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compression of Data Streams Down to Their Information Content

IEEE TRANSACTIONS ON INFORMATION THEORY 2019年第7期65卷 4471-4485页

作者： Barmpalias, George Lewis-Pye, Andrew Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100190 Peoples R China London Sch Econ Dept Math Columbia House London WC2A 2AE England

According to the Kolmogorov complexity, every finite binary string is compressible to a shortest code its information content from which it is effectively recoverable. We investigate the extent to which this holds for the infinite binary sequences (streams). We devise a new coding method that uniformly codes every stream X into an algorithmically random stream Y, in such a way that the first n bits of X are recoverable from the first 1(X vertical bar n) bits of Y, where I is any partial computable information content measure that is defined on all prefixes of X, and where X vertical bar n is the initial segment of X of length n. As a consequence, if g is any computable upper bound on the initial segment prefix-free complexity of X, then X is computable from an algorithmically random Y with oracle-use at most g. Alternatively (making no use of such a computable bound g), one can achieve an the oracle-use hounded above by K(X X vertical bar n)+ log n. This provides a strong analogue of Shannon's source coding theorem for the algorithmic information theory.

关键词： Source coding algorithmic information theory compression algorithms Kolmogorov complexity prefix-free codes

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compression OF BLACK-WHITE IMAGES WITH ARITHMETIC CODING

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IEEE TRANSACTIONS ON COMMUNICATIONS 1981年第6期29卷 858-867页

作者： LANGDON, GG RISSANEN, J IBM Corporation San Jose CA USA

A new approach for black and white image compression is described, with which the eight CCITT test documents can be compressed in a lossless manner 20-30 percent better than with the best existing compression algorithms. The coding and the modeling aspects are treated separately. The key to these improvements is an efficient binary arithmetic code. The code is relatively simple to implement because it avoids the multiplication operation inherent in some earlier arithmetic codes. Arithmetic coding permits the compression of binary sequences where the statistics change on a bit-to-bit basis. Model statistics are studied from stationary, stationary adaptive, and nonstationary adaptive assumptions.

关键词： Image coding Arithmetic Phase detection Data communication Network address translation Biographies compression algorithms Statistics Jacobian matrices Immune system

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compression and genetic sequence analysis

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BIOCHIMIE 1996年第5期78卷 315-322页

作者： Rivals, E Dauchet, M Delehaye, JP Delgrange, O UNIV MONS SERV INFORMATB-7000 MONSBELGIUM

A novel approach to genetic sequence analysis is presented. This approach, based on compression of algorithms, has been launched simultaneously by Grumbach and Tahi, Milosavljevic and Rivals. To reduce the description of an object, a compression algorithm replaces some regularities in the description by special codes. Thus a compression algorithm can be applied to a sequence in order to study the presence of those regularities all over the sequence. This paper explains this ability, gives examples of compression algorithms already developed and mentions their applications. Finally, the theoretical foundations of the approach are presented in an overview of the algorithmic theory of information.

关键词： sequence classification regularity detection compression algorithms Kolmogorov complexity

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APPLYING WAVELET TRANSFORMS WITH ARITHMETIC CODING TO RADIOLOGICAL IMAGE compression

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IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE 1995年第5期14卷 587-593页

作者： SAIPETCH, P HO, BKT PANWAR, R MA, M WEI, J Department of Radiological Sciences University of California Los Angeles USA

Radiological archives need the images to be compressed at a moderate compression ratio between 10:1 to 20:1 while retaining good diagnostic quality. We have developed a compression algorithm based on discrete wavelet transforms (DWTs) and arithmetic coding (AC) that satisfies those requirements. This new method is superior to the previously developed full frame discrete cosine transform (FFDCT) method, as well as the industrial standard developed by the joint photographic expert group (JPEG). Since DWT is localized in both spatial and scale domains, the error due to quantization of coefficients does not propagate throughout the reconstructed picture as in FFDCT. Because it is a global transformation, it does not suffer the limitation of block transform methods such as JPEG. The severity of the error as measured by the normalized mean square error (NMSE) and maximum difference technique increases very slowly with compression ratio compared to the FFDCT. Normalized nearest neighbor difference (NNND), which is a measure of blockiness, stays approximately constant, while JPEG NNND increases rapidly with compression ratio. Furthermore, DWT has an efficient finite response filter FlR implementation that can be put in parallel hardware. DWT also offers total flexibility in the image format; the size of the image does not have to be a power of two as in the case of FFDCT.< >

关键词： Wavelet transforms Arithmetic Discrete wavelet transforms Image coding Transform coding compression algorithms Discrete cosine transforms Standards development Quantization Image reconstruction

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On the performance of fractal compression with clustering

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IEEE TRANSACTIONS ON IMAGE PROCESSING 1996年第3期5卷 522-526页

作者： Wein, CJ Blake, IF Department of Electrical and Computer Engineering University of Waterloo Waterloo ONT Canada

The paper investigates a technique to reduce the computational complexity of fractal image compression on gray-scale images. The technique uses a clustering process on image domain blocks with the clusters formed with the use of k-d trees and the fast pairwise nearest neighbor algorithm of Equitz. Results indicate the method is effective for smaller domain block sizes and generally shows improvement in terms of picture peak signal-to-noise ratio (SNR) over the quadrant variance classification method.

关键词： Fractals Image coding Nearest neighbor searches Clustering algorithms Image quality compression algorithms Gray-scale PSNR Transform coding Brightness

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Sequential neural text compression

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1996年第1期7卷 142-146页

作者： Schmidhuber, J Heil, S TECH UNIV MUNICH INST INFORMATD-80290 MUNICHGERMANY CALIF STATE UNIV FRESNO DEPT COMP SCIFRESNOCA 93740

The purpose of this paper is to show that neural networks may be promising tools for data compression without loss of information, We combine predictive neural nets and statistical coding techniques to compress tent files. We apply our methods to certain short newspaper articles and obtain compression ratios exceeding those of the widely used Lempel-Ziv algorithms (which build the basis of the UNIX functions ''compress'' and ''gzip''). The main disadvantage of our methods is that they are about three orders of magnitude slower than standard methods.

