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ON THE DESIGN AND IMPLEMENTATION OF A LOSSLESS DATA-compression AND DEcompression CHIP

IEEE JOURNAL OF SOLID-STATE CIRCUITS 1993年第9期28卷 948-953页

作者： ROYALS, DM MARKAS, T KANOPOULOS, N REIF, JH STORER, JA DUKE UNIV DEPT COMP SCIDURHAMNC 27706 BRANDEIS UNIV DEPT COMP SCIWALTHAMMA 02254

A lossless data compression and decompression (LDCD) algorithm based on the notion of textual substitution has been implemented in silicon using a linear systolic array architecture. This algorithm employs a model where the encoder and decoder each have a finite amount of memory which is referred to as the dictionary. compression is achieved by finding matches between the dictionary and the input data stream whereby a substitution is made in the data stream by an index referencing the corresponding dictionary entry. The LDCD system is built using 30 application-specific integrated circuits (ASIC's) each containing 126 identical processing elements (PE's) which perform both the encoding and decoding function at clock rates up to 20 MHz.

关键词： Data compression Dictionaries Decoding compression algorithms Vocabulary Computer science Algorithm design and analysis Silicon Systolic arrays Application specific integrated circuits

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STATISTICAL DISTRIBUTIONS OF DCT COEFFICIENTS AND THEIR APPLICATION TO AN INTERFRAME compression ALGORITHM FOR 3-D MEDICAL IMAGES

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IEEE TRANSACTIONS ON MEDICAL IMAGING 1993年第3期12卷 478-485页

作者： LEE, H KIM, YM ROWBERG, AH RISKIN, EA UNIV WASHINGTON DEPT RADIOLSEATTLEWA 98195

We present displacement estimated interframe (DEI) coding, a new coding scheme for 3-D medical image data sets such as x-ray CT or MR images. To take advantage of the correlation between contiguous slices, a displacement-compensated difference image based on the previous image is encoded. We have determined the best fitting distribution functions for the discrete cosine transform (DCT) coefficients obtained from displacement compensated difference images, and used the results in allocating bits and optimizing quantizers for the coefficients. The DEI scheme is compared with a 2-D block DCT as well as a full-frame DCT using the bit allocation technique by Lo and Huang [1]. For x-ray CT head images, our bit allocation and quantizer design using an appropriate distribution model resulted in a 13 dB improvement in the SNR compared to the full-frame DCT using the bit allocation technique. For an image set with 5 mm slice thickness, the DEI method compared to the 2-D block DCT gave about 5 percent improvement in the compression ratio on average and less blockiness at the same distortion. The performance gain increases to about 10 percent when the slice thickness decreases to 3 mm.

关键词： compression ratio difference image compression algorithms interframe discrete cosine transforms Three-Dimensional Images slicing labeling scheme quantizer statistical distribution

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TREE-STRUCTURED VECTOR QUANTIZATION OF CT CHEST SCANS - IMAGE QUALITY AND DIAGNOSTIC-ACCURACY

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IEEE TRANSACTIONS ON MEDICAL IMAGING 1993年第4期12卷 727-739页

作者： COSMAN, PC TSENG, C GRAY, RM OLSHEN, RA MOSES, LE DAVIDSON, HC BERGIN, CJ RISKIN, EA STANFORD UNIV SCH MEDDIV BIOSTATSTANFORDCA 94305 STANFORD UNIV SCH MEDDEPT DIAGNOST RADIOL & NUCL MEDSTANFORDCA 94305 STANFORD UNIV SCH MEDDIV DIAGNOST RADIOLSTANFORDCA UNIV WASHINGTON DEPT ELECT ENGNSEATTLEWA 98195

