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Bayesian Predictor Combination for Lossless Image Compression

IEEE TRANSACTIONS ON IMAGE PROCESSING 2013年第12期22卷 5263-5270页

作者： Martchenko, Andrew Deng, Guang La Trobe Univ Dept Elect Engn Bundoora Vic 3086 Australia

Adaptive predictor combination (APC) is a framework for combining multiple predictors for lossless image compression and is often at the core of state-of-the-art algorithms. In this paper, a Bayesian parameter estimation scheme is proposed for APC. Extensive experiments using natural, medical, and remote sensing images of 8-16 bit/pixel have confirmed that the predictive performance is consistently better than that of APC for any combination of fixed predictors and with only a marginal increase in computational complexity. The predictive performance improves with every additional fixed predictor, a property that is not found in other predictor combination schemes studied in this paper. Analysis and simulation show that the performance of the proposed algorithm is not sensitive to the choice of hyper-parameters of the prior distributions. Furthermore, the proposed prediction scheme provides a theoretical justification for the error correction stage that is often included as part of a prediction process.

关键词： Lossless image compression Bayesian learning adaptive prediction entropy coding context modeling

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High throughput entropy decoder design for H.265/HEVC

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第7期76卷 9877-9890页

作者： Choi, Jung-Ah Ho, Yo-Sung GIST 123 Cheomdangwagi Ro Gwangju 61005 South Korea

The H.265/high efficiency video coding (HEVC) standard employs context-based adaptive binary arithmetic coding (CABAC) as a single entropy coding method. Despite the high coding efficiency of CABAC, its context modeling process limits the throughput and prevents effective parallelization, particularly at the decoder. In this paper, we design a new joint rate distortion-decoding complexity (RDDC) cost function to improve the throughput performance at the H.265/HEVC decoder side. The proposed RDDC cost function includes the complexity term based on a newly designed complexity model using the linear regression. Experimental results show that the proposed method reduces the number of context-coded bins up to 19.8 % without significant coding loss.

关键词： H.265/high efficiency video coding (HEVC) Throughput improvement Complexity estimation Linear regression entropy coding CABAC

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Linear (zero-one) programming approach to fixed-rate entropy-coded vector quantisation

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IEE PROCEEDINGS-COMMUNICATIONS 1999年第5期146卷 275-282页

作者： Khandani, AK Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

The problem of the decoding of a shaped set is formulated in terms of a zero-one linear program. Some special features of the problem are exploited to relax the zero-one constraint, and to substantially reduce the complexity of the underlying simplex search. The proposed decoding method has applications in fixed-rate entropy-coded vector quantisation of a memoryless source, in decoding of a shaped constellation, and in the bit allocation problem. The first application is considered and numerical results are presented for the quantisation of a memoryless Gaussian source demonstrating substantial (of the order of a few tens to a few hundred times) reduction in the complexity with respect to the conventional methods based on dynamic programming. It is generally observed that the complexity of the proposed method has a linear increase with respect to the quantiser dimension. The corresponding numerical results show that it is possible to get very close to the bounds determined by the rate-distortion theory, while keeping the complexity at a relatively low level.

关键词： Complexity Quantization entropy coding DEcoding LOW LEVEL Vector quantization Bit rate

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THE COMPRESSION EFFECTS OF THE BINARY-TREE OVERLAPPING METHOD ON DIGITAL IMAGERY

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IEEE TRANSACTIONS ON COMMUNICATIONS 1990年第8期38卷 1260-1265页

作者： DIMENTO, LJ BERKOVICH, SY ALLIED SIGNAL AEROSP TECHNOL CTR COLUMBIA MD 21045 USA GEORGE WASHINGTON UNIV ENGN & APPL SCI WASHINGTON DC 20052 USA

A new method of digital image compression called binary tree overlapping (BTO) is described. With this method, an image is divided into bit planes that are transformed into a binary tree representation in which initial identical portions of different lines in the bit planes are transmitted as one path in the tree. To increase compression, distortion can be introduced by considering two lines which differ in fewer that a preset number of bit positions to be identical. A time efficient method of implementing BTO based on a communication technique called content-induced transaction overlap is outlined. With this technique, only simple, logical operations are needed and the compression time is proportional to the number of bits in the compressed image. Simulation studies indicate that BTO produces good quality images with a compression ratio of about three. In terms of implementation and compression efficiencies, BTO is comparable to first-order DPCM.< >

