Network vector quantization

We present an algorithm for designing locally optimal vector quantizers for general networks. We discuss the algorithm's implementation and compare the performance of the resulting "network vector quantizers" to traditional vector quantizers (VQs) and to rate-distortion (R-D) bounds where available. While some special cases of network codes (e.g., multiresolution (MR) and multiple description (MD) codes) have been studied in the literature, we here present a unifying approach that both includes these existing solutions as special cases and provides solutions to previously unsolved examples

Minimum Distortion Variance Concatenated Block Codes for Embedded Source Transmission

Some state-of-art multimedia source encoders produce embedded source bit streams that upon the reliable reception of only a fraction of the total bit stream, the decoder is able reconstruct the source up to a basic quality. Reliable reception of later source bits gradually improve the reconstruction quality. Examples include scalable extensions of H.264/AVC and progressive image coders such as JPEG2000. To provide an efficient protection for embedded source bit streams, a concatenated block coding scheme using a minimum mean distortion criterion was considered in the past. Although, the original design was shown to achieve better mean distortion characteristics than previous studies, the proposed coding structure was leading to dramatic quality fluctuations. In this paper, a modification of the original design is first presented and then the second order statistics of the distortion is taken into account in the optimization. More specifically, an extension scheme is proposed using a minimum distortion variance optimization criterion. This robust system design is tested for an image transmission scenario. Numerical results show that the proposed extension achieves significantly lower variance than the original design, while showing similar mean distortion performance using both convolutional codes and low density parity check codes.Comment: 6 pages, 4 figures, In Proc. of International Conference on Computing, Networking and Communications, ICNC 2014, Hawaii, US

Multiresolution vector quantization

Multiresolution source codes are data compression algorithms yielding embedded source descriptions. The decoder of a multiresolution code can build a source reproduction by decoding the embedded bit stream in part or in whole. All decoding procedures start at the beginning of the binary source description and decode some fraction of that string. Decoding a small portion of the binary string gives a low-resolution reproduction; decoding more yields a higher resolution reproduction; and so on. Multiresolution vector quantizers are block multiresolution source codes. This paper introduces algorithms for designing fixed- and variable-rate multiresolution vector quantizers. Experiments on synthetic data demonstrate performance close to the theoretical performance limit. Experiments on natural images demonstrate performance improvements of up to 8 dB over tree-structured vector quantizers. Some of the lessons learned through multiresolution vector quantizer design lend insight into the design of more sophisticated multiresolution codes

Rate-distortion performance for joint source and channel coding of images

Caption title.Includes bibliographical references (p. 31-32).Supported by the German Educational Exchange Service (DAAD) as part of the HSP II-program, and in part by ARPA. F30602-92-C-0030 Supported by the Laboratory for Information and Decision Systems, Massachusetts Institute of Technology. DAAH04-95-1-0103Michael J. Ruf, James W. Modestino

Study and simulation of low rate video coding schemes

The semiannual report is included. Topics covered include communication, information science, data compression, remote sensing, color mapped images, robust coding scheme for packet video, recursively indexed differential pulse code modulation, image compression technique for use on token ring networks, and joint source/channel coder design

Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs