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A Lightweight Contextual arithmetic Coder for On-Board Remote Sensing Data Compression

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2017年第8期55卷 4825-4835页

作者： Bartrina-Rapesta, Joan Blanes, Ian Auli-Llinas, Francesc Serra-Sagrista, Joan Sanchez, Victor Marcellin, Michael W. Univ Autonoma Barcelona Dept Informat & Commun Engn E-08193 Barcelona Spain Univ Warwick Coventry CV4 7AL W Midlands England Univ Arizona Tucson AZ 85721 USA

The Consultative Committee for Space Data Systems (CCSDS) has issued several data compression standards devised to reduce the amount of data transmitted from satellites to ground stations. This paper introduces a contextual arithmetic encoder for on-board data compression. The proposed arithmetic encoder checks the causal adjacent neighbors, at most, to form the context and uses only bitwise operations to estimate the related probabilities. As a result, the encoder consumes few computational resources, making it suitable for on-board operation. Our coding approach is based on the prediction and mapping stages of CCSDS-123 lossless compression standard, an optional quantizer stage to yield lossless or near-lossless compression and our proposed arithmetic encoder. For both lossless and near-lossless compression, the achieved coding performance is superior to that of CCSDS-123, M-CALIC, and JPEG-LS. Taking into account only the entropy encoders, fixed-length codeword is slightly better than MQ and interleaved entropy coding.

关键词： arithmetic coding Consultative Committee for Space Data Systems (CCSDS)-123 lossless and near-lossless coding remote sensing data compression

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Probability Prediction Network With Checkerboard Prior for Lossless Remote Sensing Image Compression

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 17971-17982页

作者： Feng, Xuxiang Gu, Enjia Zhang, Yongshan Li, An Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

Lossless remote sensing image compression aims to reduce the storage size of images without any information loss, ensuring that the decompressed image is identical to the original one. Most existing methods focus on lossy image compression that reduce the storage cost with certain data loss. It is challenging to perform lossless compression due to the very high-resolution images, long encoding-decoding time, and low compression efficiency. In this article, we propose a lossless compression framework that compresses remote sensing images in a coarse-to-fine manner. Specifically, checkerboard segmentation is applied on each image to generate six subimages from the main diagonal and counter-diagonal of each channel to maximally preserve the detail and structural information. The subimages from the main diagonal are initially compressed by a traditional compression method, while the subimages from the counter-diagonal are compressed channel by channel using our proposed probability prediction network (P2Net) and arithmetic coding with the previously encoded subimages from both the main diagonal and counter-diagonal as prior knowledge. The proposed P2Net consists of a upsampling module, a feature enhancement module, a downsampling module, and a probability prediction module to learn the discrete probability distribution of pixels. Lossless compression is conducted with arithmetic coding on the discrete probability distribution. To the best of our knowledge, this is the first deep learning-based lossless compression framework for three-channel remote sensing images. Experiments demonstrate that our framework outperforms the state-of-the-art methods and requires about 3.4 s to compress a 1024 x 1024 x 3 image with 2.9% efficiency improvement compared to JPEG XL.

关键词： arithmetic coding checkerboard segmentation discrete probability prediction lossless image compression (L3C) remote sensing image arithmetic coding checkerboard segmentation discrete probability prediction lossless image compression (L3C) remote sensing image

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Near-Lossless Multichannel EEG Compression Based on Matrix and Tensor Decompositions

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2013年第3期17卷 708-714页

作者： Dauwels, Justin Srinivasan, K. Reddy, M. Ramasubba Cichocki, Andrzej Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Indian Inst Technol Dept Appl Mech Biomed Engn Grp Madras 600036 Tamil Nadu India RIKEN Lab Adv Brain Signal Proc BSI Wako Saitama 3510198 Japan Polish Acad Sci Syst Res Inst PL-00716 Warsaw Poland

A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. MC-EEG is represented in suitable multiway (multidimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multiway forms of MC-EEG. A compression algorithm is built based on the principle of "lossy plus residual coding," consisting of a matrix/tensor decomposition-based coder in the lossy layer followed by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error between original and reconstructed signals. The compression algorithm is applied to three different scalp EEG datasets and an intracranial EEG dataset, each with different sampling rate and resolution. The proposed algorithm achieves attractive compression ratios compared to compressing individual channels separately. For similar compression ratios, the proposed algorithm achieves nearly fivefold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm.

