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Power Series quantization for Noisy channels

IEEE TRANSACTIONS ON COMMUNICATIONS 2010年第5期58卷 1405-1414页

作者： Persson, Daniel Eriksson, Thomas Linkoping Univ Dept Elect Engn S-58183 Linkoping Sweden Chalmers Univ Technol Dept Signals & Syst S-41296 Gothenburg Sweden

A recently proposed method for transmission of correlated sources under noise-free conditions, power series quantization (PSQ), uses a separate linear or nonlinear predictor for each quantizer region, and has shown to increase performance compared to several common quantization schemes for sources with memory. In this paper, it is shown how to apply PSQ for transmission of a source with memory over a noisy channel. A channel-optimized PSQ (COPSQ) encoder and codebook optimization algorithms are derived. The suggested scheme is shown to increase performance compared with previous state-of-the-art methods.

关键词： channel-optimized quantization noisy channels memory-based quantization spectrum coding

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Lossy Multicasting Over Binary Symmetric Broadcast channels

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2011年第8期59卷 3915-3929页

作者： Bursalioglu, Ozgun Y. Fresia, Maria Caire, Giuseppe Poor, H. Vincent Univ So Calif Ming Hsieh Dept Elect Engn Los Angeles CA 90089 USA Intel Mobile Commun Munich Germany Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

Lossy multicasting of a set of independent, discrete-time, continuous-amplitude source components under the mean square error distortion measure over binary symmetric broadcast channels is investigated. The practically appealing concatenation of successive refinement source coding with broadcast coding and, specifically, time-sharing of linear binary codes, is considered. Three different system optimization criteria are formulated for the lossy multicasting problem. The resulting system optimization is fairly general and applies to a variety of combinations of successive refinement source codes and channel codes. The system optimization is investigated in depth for a class of channel optimized quantization with successive refinement, obtained by using standard embedded scalar quantizers and linear mapping of the (redundant) quantizer bitplanes onto channel codewords by using a systematic Raptor encoder. This scheme is referred to as quantization with linear index coding (QLIC). Unlike existing literature on progressive transmission with unequal error protection or channel optimized quantization, the focus here is on the regime of moderate-to-large code block length and the power of modern sparse-graph codes with iterative belief propagation decoding is leveraged. In this regime, the system optimization takes on the form of simple convex programming that reduces to linear programming for QLIC. The performance of QLIC compares favorably with respect to the state of the art channel optimized quantization in the conventional setting of a single Gaussian source over a binary symmetric channel. For the multicast scenario, the performance gap incurred by the practical QLIC design with respect to ideal source and channel codes is quantified.

关键词： Block codes channel-optimized quantization multicast communication rateless codes source-channel coding

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A Linear Encoding Approach to Index Assignment in Lossy Source-channel Coding

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IEEE TRANSACTIONS ON INFORMATION THEORY 2010年第3期56卷 1322-1344页

作者： Fresia, Maria Caire, Giuseppe Infineon Technol AG Munich Germany Univ So Calif Dept Elect Engn Los Angeles CA 90007 USA

We present a general scheme for the lossy transmission of a source with arbitrary statistics through a noisy channel under the mean-square error fidelity criterion. Our approach is based on transform coding, scalar quantization of the transform coefficients and linear encoding of the quantization indices. Entropy coding and channel coding are merged into a single linear encoding function, such that the "catastrophic" behavior of conventional entropy coding is avoided and the full power of modern coding techniques and iterative "Belief-Propagation" decoding can be exploited. We show that this approach is asymptotically optimal in the limit of large block length, for arbitrary source statistics and binary-input output-symmetric channel. In the practical regime of finite block length and low decoding complexity, we show, through the explicit construction of codes for the lossy transmission of digital images over a binary symmetric channel, that our approach yields significant improvements with respect to previously proposed channel-optimized quantization schemes and also with respect to the conventional concatenation of state-of-the art image coding with state-of-the art channel coding. Although our constructive example focuses on a special case, the approach is general and can be applied to other classes of sources of practical interest.

关键词： channel-optimized quantization iterative decoding source-channel coding

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On Multiple Description Coding of Sources with Memory

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IEEE TRANSACTIONS ON COMMUNICATIONS 2010年第8期58卷 2242-2251页

作者： Persson, Daniel Eriksson, Thomas Linkoping Univ Dept Elect Engn S-58183 Linkoping Sweden Chalmers Univ Technol Dept Signals & Syst S-41296 Gothenburg Sweden

We propose a framework for multiple description coding (MDC) of sources with memory. A new source coding method for lossless transmission of correlated sources, power series quantization (PSQ), was recently suggested. PSQ uses a separate linear or non-linear predictor for each quantizer region, and has shown increased performance compared to several common quantization schemes for sources with memory. We propose multiple description PSQ as a special case within our framework. The suggested scheme is shown to increase performance compared with previous state-of-the-art MDC methods.

关键词： channel-optimized quantization packet erasure channel memory-based quantization

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A COVQ-Based image coder for channels with bit errors and erasures

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IEEE TRANSACTIONS ON COMMUNICATIONS 2008年第2期56卷 161-165页

作者： Andersson, Tomas Skoglund, Mikael Royal Inst Technol Sch Elect Engn SE-10044 Stockholm Sweden

We illustrate how channel optimized vector quantization (COVQ) can be used for channels with both bit-errors and bit-erasures. First, a memoryless channel model is presented, and the performance of COVQ's trained for this channel is evaluated for an i.i.d. Gaussian source. Then, the new method is applied in implementing an error-robust sub-band image coder, and we present image results that illustrate the resulting performance. Our experiments show that the new approach is able to outperform a traditional scheme based on separate source and channel coding.

关键词： binary erasure channels binary symmetric channels channel-optimized quantization image coding joint source-channel coding

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Design of sample adaptive product quantizers for noisy channels

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IEEE TRANSACTIONS ON COMMUNICATIONS 2005年第4期53卷 576-580页

作者： Raza, Z Alajaji, F Linder, T T Mobile Bellevue WA 98006 USA Queens Univ Dept Math & Stat Kingston ON K7L 3N6 Canada Hungarian Acad Sci Informat Lab Comp & Automat Res Inst Budapest Hungary

channel-optimized vector quantization (COVQ) has proven to be an effective joint source-channel coding technique that makes the underlying quantizer robust to channel noise. Unfortunately, COVQ retains the high encoding complexity of the standard vector quantizer (VQ) for medium-to-high quantization dimensions and moderate-to-good channel conditions. A technique called sample adaptive product quantization (SAPQ) was recently introduced by Kim and Shroff to reduce the complexity of the VQ while achieving comparable distortions. In this letter, we generalize the design of SAPQ for the case of memoryless noisy channels by optimizing the quantizer with respect to both source and channel statistics. Numerical results demonstrate that the channel-optimized SAPQ (COSAPQ) achieves comparable performance to the COVQ (within 0.2 dB), while maintaining considerably lower encoding complexity (up to half of that of COVQ) and storage requirements. Robustness of the COSAPQ system against channel mismatch is also examined.

关键词： channel-optimized quantization encoding/storage complexity joint source-channel coding structurally constrained vector quantization

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