finite
state oneway head machine, proving that imposing this condition only on the
decoder does not lead to better compression either. Hence, offline encoding for
online decoding is not more powerful with respect to asymptotic results for
infinite strings from an information source (under some informationtheoretic
assumptions). We are interested in the power of offline decoding for online
decoding in the finite case. This models situations such as distribution of data
on CDROM where much time can be spent encoding but decoding must be fast and
simple. We have introduced the notion of online decodable optimal parsing based
on the parsing defined by the LZ2 algorithm and have shown that optimal offline
encoding is NPcomplete for both the original LZ2 technique and for some widely
used practical implementations (e.g., firstcharacter learning and nextcharacter
learning). We have also shown that approximation algorithms for these problems
cannot be realized online.
For further information and references see:

ChihYu Tang and J. A. Storer [2009].
"Network Aware Compression Based Rate Control for Printing Systems",
Proceedings 10th International Symposium on Pervasive Systems (ISPAN 2009),
Kaohsiung, Taiwan,
123128 (coauthored with ChihYu Tang).

J. H. Reif and J. A. Storer [2001].
"Optimal Encoding of NonStationary Sources",
Information Sciences 135,
87105.

S. DeAgostino and J. A. Storer [1996].
"OnLine versus OffLine Computation in Dynamic Text Compression",
Information Processing Letters 59,
169174.

J. A. Storer [1991].
"The WorstCase Performance of Adaptive Huffman Codes",
Proceedings IEEE Symposium on Information Theory,
Budapest, Hungary.

J. A. Storer [1985].
"Textual Substitution Techniques for Data Compression",
Combinatorial Algorithms on Words,
SpringerVerlag,
111129.

J. A. Storer and T. G. Szymanski [1982].
"Data Compression Via Textual Substitution",
Journal of the ACM 29:4,
928951.

J. A. Storer [1983].
"An Abstract Theory of Data Compression",
Theoretical Computer Science 24,
221237.
Other Research
For some of Dr. Storer's past research outside
the data compression group, see:

J. A. Storer and J. H. Reif [1994].
"Shortest Paths in the Plane with Polygonal Obstacles",
Journal of the ACM 41:5,
9821012.

J. H. Reif and J. A. Storer [1994].
"A SingleExponential Upper Bound
for Finding Shortest Paths in Three Dimensions",
Journal of the ACM 41:5,
10131019.

J. Lin and J. A. Storer [1991].
"ProcessorEfficient Algorithms for the Knapsack Problem",
Journal of Parallel and Distributed Computing 11,
332337.

J. H. Reif and J. A. Storer [1987].
"Minimizing Turns for Discrete Movement in the Interior of a Polygon",
IEEE
Journal of Robotics and Automation 3:3,
182193.

J. A. Storer, A. J. Nicas, and J. Becker [1985].
"Uniform Circuit Placement",
VLSI Algorithms and Architectures,
SpringerVerlag,
255273.

J. A. Storer [1984].
"On Minimal Node Cost Planar Embeddings",
Networks 14:2,
181212.

J. A. Storer [1983].
"On the Complexity of Chess",
Journal of Computer and System Sciences 27:1,
77100.

J. A. Storer [1981].
"Constructing Full Spanning Trees for Cubic Graphs",
Information Processing Letters 13:1,
811.

J. Gallant, D. Maier, and J. A. Storer [1980].
"On Finding Minimal Length Superstrings",
Journal of Computer and System Sciences 20,
5058.

D. Maier and J. A. Storer [1978].
"A Note on the Complexity of the Superstring Problem",
Proceedings Twelfth Annual CISS, 5256.
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