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Nonblock coding of Binary Sources by Probabilistic Enumeration

IEEE TRANSACTIONS ON INFORMATION THEORY 2011年第9期57卷 6170-6179页

作者： Teuhola, Jukka Univ Turku Dept Informat Technol SF-20500 Turku Finland

A simple and efficient nonblock coding scheme for binary sources is suggested. It uses a probability model to map any source string to a unique integer, and thereby defines an enumeration of all possible strings. Contrary to normal enumerative coding, the new method is noncombinatorial and operates sequentially, incrementing the code value symbol by symbol, by simple arithmetic. It is akin to arithmetic coding but does not use intervals. For memoryless sources, the redundancy per symbol is shown to be asymptotically less than p(3) bits, where p is the smaller of symbol probabilities. Especially, for integer-valued 1/p, the code is asymptotically optimal. These results are confirmed by both analysis and experiments with arbitrary-precision arithmetic. In a practical implementation, the source is partitioned into substrings enabling restricted-precision arithmetic. Thanks to subtle implicit coding, the additional redundancy is marginal. The source model can be also context-based, and even adaptivity can be incorporated. A peculiarity of the method is that decoding is done backwards (LIFO). Hence, for higher-order models, the string suffix, not prefix, is used as the context domain. The speed of the practical version is close to that of binary arithmetic coders.

关键词： arithmetic coding binary source enumerative coding nonblock coding source coding

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Resolution Scalable Image coding With Reversible Cellular Automata

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2011年第5期20卷 1461-1468页

作者： Cappellari, Lorenzo Milani, Simone Cruz-Reyes, Carlos Calvagno, Giancarlo Univ Padua Dept Informat Engn I-35131 Padua Italy Univ Rovira & Virgili Res Grp Math Linguist Tarragona 43002 Spain

In a resolution scalable image coding algorithm, a multiresolution representation of the data is often obtained using a linear filter bank. Reversible cellular automata have been recently proposed as simpler, nonlinear filter banks that produce a similar representation. The original image is decomposed into four subbands, such that one of them retains most of the features of the original image at a reduced scale. In this paper, we discuss the utilization of reversible cellular automata and arithmetic coding for scalable compression of binary and grayscale images. In the binary case, the proposed algorithm that uses simple local rules compares well with the JBIG compression standard, in particular for images where the foreground is made of a simple connected region. For complex images, more efficient local rules based upon the lifting principle have been designed. They provide compression performances very close to or even better than JBIG, depending upon the image characteristics. In the grayscale case, and in particular for smooth images such as depth maps, the proposed algorithm outperforms both the JBIG and the JPEG2000 standards under most coding conditions.

关键词： arithmetic coding cellular automata (CA) scalable image coding

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VLSI implementation of SISO arithmetic decoders for joint source channel coding 08

VLSI implementation of SISO arithmetic decoders for joint so...

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2008 Design, Automation and Test in Europe (DATE 2008), vol.3

作者： Simone Zezza Guido Masera Politecnico di Torino Italy

ISBN: (纸本)9783981080131

In this paper we propose an efficient VLSI implementation of a Soft Input Soft Output (SISO) arithmetic code (AC) decoder for joint source channel coding. The addressed application shows a very high level of processing complexity, but, to the best of our knowledge, no papers have been published in the literature on the hardware implementation of the considered joint source channel scheme. First we introduce a simplified algorithm for the SISO AC, which is 1.3 times faster than the standard one. Then an efficient SISO AC architecture is proposed and synthesis results on a 0.13μm standard cells technology are reported for two different sets of parameters (M=128, M=256). The proposed core runs at 338.9MHz and can decode up to 124.987kbit/s.

