Randomized Quantization and Source Coding With Constrained Output Distribution

Network Source Coding Using Entropy Constrained Dithered Quantization

Adaptive Transform Coding as Constrained Vector Quantization

We investigate the application of local Principal Com ponent Analysis PCA to transform coding for xed rate image compression Local PCA transform coding adapts to di erences in correlations between signal components by partitioning the signal space into regions and compressing signal vectors in each region with a separate local transform coder Previous researchers optimize the signal space parti...

Quantization of Speech Features: Source Coding

In this chapter, we describe various schemes for quantizing speech features to be used in distributed speech recognition (DSR) systems. We have analyzed the statistical properties of MFCCs that are most relevant to quantization, namely the correlation and probability density function shape, in order to determine the type of quantization scheme that would be most suitable for quantizing them eff...

Source-Channel Coding Strategies: Tandem Coding vs. Channel-Optimized Quantization

Two common source-channel coding strategies, tandem coding and channel-optimized quantization, are compared on the basis of distortion vs. complexity by analyzing specific representatives of each when transmitting analog data samples across a binary symmetric channel. Transform coding is used in each strategy, and Reed-Solomon codes are added to form the tandem code. For each strategy, formulas...

Entropy-constrained gradient-match vector quantization for image coding

Side-match VQ(SMVQ) is a well-known class of FSVQ used for low-bit rate image/video coding. It exploits the spatial correlation between the neighboring blocks to select several codewords that are very close to the encoding block from the master codebook. But if the block boundary is in the region edge arca. the spatial correlations are not high and the SMVQ can’t sclcct proper codewords to enco...