A Distributed Computationally Aware Quantizer Design via Hyper Binning

Multiple Description Quantizer Design Using A Channel Optimized Quantizer Approach

This paper extends the channel optimized quantization scheme of Farvardin and Vaishampayan [1] to two parallel channels. The extended multiple-channel optimized quantizer design (MCOQD) framework is applied to discrete memoryless channels with erasures. The resultant MCOQD subsumes the multiple description scalar quantizer (MDSQ) design of Vaishampayan [2]. While MDSQ is suited to only “on-off”...

On Achievability via Random Binning

In [1], the authors present a novel tool to establish achievability results in network information theoretic problems. The main idea is to study a set of discrete memoryless correlated sources, and the ensuing statistics of the bin indices resulting from random binning of the sources. Sufficient conditions are established on rates of random binning such that these indices are mutually independe...

A Power Aware Cache and Register File Design Space Exploration

Case Study: Empirical Quantizer Design

Now that we have safely made our way through the combinatorial forests of Vapnik–Chervonenkis classes, we will look at an interesting application of the VC theory to a problem in communications engineering: empirical design of vector quantizers. Vector quantization is a technique for lossy data compression (or source coding), so we will first review, at a very brisk pace, the basics of source c...

Vector Quantizer Design Using Genetic Algorithms

The design of vector quantizers (VQs) that yield minimal distortion is one of the most challenging problems in source coding. The problem of VQ design is to find a codebook that gives the least overall distortion (or equivalently, the largest signal-to-noise ratio (SNR)) for a given set of input vectors. This problem is known to be difficult as there are no known closed-form solutions. The Gene...