关键词： Combined source-channel coding Channel coding decoders Very large scale integration Joints ALTERNATING CURRENT arithmetic arithmetic coding

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Universal coding Algorithm for a Family of context Markov sources

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PROBLEMS OF INFORMATION TRANSMISSION 2014年第3期50卷 285-291页

作者： Shtarkov, Yu M. Russian Acad Sci Kharkevich Inst Informat Transmiss Problems Moscow Russia

Often, for a source to be encoded it is only known (or assumed) that its model belongs to some known family;parameters of models are unknown. The number of context Markov models in a family can be enormous, and methods for finding the best of them to describe a current block (message fragment of length n) have not been discussed previously. We propose a way to solve this problem and describe a coding algorithm.

关键词： coding theory MARKOV processes ALGORITHMS PROBABILITY theory arithmetic coding

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Delay and Redundancy in Lossless Source coding

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IEEE TRANSACTIONS ON INFORMATION THEORY 2014年第9期60卷 5470-5485页

作者： Shayevitz, Ofer Meron, Eado Feder, Meir Zamir, Ram Tel Aviv Univ Dept Elect Engn Syst IL-69978 Tel Aviv Israel

The penalty incurred by imposing a finite delay constraint in lossless source coding of a memoryless source is investigated. It is well known that for the so-called block-to-variable and variable-to-variable codes, the redundancy decays at best polynomially with the delay, where in this case the delay is identified with the source block length or maximal source phrase length, respectively. In stark contrast, it is shown that for sequential codes (e. g., a delay-limited arithmetic code) the redundancy can be made to decay exponentially with the delay constraint. The corresponding redundancy-delay exponent is shown to be at least as good as the Renyi entropy of order 2 of the source, but (for almost all sources) not better than a quantity depending on the minimal source symbol probability and the alphabet size.

关键词： Lossless source coding coding delay redundancy arithmetic coding

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Lossless coding of Point Cloud Geometry Using a Deep Generative Model

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第12期31卷 4617-4629页

作者： Nguyen, Dat Thanh Quach, Maurice Valenzise, Giuseppe Duhamel, Pierre Univ Paris Saclay Lab Signaux & Syst UMR 8506 CNRS Cent Supelec F-91190 Gif Sur Yvette France

This paper proposes a lossless point cloud (PC) geometry compression method that uses neural networks to estimate the probability distribution of voxel occupancy. First, to take into account the PC sparsity, our method adaptively partitions a point cloud into multiple voxel block sizes. This partitioning is signalled via an octree. Second, we employ a deep auto-regressive generative model to estimate the occupancy probability of each voxel given the previously encoded ones. We then employ the estimated probabilities to code efficiently a block using a context-based arithmetic coder. Our context has variable size and can expand beyond the current block to learn more accurate probabilities. We also consider using data augmentation techniques to increase the generalization capability of the learned probability models, in particular in the presence of noise and lower-density point clouds. Experimental evaluation, performed on a variety of point clouds from four different datasets and with diverse characteristics, demonstrates that our method reduces significantly (by up to 37%) the rate for lossless coding compared to the state-of-the-art MPEG codec.

关键词： Three-dimensional displays Octrees Geometry Image coding Encoding Context modeling Convolutional codes Point cloud coding deep learning G-PCC context model arithmetic coding

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Embedded block coding in JPEG 2000

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2002年第1期17卷 49-72页

作者： Taubman, D Ordentlich, E Weinberger, M Seroussi, G Univ New S Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia GlobeSpan Santa Clara CA USA Hewlett Packard Labs Palo Alto CA USA

This paper describes the embedded block coding algorithm at the heart of the JPEG 2000 image compression standard, The paper discusses key considerations which led to the development and adoption of this algorithm, and also investigates performance and complexity issues, The JPEG 2000 coding system achieves excellent compression performance, somewhat higher (and, in some cases, substantially higher) than that of SPIHT with arithmetic coding, a popular benchmark for comparison The algorithm utilizes the same low complexity binary arithmetic coding engine as JBIG2. Together with careful design of the bit-plane coding primitives, this enables comparable execution speed to that observed with the simpler variant of SPIHT without arithmetic coding. The coder offers additional advantages including memory locality, spatial random access and ease of geometric manipulation. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： JPEG 2000 image compression scalable compression embedded coding bit-plane coding arithmetic coding wavelet transform

