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Multiple-Description coding by Dithered Delta-Sigma Quantization

IEEE TRANSACTIONS ON INFORMATION THEORY 2009年第10期55卷 4661-4675页

作者： Ostergaard, Jan Zamir, Ram Aalborg Univ Dept Elect Syst Aalborg Denmark Tel Aviv Univ Dept Elect Engn Syst IL-69978 Tel Aviv Israel

We address the connection between the multiple-description (MD) problem and Delta-Sigma quantization. The inherent redundancy due to oversampling in Delta-Sigma quantization, and the simple linear-additive noise model resulting from dithered lattice quantization, allow us to construct a symmetric and time-invariant MD coding scheme. We show that the use of a noise-shaping filter makes it possible to trade off central distortion for side distortion. Asymptotically, as the dimension of the lattice vector quantizer and order of the noise-shaping filter approach infinity, the entropy rate of the dithered Delta-Sigma quantization scheme approaches the symmetric two-channel MD rate-distortion function for a memoryless Gaussian source and mean square error (MSE) fidelity criterion, at any side-to-central distortion ratio and any resolution. In the optimal scheme, the infinite-order noise-shaping filter must be minimum phase and have a piecewise flat power spectrum with a single jump discontinuity. An important advantage of the proposed design is that it is symmetric in rate and distortion by construction, so the coding rates of the descriptions are identical and there is therefore no need for source splitting.

关键词： Delta-Sigma modulation dithered lattice quantization entropy coding joint source-channel coding multiple-description (MD) coding vector quantization

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Image Compression Based on Compressive Sensing: End-to-End Comparison With JPEG

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IEEE TRANSACTIONS ON MULTIMEDIA 2020年第11期22卷 2889-2904页

作者： Yuan, Xin Haimi-Cohen, Raziel Bell Labs 600 Mt Ave Murray Hill NJ 07974 USA Verizon Labs Bedminster NJ 07921 USA

We present an end-to-end image compression system based on compressive sensing. The presented system integrates the conventional scheme of compressive sampling (on the entire image) and reconstruction with quantization and entropy coding. The compression performance, in terms of decoded image quality versus data rate, is shown to be comparable with JPEG and significantly better at the low rate range. We study the parameters that influence the system performance, including (i) the choice of sensing matrix, (ii) the trade-off between quantization and compression ratio, and (iii) the reconstruction algorithms. We propose an effective method to select, among all possible combinations of quantization step and compression ratio, the ones that yield the near-best quality at any given bit rate. Furthermore, our proposed image compression system can be directly used in the compressive sensing camera, e.g., the single pixel camera, to construct a hardware compressive sampling system.

关键词： Image coding Transform coding Quantization (signal) Sensors Image reconstruction Reconstruction algorithms Bit rate Compressive sensing image compression quantization entropy coding sparse coding reconstruction JPEG JPEG2000

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Sub-block interchange for lossless image compression

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 1999年第1期45卷 236-242页

作者： Ng, KS Cheng, LM City Univ Hong Kong Dept Elect Engn Kowloon SAR Hong Kong Peoples R China

Lempel-Ziv-Welch (LZW) technique for text compression has been successfully modified to lossless image compression such as GIF. Recently, a new class of text compression, namely, Burrows and Wheeler Transformation (BWT) has been developed which gives promising results for text compression. Here, we propose a sub-block interchange lossless compression method which belongs to this block sorting class. Our compression results have outperformed GIF in compression ratios and BWT in compression times when tested with 512x512 pixel 8-bit grey scale images. The comparison of compression ratios and times with GIF, BWT and other popular LZ based compression methods are discussed.

关键词： Image coding Sorting Testing Pixel Compression algorithms Data compression Performance loss Decoding entropy coding

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Multiple shell structured hypercube feature maps for vector quantization of images

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SIGNAL PROCESSING-IMAGE COMMUNICATION 1996年第6期8卷 501-512页

作者： Kim, YK Ra, JB KOREA ADV INST SCI & TECHNOL DEPT ELECT ENGN YUSONG GU TAEJON 305701 SOUTH KOREA

A new neural network architecture is proposed for spatial domain image vector quantization (VQ). The proposed model has a multiple shell structure consisting of binary hypercube feature maps of various dimensions, which are extended forms of Kohonen's self-organizing feature maps (SOFMs). It is trained so that each shell can contain similar-feature vectors. A partial search scheme using the neighborhood relationship of hypercube feature maps can reduce the computational complexity drastically with marginal coding efficiency degradation. This feature is especially proper for vector quantization of a large block or high dimension. The proposed scheme can also provide edge preserving VQ by increasing the number of shells, because shells far from the origin are trained to contain edge block features.