关键词： arithmetic coding compression electroencephalogram (EEG) multichannel EEG parallel factor decomposition (PARAFAC) singular value decomposition (SVD)

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Compression of TPC data in the ALICE experiment

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2002年第3期487卷 542-556页

作者： Nicolaucig, A Mattavelli, M Carrato, S Swiss Fed Inst Technol LSI ISL Integrated Syst Lab CH-1015 Lausanne Switzerland Univ Trieste DEEI Image Proc Lab I-34127 Trieste Italy

In this paper two algorithms for the compression of the data generated by the Time Projection Chamber (TPC) detector of the ALICE experiment at CERN are described. The first algorithm is based on a lossless source code modeling technique., i.e. the original TPC signal information can be reconstructed without errors at the decompression stage. The source model exploits the temporal correlation that is present in the TPC data to reduce the entropy of the source. The second algorithm is based on a source model which is lossy if samples of the TPC signal are considered one by one. Conversely, the source model is lossless or quasi-lossless if some physical quantities that are of main interest for the experiment are considered. These quantities are the area and the location of the center of mass of each TPC signal pulse. Obviously entropy coding is applied to the set of events defined by the two source models to reduce the bit rate to the corresponding source entropy. Using TPC simulated data according to the expected ALICE TPC performance, the lossless and the lossy compression algorithms achieve a data reduction, respectively, to 49.2% and in the range of 34.2% down to 23.7% of the original data rate. The number of operations per input symbol required to implement the compression stage for both algorithms is relatively low, so that a real-time implementation embedded in the TPC data acquisition chain using low-cost integrated electronics is a realistic option to effectively reduce the data storing cost of ALICE experiment. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： ALICE TPC compression lossless lossy arithmetic coding

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A NEW DATA STRUCTURE FOR CUMULATIVE FREQUENCY TABLES (VOL 24, PG 327, 1994)

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SOFTWARE-PRACTICE & EXPERIENCE 1994年第7期24卷 677-677页

作者： FENWICK, PM Department of Computer Science University of Auckland Private Bag 92019 Auckland New Zealand

A new method (the ‘binary indexed tree’) is presented for maintaining the cumulative frequencies which are needed to support dynamic arithmetic data compression. It is based on a decomposition of the cumulative frequencies into portions which parallel the binary representation of the index of the table element (or symbol). The operations to traverse the data structure are based on the binary coding of the index. In comparison with previous methods, the binary indexed tree is faster, using more compact data and simpler code. The access time for all operations is either constant or proportional to the logarithm of the table size. In conjunction with the compact data structure, this makes the new method particularly suitable for large symbol alphabets.

关键词： Binary indexed tree arithmetic coding Cumulative frequencies

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DESIGN OF HIGH-SPEED AND COST-EFFECTIVE SELF-TESTING CHECKERS FOR LOW-COST arithmetic CODES

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IEEE TRANSACTIONS ON COMPUTERS 1990年第3期39卷 360-374页

作者： PIESTRAK, SJ UNIV GEORGIA DEPT COMP SCI ATHENS GA 30602 USA

Methods for designing self-testing checkers (STCs) for arithmetic error-detecting codes are presented. First, general rules for the design of minimal-level STCs for any error-detecting code are given. The design is illustrated with STCs for 3N+B codes, 0>or=B>or=2. Then the recursive structure of both 3N+B codes and residue/inverse-residue codes with check base A=3 is revealed. The resulting design of STCs is very flexible and universal, in the sense that an iterative, cost-effective, or high-speed version of the checker can be designed for either code. The design approach, unlike previous approaches for arithmetic codes, gives a unified treatment to STCs for nonseparate (3N+B) and separate (residue and inverse residue) codes. The speed and the complexity of the STC for a code from either class with n bits are about the same. Both high-speed checkers (which have up to three gate levels) and cost-effective checkers are faster and require less hardware than analogous checkers proposed for 3N codes and for residue codes with A=3.