关键词： Decoding Neural networks Table lookup Hydrogen Huffman coding Arithmetic History Probability distribution Character generation compression algorithms

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Dynamic Alternation of Huffman Codebooks for Sensor Data compression

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IEEE EMBEDDED SYSTEMS LETTERS 2017年第3期9卷 81-84页

作者： Yunge, Daniel Park, Sangyoung Kindt, Philipp Chakraborty, Samarjit Tech Univ Munich Chair Real Time Comp Syst D-80290 Munich Germany

Signal compression is crucial for reducing the amount of communication, and hence power consumption of wireless sensors. Lossless compression techniques, such as Huffman coding, are often used in healthcare applications since they do not compromise the integrity of vital signals. Techniques that adapt to changing signal patterns have been proposed. However, most of them involve significant computation overhead or are too simple to maintain high compression rates under changing signal patterns. In this letter, we propose a technique that makes use of multiple codebooks, which are generated offline based on the signal context. In the applications we study, we observe that the symbols that compose a big variety of signals follow Laplacian distributions in which the spread changes over time. This can be effectively utilized to generate a set of codebooks. Then, appropriate codebooks are selected online depending on the currently measured spread, which ensures high compression efficiency and the adaptability to changing signal patterns. Our experiments on real-world medical datasets show that our approach is computationally very efficient, and exhibits competitive compression rates. Our proposed technique outperforms a state-of-the-art compression algorithm, FAS-LEC, in terms of average data reduction by 4.3%, while consuming a similar amount of energy. Compared to the adaptive Huffman method, which achieves near-optimal compression rates, our results indicate energy savings of 19% due to the reduced computational complexity, while the compression rate is improved by 0.6%.

关键词： Bluetooth body sensor networks codecs compression algorithms Huffman coding maximum likelihood estimation meteorology telemedicine wearable computers

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VLSI IMPLEMENTATION OF A DPCM compression ALGORITHM FOR DIGITAL TV

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IEEE TRANSACTIONS ON COMMUNICATIONS 1987年第8期35卷 854-856页

作者： BRAINARD, RC OTHMER, JH AT&T Bell Lab. Holmdel NJ USA

Future video services in the loop plant may be based on digital transmission through optical fibers. A small and inexpensive codec is required for a variety of services based on digital television. We have demonstrated a compression algorithm for transmission of NTSC color television over a DS3 channel (44.736 Mbits/s). This predictive coding algorithm has been implemented using circuits built with conventional TTL logic. The resulting picture is visually unimpaired, but may not have network quality. The major portion of the compression or reconstruction circuit has also been implemented in one CMOS VLSI chip. compression is accomplished using this chip with two small ROM chips. Reconstruction is done with the same VLSI chip and one ROM chip.

关键词： Very large scale integration compression algorithms Digital TV Circuits Read only memory Video compression Optical fibers Codecs Predictive coding Prediction algorithms

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DATA-compression AND PERCEIVED QUALITY

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IEEE SIGNAL PROCESSING MAGAZINE 1995年第5期12卷 58-59页

作者： SMITH, JL Simulation and Signature Department at the Guided Weapons Evaluation Facility Vitro Technical Services FL USA

Discusses image compression and the block compression algorithm (BCA). The power spectrum, Bode diagram, or modulation transfer function (MTF) for the transmitted, reconstructed signal are the usual measures of system resolution. Any one of these resolution measures is often nearly identical for the ordinary signal and that for which the BCA is used. Yet, observers of that same transmitted signals perceive a much more profound difference than indicated by the resolution measure. At high compression, they say it is choppy and disturbing for the dynamic features. The author discusses how one can describe a measure of the effect of the BCA on the observation.< >

关键词： Data compression Human factors Signal resolution Computer vision Power measurement Noise level Gaussian noise Image coding Microwave communication compression algorithms

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Optimal Rule Caching and Lossy compression for Longest Prefix Matching

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IEEE-ACM TRANSACTIONS ON NETWORKING 2017年第2期25卷 850-864页

作者： Rottenstreich, Ori Tapolcai, Janos Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Budapest Univ Technol & Econ MTA BME Future Internet Res Grp H-1117 Budapest Hungary

Packet classification is a building block in many network services, such as routing, monitoring, and policy enforcement. In commodity switches, classification is often performed by memory components of various rule matching patterns (longest prefix match, ternary matches, exact match, and so on). The memory components are fast but expensive and power-hungry with power consumption proportional to their size. In this paper, we study the applicability of rule caching and lossy compression to create packet classifiers requiring much less memory than the theoretical size limits of the semantically-equivalent representations, enabling significant reduction in their cost and power consumption. This paper focuses on the longest prefix matching. Our objective is to find a limited-size longest prefix match classifier that can correctly classify a high portion of the traffic, so that it can be implemented in commodity switches with classification modules of restricted size. While for the lossy compression scheme a small amount of traffic might observe classification errors, a special indication is returned for traffic that cannot be classified in the rule caching scheme. We develop optimal dynamic-programming algorithms for both problems and describe how to treat the small amount of traffic that cannot be classified. We generalize our solutions for a wide range of classifiers with different similarity metrics. We evaluate their performance on real classifiers and traffic traces and show that in some cases we can reduce a classifier size by orders of magnitude while still classifying almost all traffic correctly.

关键词： compression algorithms classification algorithms cache memory software defined networking

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