Pyramid vector quantization (PVQ) uses the lattice points of a pyramidal shape in multidimensional space as the quantizer codebook. It is a fixed-rate quantization technique that can be used for the compression of Laplacian-like sources arising from transform and subband image coding, where its performance approaches the optimal entropy-coded scalar quantizer without the necessity of variable length codes. In this paper, we investigate the use of PVQ for compressed image transmission over noisy channels, where the fixed-rate quantization reduces the susceptibility to bit-error corruption. We propose a new method of deriving the indices of the lattice points of the multidimensional pyramid and describe how these techniques can also improve the channel noise immunity of general symmetric lattice quantizers. Our new indexing scheme improves channel robustness by up to 3 dB over previous indexing methods, and can be performed with similar computational cost. The final fixed-rate coding algorithm surpasses the performance of typical Joint Photographic Experts Group (JPEG) implementations and exhibits much greater error resilience.

Our present ability to work with video has been confined to a wired environment, requiring both the video encoder and decoder to be physically connected to a power supply and a wired communication link. This paper describes an integrated approach to the design of a portable video-on-demand system capable of delivering high-quality image and video data in a wireless communication environment. The discussion will focus on both the algorithm and circuit design techniques developed for implementing a low-power video compression/decompression system at power levels that are two orders of magnitude below existing solutions. This low-power video compression system not only provides a compression efficiency similar to industry standards, but also maintains a high degree of error tolerance to guard against transmission errors often encountered in wireless communication. The required power reduction can best be attained through reformulating compression algorithms for energy conservation. We developed an intra-frame compression algorithm that requires minimal computation energy in its hardware implementations.>

This paper presents an improved algorithm using pyramid vector quantization with subband decomposed images. Specifically, the use of large vector dimensions and different dimensions for each subband yields significant improvement over previously reported results. Simulations reveal compression performance comparable to JPEG using a purely fixed-rate code, which has less hardware complexity and greater error resiliency. A comparison between product and inner pyramid VQ using statistical analysis of subband data and simulations demonstrates that product pyramid VQ is better suited for subband coding.>