关键词： vector quantization multiple shell structure hypercube feature maps edge preservation entropy coding

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Biased Run-Length coding of Bilevel Classification Label Maps of Hyperspectral Images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2017年第10期10卷 4580-4588页

作者： Liaghati, Amir Leon Pan, W. David Jiang, Zhuocheng Boeing Co Huntsville AL 35824 USA Univ Alabama Dept Elect & Comp Engn Huntsville AL 35899 USA

For efficient coding of bilevel sources with some dominant symbols often found in classification label maps of hyperspectral images, we proposed a novel biased run-length (BRL) coding method, which codes the most probable symbols separately from other symbols. To determine the conditions in which the BRL coding method would be effective, we conducted an analysis of the method using statistical models. We first analyzed the effect of 2-D blocking of pixels, which were assumed to have generalized Gaussian distributions. The analysis showed that the resulting symbol blocks tended to have lower entropies than the original source without symbol blocking. We then analyzed the BRL coding method applied on the sequence of block symbols characterized by a first-order Markov model. Information-theoretic analysis showed that the BRL coding method tended to generate codewords that have lower entropies than the conventional run-length coding method. Furthermore, numerical simulations on lossless compression of actual data showed improvement of the state of the art. Specifically, end-to-end implementation integrating symbol blocking, BRL, and Huffman coding achieved up to 4.3% higher compression than the JBIG2 standard method and up to 3.2% higher compression than the conventional run-length coding method on classification label maps of the widely used "Indian Pines" dataset.

关键词： Data compression entropy coding hyperspectral imaging image classification

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Sequence Statistical Code Based Data Compression Algorithm for Wireless Sensor Network

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WIRELESS PERSONAL COMMUNICATIONS 2019年第3期106卷 971-985页

作者： Jancy, S. Jayakumar, C. Sathyabama Univ CSE Dept Chennai Tamil Nadu India Sri Venkateswara Coll Engn Chennai Tamil Nadu India

Sensors play an integral part in the technologically advanced real world. Wireless sensors are which have powered by batteries with limited capacity. Hence energy efficiency is one of the major issues with wireless sensors. Many techniques have been proposed in order to improve sensor efficiency. This paper discusses to improve energy efficiency of sensor through data compression. Sequence statistical code based data compression algorithm is being proposed to improve the energy efficiency of sensors. SDC and FOST codes were used in this algorithm in order to achieve better compression ratio. The simulation result was compared with arithmetic data compression techniques. In the proposed algorithm computation process is very simple than arithmetic data compression techniques.

关键词： Data compression Wireless sensor networks entropy coding Arithmetic data compression techniques

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Hardware architecture for AVS entropy encoder

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2008年第2期54卷 769-777页

作者： Xu, Long Deng, Lei Ji, Xiangyang Peng, Xiaoming Gao, Wen Chinese Acad Sci Inst Comp Technol Beijing 100080 Peoples R China Peking Univ Inst Digital Media Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China

In AVS-P2 video compression standard, similar to MPEG-2, entropy coding firstly assembles two dimensional coefficients of each block into a sequence of (Run, Level) combinations serially. As we know, such the serial run-length method is usually undesirable for hardware accelerator and thus, this paper proposes an efficient parallel algorithm to Run-Length coding, which can determine the (Run, Level) combinations for one row of coefficients from a block in one clock cycle. In addition, Level-based multiple VLC tables switch mechanism (Context-based VLC) is further introduced in AVS-P2 entropy coding module to identify the big variation of probability distribution of (Run, Level) combinations. As a result, table selection for coding the current Level necessarily depends on the previously coded coefficients. Thus, we propose a parallel Looking-Up Table method, which can select the tables for one row of coefficients from a block in one clock cycle. On the other band, at RDO stage, the calculation of rate term only needs to get the number of bits for each coded signal without the knowledge of its concrete value. Consequently, in hardware design, the Looking-Up Table in pre-coding can be mapped into a series of logic operations and thus much hardware memory can be saved. At the actual entropy coding, we only need to replace the logic operation of pre-coding with the actual 2D-VLC tables. Using our proposed hardware accelerator of A VS entropy coder, the results of simulation and synthesis demonstrate that the computing complexity and memory requirements are both reduced.