关键词： design approach Automatic testing checker SINGLE STRIP CONTAINERS arithmetic coding Codes residue codes error detection codes

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Transmission error detection and concealment in JPEG images

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SIGNAL PROCESSING-IMAGE COMMUNICATION 1998年第1期13卷 45-64页

作者： Abdat, M Al Kachouh, Z Bellanger, MG Conservatoire Natl Arts & Metiers Elect Lab F-75141 Paris 03 France

The robustness to transmission errors of JPEG coded images is investigated and techniques are proposed to reduce their effects. After an analysis of the JPEG transfer format, three main classes of transfer format constituents are distinguished, and a JPEG compatible approach is proposed to stop error propagation in the entropy coded data, with encoder and decoder reset after fixed coding interval lengths. With the use of restart intervals, the propagation of errors is stopped but no correction has taken place. Therefore, a concealment procedure is defined acid investigated. It consists of two steps. First, error detection must be performed and three different techniques are assessed and compared. Then, block error concealment is achieved. Simulation results are reported. Depending on the entropy coding and on the neighborhood templates used for detection and concealment, prediction based or interpolation based, improvements between 3 and 10dB in peak-to-peak signal-to-noise ratios (PSNR) are provided by the robust decoder with respect to conventional JPEG decoders, for bit error rates around 10(-4). (C) 1998 Elsevier Science B.V. All rights reserved.

关键词： image compression JPEG Huffman coding arithmetic coding transmission errors error detection error concealment

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Efficient universal lossless data compression algorithms based on a greedy sequential grammar transform - Part one: Without context models

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IEEE TRANSACTIONS ON INFORMATION THEORY 2000年第3期46卷 755-777页

作者： Yang, EH Kieffer, JC Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

A grammar transform is a transformation that converts any data sequence to be compressed into a grammar from which the original data sequence can be fully reconstructed. In a grammar-based code, a data sequence is first converted into a grammar by a grammar transform and then losslessly encoded. In this paper, a greedy grammar transform is first presented;this grammar transform constructs sequentially a sequence of irreducible grammars from which the original data sequence can be recovered incrementally. Based on this grammar transform, three universal lossless data compression algorithms, a sequential algorithm, an improved sequential algorithm, and a hierarchical algorithm, are then developed. These algorithms combine the power of arithmetic coding with that of string matching. It is shown that these algorithms are all universal in the sense that they can achieve asymptotically the entropy rate of any stationary, ergodic source. Moreover, it is proved that their worst case redundancies among all individual sequences of length n are upper-bounded by c log log n / log n, where c is a constant. Simulation results show that the proposed algorithms outperform the Unix Compress and Gzip algorithms, which are based on LZ78 and LZ77, respectively.

关键词： arithmetic coding entropy grammar-based source codes redundancy string matching universal sequential and hierarchical data compression

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DYNAMIC-HISTORY PREDICTIVE COMPRESSION

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INFORMATION SYSTEMS 1988年第1期13卷 129-140页

作者： WILLIAMS, RN Department of Computer Science The University of Adelaide G.P.O. Box 498 Adelaide 5001 South AustraliaAustralia

The problem of compressing a discrete finite ergodic source sequence is approached from a predictive point of view. An algorithm is described that dynamically builds a conditional probability tree by adding leaves in proportion to their potential for reducing the entropy of the tree. This results in a tree that is containable but which can still adapt to unusual source characteristics. An implementation of the algorithm produced compression competitive with other contemporary data compression algorithms.

关键词： Data compression prediction variable order Markov model arithmetic coding adaptive coding

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Efficient statistical modeling for the compression of tree structured intermediate code

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COMPUTER JOURNAL 2003年第5期46卷 476-486页

作者： Rai, S Shankar, P Indian Inst Sci Dept Comp Sci & Automat Bangalore 560012 Karnataka India

We propose a scheme for the compression of tree structured intermediate code consisting of a sequence of trees specified by a regular tree grammar. The scheme is based on arithmetic coding, and the model that works in conjunction with the coder is automatically generated from the syntactical specification of the tree language. Experiments on data sets consisting of intermediate code trees yield compression ratios ranging from 2.5 to 8, for file sizes ranging from 167 bytes to 1 megabyte.

关键词： trees compression ratio Scheme arithmetic coding compression statistical models (nuclear) Megabyte Dataset File Size AUTOMATIC ENCODER Codes Byte

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