关键词： Image coding Binary trees Digital images Pulse modulation Hardware Predictive coding Modulation coding Transform coding entropy coding Costs

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High-resolution source coding for non-difference distortion measures: Multidimensional companding

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IEEE TRANSACTIONS ON INFORMATION THEORY 1999年第2期45卷 548-561页

作者： Linder, T Zamir, R Zeger, K Queens Univ Dept Math & Stat Kingston ON K7L 3N6 Canada Tel Aviv Univ Dept Elect Engn Syst IL-69978 Ramat Aviv Israel Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

entropy-coded vector quantization is studied using high-resolution multidimensional companding over a class of non-difference distortion measures. For distortion measures which are "locally quadratic" a rigorous derivation of the asymptotic distortion and entropy-coded rate of multidimensional companders is given along with conditions for the optimal choice of the compressor function. This optimum compressor, when it exists, depends on the distortion measure but not on the source distribution. The rate-distortion performance of the companding scheme is studied using a recently obtained asymptotic expression for the rate-distortion function which parallels the Shannon lower bound for difference distortion measures. It is proved that the high-resolution performance of the scheme is arbitrarily close to the rate-distortion limit for large quantizer dimensions if the compressor function and the lattice quantizer used in the companding scheme are optimal, extending an analogous statement for entropy-coded lattice quantization and MSE distortion. The companding approach is applied to obtain a high-resolution quantizing scheme for noisy sources.

关键词： asymptotic quantization theory entropy coding lattice quantizers multidimensional companding non-difference distortion measures rate-distortion function

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3D Point Cloud Attribute Compression Using Geometry-Guided Sparse Representation

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2020年 29卷 796-808页

作者： Gu, Shuai Hou, Junhui Zeng, Huanqiang Yuan, Hui Ma, Kai-Kuang Huaqiao Univ Sch Informat Sci & Engn Xiamen 361021 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen 51800 Peoples R China Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

3D point clouds associated with attributes are considered as a promising paradigm for immersive communication. However, the corresponding compression schemes for this media are still in the infant stage. Moreover, in contrast to conventional image/video compression, it is a more challenging task to compress 3D point cloud data, arising from the irregular structure. In this paper, we propose a novel and effective compression scheme for the attributes of voxelized 3D point clouds. In the first stage, an input voxelized 3D point cloud is divided into blocks of equal size. Then, to deal with the irregular structure of 3D point clouds, a geometry-guided sparse representation (GSR) is proposed to eliminate the redundancy within each block, which is formulated as an l(0)-norm regularized optimization problem. Also, an inter-block prediction scheme is applied to remove the redundancy between blocks. Finally, by quantitatively analyzing the characteristics of the resulting transform coefficients by GSR, an effective entropy coding strategy that is tailored to our GSR is developed to generate the bitstream. Experimental results over various benchmark datasets show that the proposed compression scheme is able to achieve better rate-distortion performance and visual quality, compared with state-of-the-art methods.

关键词： 3D point cloud sparse representation irregular structure predictive coding entropy coding

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ADAPTIVE entropy-CODED PRUNED TREE-STRUCTURED PREDICTIVE VECTOR QUANTIZATION OF IMAGES

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IEEE TRANSACTIONS ON COMMUNICATIONS 1993年第1期41卷 171-185页

作者： KIM, YH MODESTINO, JW Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

In recent work, we described an adaptive entropycoded predictive vector quantization (PVQ) scheme for images which was shown to be capable of excellent rate-distortion performance and to be surprisingly robust when applied to images outside the training set used in its design. This scheme made use of several entropy-constrained vector quantizers (ECVQ's), each with a corresponding Huffman encoder/decoder pair, embedded in a vector predictive feedback loop. The particular entropy-coded ECVQ in effect for any input image block depended upon the instantaneous occupancy state of a buffer used to interface the resulting variable-length codewords to a fixed-rate transmission or storage channel. This entropy-coded PVQ scheme is a vector extension of previous work on adaptive entropy-coded predictive scalar quantization (PSQ);in particular, 2-D DPCM. The embedded ECVQ in this adaptive entropy-coded PVQ scheme made use of a modification of a recently introduced design algorithm, based upon clustering, which resulted in unstructured codebooks. Unfortunately, the computational complexity associated with this unstructured embedded ECVQ can be substantial. In this paper we describe much simpler versions of this adaptive entropy-coded PVQ scheme where the embedded ECVQ is replaced by a pruned tree-structured VQ (PTSVQ). The resulting encoding scheme is shown to result in drastically reduced complexity at only a small cost in performance. We demonstrate coding results on selected real-world images.