关键词： AVS-P2 entropy coding VLC context-based hardware pipeline

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End-to-end deep multispectral image compression based on interspectral prediction network

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JOURNAL OF APPLIED REMOTE SENSING 2022年第3期16卷

作者： Kong, Fanqiang Meng, Yuxin Li, Dan Hu, Kedi Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing Peoples R China

Multispectral images have numerous features and a wide range of applications. However, traditional image compression methods, such as JPEG2000 and 3D-SPIHT, do not make effective use of spectral information. We propose a deep compression framework based on interspectral prediction to take full advantage of spectral correlation when using temporal correlation for interframe prediction in video compression. First, two-dimensional and three-dimensional convolutions were used to obtain spatial and spectral information for predicting the original image. Then, we applied a residual neural network to compress the residual information of the image. Subsequently, a decoder was employed to reconstruct the multispectral image based on the compressed image and residual information. All components were jointly trained by a single loss function that considered the tradeoff between the compression bit rate and decoded image quality. The experimental results showed that our proposed method outperformed other traditional compression algorithms, including JPEG2000, 3D-SPIHT, and PCA+JPEG2000, in terms of peak signal-to-noise ratio and spectral angle and is equivalent to or even better than some image compression algorithms based on deep neural networks. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： multispectral images interspectral prediction network rate-distortion optimization entropy coding

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ANALYSIS AND FURTHER RESULTS ON ADAPTIVE entropy-CODED QUANTIZATION

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IEEE TRANSACTIONS ON INFORMATION THEORY 1990年第5期36卷 1069-1088页

作者： HARRISON, DD MODESTINO, JW RENSSELAER POLYTECH INST DEPT ELECT COMP & SYST ENGNTROYNY 12181

Buffer underflow and overflow problems associated with entropy coding are completely eliminated by effectively imposing reflecting walls at the buffer endpoints. Synchronous operation of the AECQ (adaptive entropy-coded quantizer) encoder and decoder is examined in detail, and it is shown that synchronous operation is easily achieved without side information. A method is developed to explicitly solve for the buffer-state probability distribution and the resulting average distortion when memoryless buffer-state feedback is used as well as when the source is stationary and memoryless. This method is then used as a tool in the design of low-distortion AECQ systems, with particular attention given to developing source scale-invariant distortion performance. It is shown that the introduction of reflecting buffer walls in a properly designed AECQ system results in a very small rate-distortion performance penalty and that the resulting AECQ system can be an extremely simple and effective solution to the stationary memoryless source-coding problem for a wide range of source types. Operation with nonstationary sources is also examined.

关键词： Quantization synchronous operation average distortion decoders entropy coding Probability distribution Self tuning

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Do optimal entropy-constrained quantizers have a finite or infinite number of codewords?

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IEEE TRANSACTIONS ON INFORMATION THEORY 2003年第11期49卷 3031-3037页

作者： György, A Linder, T Chou, PA Betts, BJ Queens Univ Dept Math & Stat Kingston ON K7L 3N6 Canada Microsoft Corp Redmond WA 98052 USA NASA Ames Res Ctr Moffett Field CA 94035 USA

An entropy-constrained quantizer Q is optimal if it minimizes the expected distortion D(Q) subject to a constraint on the output entropy H(Q). In this correspondence, we use the Lagrangian formulation to show the existence and study the structure of optimal entropy-constrained quantizers that achieve a point on the lower convex hull of the operational distortion-rate function D-h(R) = inf(Q){D(Q) : H(Q) less than or equal to R}. In general, an optimal entropy-constrained quantizer may have a countably infinite number of codewords. Our main results show that if the tail of the source distribution is sufficiently light (resp., heavy) with respect to the distortion measure, the Lagrangian-optimal entropy-constrained quantizer has a finite (resp., infinite) number of codewords. In particular, for the squared error distortion measure, if the tail of the source distribution is lighter than the tail of a Gaussian distribution, then the Lagrangian-optimal quantizer has only a finite number of codewords, while if the tail is heavier than that of the Gaussian, the Lagrangian-optimal quantizer has an infinite number of codewords.

关键词： difference distortion measures entropy coding infinite-level quantizers Lagrangian performance optimal quantization

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