The quality of lossy compressed images is often characterized by signal-to-noise ratios, informal tests of subjective quality, or receiver operating characteristic (ROC) curves that include subjective appraisals of the value of an image for a particular application. We believe that for medical applications, lossy compressed images should be judged by a more natural and fundamental aspect of relative image quality: their use in making accurate diagnoses. We apply a lossy compression algorithm to medical images, and quantify the quality of the images by the diagnostic performance of radiologists, as well as by traditional signal-to-noise ratios and subjective ratings. Our study is unlike previous studies of the effects of lossy compression in that we consider non-binary detection tasks, simulate actual diagnostic practice instead of using paired tests or confidence rankings, use statistical methods that are more appropriate for non-binary clinical data than are the popular ROC curves, and use low-complexity predictive tree-structured vector quantization for compression rather than DCT-based transform codes combined with entropy coding. Our diagnostic tasks are the identification of nodules (tumors) in the lungs and lymphadenopathy in the mediastinum from computerized tomography (CT) chest scans. Radiologists read both uncompressed and lossy compressed versions of images. For the image modality, compression algorithm, and diagnostic tasks we consider, the original 12 bit per pixel (bpp) CT image can be compressed to between 1 bpp and 2 bpp with no significant changes in diagnostic accuracy. The techniques presented in this paper for evaluating image quality do not depend on the specific compression algorithm and are useful new methods for evaluating the benefits of any lossy image processing technique.

关键词： Vector quantization Computed tomography Image coding compression algorithms Biomedical imaging Medical diagnostic imaging Performance loss Signal to noise ratio Predictive models Medical simulation

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SOURCE-CODING AND TRANSMISSION OF HDTV IMAGES COMPRESSED WITH THE WAVELET TRANSFORM

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 1993年第1期11卷 46-58页

作者： ARGENTI, F BENELLI, G MECOCCI, A UNIV PAVIA DEPT ELECTR ENGN I-27100 PAVIA ITALY

Digital high-definition TV (HDTV) signals are generally compressed to reduce transmission bandwidth requirements. This paper presents a compression algorithm for the bit rate reduction of an HDTV image using the wavelet transform. The major problems related to the transmission of a compressed HDTV signal are analyzed. Transmission is examined both on a noisy channel and an asynchronous transfer mode (ATM) network. The effects of channel noise on the reconstructed image are determined, and a solution to mitigate the degradation of the image quality is presented. Lastly, a model for the output bit rate of the HDTV coder is derived and used to simulate the transmission of an ATM multiplexer so that the network's main performance parameters can be determined.

关键词： wavelets source coding noisy channel channel noise wavelet transforms characteristic parameter Image quality COMPRESSED bit rate reduction compression algorithms

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EMBEDDED IMAGE-CODING USING ZEROTREES OF WAVELET COEFFICIENTS

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1993年第12期41卷 3445-3462页

作者： SHAPIRO, JM David Samoff Research Center Inc. Princeton NJ USA

The embedded zerotree wavelet algorithm (EZW) is a simple, yet remarkably effective, image compression algorithm, having the property that the bits in the bit stream are generated in order of importance, yielding a fully embedded Code. The embedded code represents a sequence of binary decisions that distinguish an image from the ''null'' image. Using an embedded coding algorithm, an encoder can terminate the encoding at any point thereby allowing a target rate or target distortion metric to be met exactly. Also, given a bit stream, the decoder can cease decoding at any point in the bit stream and still produce exactly the same image that would have been encoded at the bit rate corresponding to the truncated bit stream. In addition to producing a fully embedded bit stream, EZW consistently produces compression results that are competitive with virtually all known compression algorithms on standard test images. Yet this performance is achieved with a technique that requires absolutely no training, no pre-stored tables or Codebooks, and requires no prior knowledge of the image source. The EZW algorithm is based on four key concepts: 1) a discrete wavelet transform or hierarchical subband decomposition, 2) prediction of the absence of significant information across scales by exploiting the self-similarity inherent in images, 3) entropy-coded successive-approximation quantization, and 4) universal lossless data compression which is achieved via adaptive arithmetic coding.