关键词： entropy coding predictive vector quantization coding systems Vector quantization Self tuning

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HIGH-SPEED PATTERN-MATCHING FOR A FAST HUFFMAN DECODER

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 1995年第1期41卷 97-103页

作者： CHOI, SB LEE, MH Department of Information & Communication Engineering Chonbuk National University of Korea Cheongju South Korea

The fast Huffman decoding algorithm has been used in JPEG, MPEG and image data compression standards, etc. And code compression is a key element in high speed digital data transport. A major compression is performed by converting the fixed-length codes to variable-length codes through a entropy coding scheme. Huffman coding combined with run-length coding is shown to be a very efficient coding shceme. To speed up the process of search for a symbol in a Huffman tree and to reduce the memory size we have proposed a tree clustering and a pattern matching algorithm to avoid high sparsity of the tree. The method is shown to be very efficient in memory size and fast searching for the symbol. For an experimental video data with Huffman codes extended up to 16 bits in length, i.e. it is used for the standard JPEG, the result of experiments show that the proposed algorithm has a very high speed and performance. The design of the decoder is carried out using silicon-gate CMOS process.

关键词： Pattern matching Decoding Transform coding Image coding Data compression Image converters entropy coding Huffman coding Clustering algorithms Code standards

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Security analysis of multimedia encryption schemes based on multiple Huffman table

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IEEE SIGNAL PROCESSING LETTERS 2007年第3期14卷 201-204页

作者： Zhou, Jiantao Liang, Zhiqin Chen, Yan Au, Oscar C. Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Hong Kong Peoples R China

This letter addresses the security issues of the multimedia encryption schemes using multiple Huffman table (MHT). A known-plaintext attack is presented to show that the MHTs used for encryption should be carefully selected to avoid the weak keys problem. We then propose chosen-plaintext attacks on the basic MHT algorithm as well as the enhanced scheme with random bit insertion. In addition, we suggest two empirical criteria for Huffman table selection, based on which we can simplify the stream cipher integrated scheme, while ensuring a high level of security.

关键词： cryptanalysis entropy coding Huffman coding lightweight encryption multimedia security

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Enhancing Octree-Based Context Models for Point Cloud Geometry Compression With Attention-Based Child Node Number Prediction

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 1835-1839页

作者： Sun, Chang Yuan, Hui Mao, Xiaolong Lu, Xin Hamzaoui, Raouf Shandong Univ Joint SDU NTU Ctr Artificial Intelligence Res & Sc Jinan 250000 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH England De Montfort Univ Sch Engn & Sustainable Dev Leicester LE1 9BH England

In point cloud geometry compression, most octree-based context models use the cross-entropy between the one-hot encoding of node occupancy and the probability distribution predicted by the context model as the loss. This approach converts the problem of predicting the number (a regression problem) and the position (a classification problem) of occupied child nodes into a 255-dimensional classification problem. As a result, it fails to accurately measure the difference between the one-hot encoding and the predicted probability distribution. We first analyze why the cross-entropy loss function fails to accurately measure the difference between the one-hot encoding and the predicted probability distribution. Then, we propose an attention-based child node number prediction (ACNP) module to enhance the context models. The proposed module can predict the number of occupied child nodes and map it into an 8-dimensional vector to assist the context model in predicting the probability distribution of the occupancy of the current node for efficient entropy coding. Experimental results demonstrate that the proposed module enhances the coding efficiency of octree-based context models.

关键词： Context modeling Predictive models Point cloud compression Probability distribution Loss measurement Solid modeling Geometry Context model deep learning entropy coding point cloud geometry compression

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