Now showing items 1-6 of 6
Finite-State Vector Quantization for Noisy Channels
Under noiseless channel conditions and for sources with memory, finite-state vector quantizers (FSVQs) exhibit improvements over memoryless vector quantizers. It is shown, however, that in the presence of channel noise, ...
A Study of the Reflection Symmetric Residual Quantizer and Application to Speech Coding
The performance of the reflection symmetric residual quantizer (rRQ) on various types of sources is tested and certain conclusions about its capabilities and limitations are drawn. rRQ is then used to design several ...
Design and Performance Evaluation of a Class of Finite-State Vector Quantizers
The finite-state vector quantizer (FSVQ), introduced by Foster, Dunham and Gray, is a finite-state machine that can be viewed as a collection of memoryless full-searched vector quantizers, where each input vector is encoded ...
Quantization Over Discrete Noisy Channels Under Complexity Constraints
A fundamental problem in communication is the transmission of an information source across a communication channel. According to Shannon's separation principle, this problem can be separated (without loss of optimality) ...
Discrete Representation of Signals from Infinite Dimensional Hilbert Spaces with Applications to Noise Suppression and Compression
Addressed in this thesis is the issue of representing signals from infinite dimensional Hilbert spaces in a discrete form. The discrete representations which are studied come from the irregular samples of a signal dependent ...
Speech Coding over Noisy Channels
This chapter contains a discussion of quantization over noisy channels. The effects of channel noise on the performance of vector quantizers are discussed and algorithms for the design of noisy-channel vector quantizers ...