关键词： Image coding Wavelet coefficients Streaming media Decoding Discrete wavelet transforms Rate distortion theory Bit rate compression algorithms Testing Quantization

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PUTTING DATA ON A DIET

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IEEE SPECTRUM 1993年第8期30卷 36-39页

作者： WEISS, J SCHREMP, D Telco Systems United States

Within well-understood theoretical limits, data compression trades some computational complexity for reduced bandwidth and storage requirements. Lossless data-compression algorithms preserve all the information in the data so that it can be reconstructed without error. Lossy compression techniques do not offer perfect reproduction, but can compress data into as little as 1% of its uncoded length. The simplest method for lossless compression is run-length encoding. A more sophisticated compression algorithm is the Huffman coder, which tries to assign the most economical possible variable-length bit string to each symbol in an alphabet. Arithmetic codes were developed to overcome the fractional-bit deficiencies of Huffman codes. As good as they are, the Huffman and arithmetic models are less than efficient at modeling text. Far more effective are 2 simple string-matching techniques, known as LZ-77 and LZ-78. An area to which compression has a very large contribution to make is the wide-area network packet-based environment.

关键词： Access protocols Image coding Costs Local area networks Bandwidth compression algorithms Internet Computer industry Computer networks Disk drives

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The use of high performance computing in JPEG image compression

The use of high performance computing in JPEG image compress...

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Asilomar Conference on Signals, Systems & Computers

作者： G.W. Cook E.J. Delp Computer Vision and Image Processing Laboratory School of Electrical Engineering Purdue University West Lafayette IN USA

The problem inherent to any digital image (or digital video) system is the large amount of bandwidth required for transmission or storage. This has driven the research area of image compression to develop more complex algorithms that compress images to lower data rates with better fidelity. One approach that can be used to increase the execution speed of these complex algorithms is to implement the algorithms on a parallel supercomputer. The authors address the parallel implementation of the JPEG still image compression standard on the MasPar MP-1, a massively parallel SIMD computer. They demonstrate that the greatest difficulty lies not with the compression algorithm per se, but with the speed bottleneck that arises in the output of the compressed image. They develop a parallel output algorithm which addresses this problem and present results which show real-time performance on 1024/spl times/1024 images.< >

关键词： High performance computing Transform coding Image coding Image storage Digital images Video compression Bandwidth Supercomputers Concurrent computing compression algorithms

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A compression Algorithm For The Hierarchical Subband Representations Of the image sequences

A Compression Algorithm For The Hierarchical Subband Represe...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： L. Margarit R.C. Zaciu Applied Electronics Department University of Politehnica Bucharest Romania

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A cycle compression algorithm for CRMA high-speed networks

A cycle compression algorithm for CRMA high-speed networks

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IEEE International Conference on Communications (ICC)

作者： N.-F. Huang C.-S. Chang G.-K. Ma Department of Computer Science National Tsing Hua University Hsinchu Taiwan Advanced Technology Center Computer and Communications Research Laboratories Hsinchu Taiwan

Cyclic-reservation multiple-access (CRMA) is an access scheme for high-speed local and metropolitan area networks based on a dual-bus configuration. In CRMA, the headend generates the reserve commands periodically. It is proved that the problem of finding a shortest cycle for a CRMA reservation (called a cycle compression problem) is NP-complete by showing that it is equivalent to the interval coloring problem. An O(n/sup 2/) approximation algorithm is proposed to solve this problem, where n is the number of stations in the network.< >

关键词： compression algorithms High-speed networks Computer science Metropolitan area networks Mice Delay Approximation algorithms Local area networks

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SPECTRUM analysis of multispectral imagery in conjunction with wavelet/KLT data compression

SPECTRUM analysis of multispectral imagery in conjunction wi...

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Asilomar Conference on Signals, Systems & Computers

作者： J.N. Bradley C.M. Brislawn Los Alamos National Laboratory Los Alamos NM USA

The data analysis program, SPECTRUM, is used for fusion, visualization, and classification of multispectral imagery. To facilitate data transmission and storage, a compression algorithm is proposed based on spatial wavelet transform coding and KLT decomposition of interchannel spectral vectors, followed by adaptive optimal multiband scalar quantization. The performance of SPECTRUM clustering and visualization is evaluated on compressed multispectral data. 8-bit visualizations of 56-bit data show little visible distortion, at 50:1 compression and graceful degradation at higher compression ratios.< >

关键词： Image analysis Multispectral imaging Data visualization Data analysis Data communication compression algorithms Transform coding Karhunen-Loeve transforms Quantization Image coding

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