Image coding using entropy-constrained residual vector quantization

The residual vector quantization (RVQ) structure is exploited to produce a variable length codeword RVQ. Necessary conditions for the optimality of this RVQ are presented, and a new entropy-constrained RVQ (ECRVQ) design algorithm is shown to be very effective in designing RVQ codebooks over a wide range of bit rates and vector sizes. The new EC-RVQ has several important advantages. It can outperform entropy-constrained VQ (ECVQ) in terms of peak signal-to-noise ratio (PSNR), memory, and computation requirements. It can also be used to design high rate codebooks and codebooks with relatively large vector sizes. Experimental results indicate that when the new EC-RVQ is applied to image coding, very high quality is achieved at relatively low bit rates

title = { Approximation, Sampling and Voting in Hybrid Computing Systems},

This report addresses the question of extending the usual approximation and sampling theory of continuous signals and systems to those encompassing discontinuities, such as found in modern distributed control systems. We provide a topological framework dealing with continuous, discrete and mixed systems in a uniform manner. We show how this theoretical framework can be used for appoximating systems, sampling and voting on hybrid signals in critical real-time systems