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Optimal coding of Generalized-Gaussian-Distributed Frequency Spectra for Low-Delay Audio Coder With Powered All-Pole Spectrum Estimation

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2015年第8期23卷 1309-1321页

作者： Sugiura, Ryosuke Kamamoto, Yutaka Harada, Noboru Kameoka, Hirokazu Moriya, Takehiro NTT Corp Yokohama Kanagawa 2430198 Japan

We present an optimal coding scheme that parameterizes the maximum-likelihood estimate of variance for frequency spectra belonging to the generalized Gaussian distribution, the distribution covering the Laplacian and the Gaussian. By slightly modifying the all-pole model of the conventional linear prediction (LP), we can estimate the variance with the same method as in LP, which has low computational costs. Experimental results show that incorporating the coding scheme in a state-of-the-art wide-band audio coder enhances its objective and subjective quality in a low-bit-rate and low-delay situation by increasing the compression efficiency. Thus, this coding scheme will be useful in applications like mobile communications, which requires highly efficient compression.

关键词： arithmetic coding audio compression generalized Gaussian distribution linear prediction low delay transform coded excitation

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Lossy Cutset coding of Bilevel Images Based on Markov Random Fields

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2014年第4期23卷 1652-1665页

作者： Reyes, Matthew G. Neuhoff, David L. Pappas, Thrasyvoulos N. Univ Michigan Dept Elect & Engn Comp Sci Ann Arbor MI 48109 USA Northwestern Univ Dept Elect & Engn Comp Sci Evanston IL 60208 USA

An effective, low complexity method for lossy compression of scenic bilevel images, called lossy cutset coding, is proposed based on a Markov random field model. It operates by losslessly encoding pixels in a square grid of lines, which is a cutset with respect to a Markov random field model, and preserves key structural information, such as borders between black and white regions. Relying on the Markov random field model, the decoder takes a MAP approach to reconstructing the interior of each grid block from the pixels on its boundary, thereby creating a piecewise smooth image that is consistent with the encoded grid pixels. The MAP rule, which reduces to finding the block interiors with fewest black-white transitions, is directly implementable for the most commonly occurring block boundaries, thereby avoiding the need for brute force or iterative solutions. Experimental results demonstrate that the new method is computationally simple, outperforms the current lossy compression technique most suited to scenic bilevel images, and provides substantially lower rates than lossless techniques, e.g., JBIG, with little loss in perceived image quality.

关键词： Lossy bilevel image coding lossy bilevel image compression arithmetic coding Ising model odd bonds Markov random fields MAP image reconstruction image interpolation

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Parameter Selection for Wyner-Ziv coding of Laplacian Sources with Additive Laplacian or Gaussian Innovation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2009年第8期57卷 3208-3225页

作者： Mukherjee, Debargha Hewlett Packard Labs Palo Alto CA 94304 USA

A large number of practical coding scenarios deal with sources such as transform coefficients that can be well modeled as Laplacians. For regular coding of such sources, samples are often quantized by a family of uniform quantizers possibly with a dead-zone, and then entropy coded. For the Wyner-Ziv coding problem when correlated side-information is available at the decoder, the side-information can be modeled as obtained by independent additive Laplacian or Gaussian innovation on the source. This paper deals with optimal choice of parameters for practical Wyner-Ziv coding in such scenarios, using the same quantizer family as in the regular codec to cover a range of rate-distortion tradeoffs, given the variances of the source and additive noise. We propose and analyze a general encoding model based on multilevel coset codes that combines source coding and channel coding and show that at practical block lengths and code complexities, not pure channel coding but a hybrid combination of source coding and channel coding with right parameters provide optimal rate-distortion performance. We also provide a framework for on-the-fly parameter choice based on non-parametric representation of a set of seed functions, for use in scenarios where variances are estimated during encoding. A good insight in the optimal parameter selection mechanism is essential for building practical distributed codecs.

关键词： arithmetic coding channel coding distributed source coding Laplacian distribution optimal reconstruction rate-distortion analysis systematic repeat accumulate codes

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