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

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

Development and evaluation of packet video schemes

Reflecting the two tasks proposed for the current year, namely a feasibility study of simulating the NASA network, and a study of progressive transmission schemes, are presented. The view of the NASA network, gleaned from the various technical reports made available to use, is provided. Also included is a brief overview of how the current simulator could be modified to accomplish the goal of simulating the NASA network. As the material in this section would be the basis for the actual simulation, it is important to make sure that it is an accurate reflection of the requirements on the simulator. Brief descriptions of the set of progressive transmission algorithms selected for the study are contained. The results available in the literature were obtained under a variety of different assumptions, not all of which are stated. As such, the only way to compare the efficiency and the implementational complexity of the various algorithms is to simulate them

Multiresolution source coding using entropy constrained dithered scalar quantization

In this paper, we build multiresolution source codes using entropy constrained dithered scalar quantizers. We demonstrate that for n-dimensional random vectors, dithering followed by uniform scalar quantization and then by entropy coding achieves performance close to the n-dimensional optimum for a multiresolution source code. Based on this result, we propose a practical code design algorithm and compare its performance with that of the set partitioning in hierarchical trees (SPIHT) algorithm on natural images

Heuristics for Network Coding in Wireless Networks

Multicast is a central challenge for emerging multi-hop wireless architectures such as wireless mesh networks, because of its substantial cost in terms of bandwidth. In this report, we study one specific case of multicast: broadcasting, sending data from one source to all nodes, in a multi-hop wireless network. The broadcast we focus on is based on network coding, a promising avenue for reducing cost; previous work of ours showed that the performance of network coding with simple heuristics is asymptotically optimal: each transmission is beneficial to nearly every receiver. This is for homogenous and large networks of the plan. But for small, sparse or for inhomogeneous networks, some additional heuristics are required. This report proposes such additional new heuristics (for selecting rates) for broadcasting with network coding. Our heuristics are intended to use only simple local topology information. We detail the logic of the heuristics, and with experimental results, we illustrate the behavior of the heuristics, and demonstrate their excellent performance

A new multistage lattice vector quantization with adaptive subband thresholding for image compression

Lattice vector quantization (LVQ) reduces coding complexity and computation due to its regular structure. A new multistage LVQ (MLVQ) using an adaptive subband thresholding technique is presented and applied to image compression. The technique concentrates on reducing the quantization error of the quantized vectors by "blowing out" the residual quantization errors with an LVQ scale factor. The significant coefficients of each subband are identified using an optimum adaptive thresholding scheme for each subband. A variable length coding procedure using Golomb codes is used to compress the codebook index which produces a very efficient and fast technique for entropy coding. Experimental results using the MLVQ are shown to be significantly better than JPEG 2000 and the recent VQ techniques for various test images