Distributed coding using punctured quasi-arithmetic codes for memory and memoryless sources

This correspondence considers the use of punctured quasi-arithmetic (QA) codes for the Slepian–Wolf problem. These entropy codes are defined by finite state machines for memoryless and first-order memory sources. Puncturing an entropy coded bit-stream leads to an ambiguity at the decoder side. The decoder makes use of a correlated version of the original message in order to remove this ambiguity. A complete distributed source coding (DSC) scheme based on QA encoding with side information at the decoder is presented, together with iterative structures based on QA codes. The proposed schemes are adapted to memoryless and first-order memory sources. Simulation results reveal that the proposed schemes are efficient in terms of decoding performance for short sequences compared to well-known DSC solutions using channel codes.Peer ReviewedPostprint (published version

Wyner-Ziv coding based on TCQ and LDPC codes and extensions to multiterminal source coding

Driven by a host of emerging applications (e.g., sensor networks and wireless video), distributed source coding (i.e., Slepian-Wolf coding, Wyner-Ziv coding and various other forms of multiterminal source coding), has recently become a very active research area. In this thesis, we first design a practical coding scheme for the quadratic Gaussian Wyner-Ziv problem, because in this special case, no rate loss is suffered due to the unavailability of the side information at the encoder. In order to approach the Wyner-Ziv distortion limit D??W Z(R), the trellis coded quantization (TCQ) technique is employed to quantize the source X, and irregular LDPC code is used to implement Slepian-Wolf coding of the quantized source input Q(X) given the side information Y at the decoder. An optimal non-linear estimator is devised at the joint decoder to compute the conditional mean of the source X given the dequantized version of Q(X) and the side information Y . Assuming ideal Slepian-Wolf coding, our scheme performs only 0.2 dB away from the Wyner-Ziv limit D??W Z(R) at high rate, which mirrors the performance of entropy-coded TCQ in classic source coding. Practical designs perform 0.83 dB away from D??W Z(R) at medium rates. With 2-D trellis-coded vector quantization, the performance gap to D??W Z(R) is only 0.66 dB at 1.0 b/s and 0.47 dB at 3.3 b/s. We then extend the proposed Wyner-Ziv coding scheme to the quadratic Gaussian multiterminal source coding problem with two encoders. Both direct and indirect settings of multiterminal source coding are considered. An asymmetric code design containing one classical source coding component and one Wyner-Ziv coding component is first introduced and shown to be able to approach the corner points on the theoretically achievable limits in both settings. To approach any point on the theoretically achievable limits, a second approach based on source splitting is then described. One classical source coding component, two Wyner-Ziv coding components, and a linear estimator are employed in this design. Proofs are provided to show the achievability of any point on the theoretical limits in both settings by assuming that both the source coding and the Wyner-Ziv coding components are optimal. The performance of practical schemes is only 0.15 b/s away from the theoretical limits for the asymmetric approach, and up to 0.30 b/s away from the limits for the source splitting approach

Bridging Hamming Distance Spectrum with Coset Cardinality Spectrum for Overlapped Arithmetic Codes

Overlapped arithmetic codes, featured by overlapped intervals, are a variant of arithmetic codes that can be used to implement Slepian-Wolf coding. To analyze overlapped arithmetic codes, we have proposed two theoretical tools: Coset Cardinality Spectrum (CCS) and Hamming Distance Spectrum (HDS). The former describes how source space is partitioned into cosets (equally or unequally), and the latter describes how codewords are structured within each coset (densely or sparsely). However, until now, these two tools are almost parallel to each other, and it seems that there is no intersection between them. The main contribution of this paper is bridging HDS with CCS through a rigorous mathematical proof. Specifically, HDS can be quickly and accurately calculated with CCS in some cases. All theoretical analyses are perfectly verified by simulation results

Layered Wyner-Ziv video coding for noisy channels

The growing popularity of video sensor networks and video celluar phones has generated the need for low-complexity and power-efficient multimedia systems that can handle multiple video input and output streams. While standard video coding techniques fail to satisfy these requirements, distributed source coding is a promising technique for ??uplink?? applications. Wyner-Ziv coding refers to lossy source coding with side information at the decoder. Based on recent theoretical result on successive Wyner-Ziv coding, we propose in this thesis a practical layered Wyner-Ziv video codec using the DCT, nested scalar quantizer, and irregular LDPC code based Slepian-Wolf coding (or lossless source coding with side information) for noiseless channel. The DCT is applied as an approximation to the conditional KLT, which makes the components of the transformed block conditionally independent given the side information. NSQ is a binning scheme that facilitates layered bit-plane coding of the bin indices while reducing the bit rate. LDPC code based Slepian-Wolf coding exploits the correlation between the quantized version of the source and the side information to achieve further compression. Different from previous works, an attractive feature of our proposed system is that video encoding is done only once but decoding allowed at many lower bit rates without quality loss. For Wyner-Ziv coding over discrete noisy channels, we present a Wyner-Ziv video codec using IRA codes for Slepian-Wolf coding based on the idea of two equivalent channels. For video streaming applications where the channel is packet based, we apply unequal error protection scheme to the embedded Wyner-Ziv coded video stream to find the optimal source-channel coding trade-off for a target transmission rate over packet erasure channel

Protograph LDPC Based Distributed Joint Source Channel Coding

该文提出一种基于原模图低密度奇偶校验(P-LDPC)码的分布式联合信源信道编译码系统方案。该方案编码端,分布式信源发送部分信息位及校验位以同时实; 现压缩及纠错功能;译码端,联合迭代信源信道译码的运用进一步发掘信源的相关性以获得额外的编码增益。此外,论文研究了所提方案在译码端未知相关性系数的; 译码算法。仿真结果表明,所提出的基于P-LDPC码的分布式联合信源信道编译码方案在外部迭代次数不大的情况可以获得较大的性能增益,并且相关性系数在; 译码端已知和未知系统性能基本相当。This paper proposes a Distributed Joint Source-Channel Coding (DJSCC); scheme using Protograph Low Density Parity Check (P-LDPC) code. In the; proposed scheme, the distributed source encoder sends some information; bits together with the parity bits to simultaneously achieve both; distributed compression and channel error correction. Iterative joint; decoding is introduced to further exploit the source correlation.; Moreover, the proposed scheme is investigated when the correlation; between sources is not known at the decoder. Simulation results indicate; that the proposed DJSCC scheme can obtain relatively large additional; coding gains at a relatively small number of global iterations, and the; performance for unknown correlated sources is almost the same as that; for known correlated sources since correlation can be estimated jointly; with the iterative decoding process.福建省自然科学基金; 国家自然科学基

Design techniques for graph-based error-correcting codes and their applications

In ShannonÂs seminal paper, ÂA Mathematical Theory of CommunicationÂ, he defined ÂChannel Capacity which predicted the ultimate performance that transmission systems can achieve and suggested that capacity is achievable by error-correcting (channel) coding. The main idea of error-correcting codes is to add redundancy to the information to be transmitted so that the receiver can explore the correlation between transmitted information and redundancy and correct or detect errors caused by channels afterward. The discovery of turbo codes and rediscovery of Low Density Parity Check codes (LDPC) have revived the research in channel coding with novel ideas and techniques on code concatenation, iterative decoding, graph-based construction and design based on density evolution. This dissertation focuses on the design aspect of graph-based channel codes such as LDPC and Irregular Repeat Accumulate (IRA) codes via density evolution, and use the technique (density evolution) to design IRA codes for scalable image/video communication and LDPC codes for distributed source coding, which can be considered as a channel coding problem. The first part of the dissertation includes design and analysis of rate-compatible IRA codes for scalable image transmission systems. This part presents the analysis with density evolution the effect of puncturing applied to IRA codes and the asymptotic analysis of the performance of the systems. In the second part of the dissertation, we consider designing source-optimized IRA codes. The idea is to take advantage of the capability of Unequal Error Protection (UEP) of IRA codes against errors because of their irregularities. In video and image transmission systems, the performance is measured by Peak Signal to Noise Ratio (PSNR). We propose an approach to design IRA codes optimized for such a criterion. In the third part of the dissertation, we investigate Slepian-Wolf coding problem using LDPC codes. The problems to be addressed include coding problem involving multiple sources and non-binary sources, and coding using multi-level codes and nonbinary codes

Balanced Distributed Coding of Omnidirectional Images

This paper presents a distributed coding scheme for the representation of 3D scenes captured by a network of omnidirectional cameras. We consider a scenario where images captured at different viewpoints are encoded independently, with a balanced rate distribution among the different cameras. The distributed coding is built on multiresolution representation and partitioning of the visual information in each camera. The encoder then transmits one partition after entropy coding, as well as the syndrome bits resulting from the channel encoding of the other partition. The joint decoder exploits the intra-view correlation by predicting the missing source information with help of the syndrome bits. At the same time, it exploits the inter-view correlation by using motion estimation between images from different cameras. Experiments demonstrate that the distributed coding solution performs better than a scheme where images are handled independently, while the coding rate advantageously stays balanced between encoders