Ph. D. Thesis Abstract

Optimization techniques for adaptive quantization of image and video under delay constraintsAdaptive quantization without side information
A. Ortega
M. Vetterli
June 1994
Dept. of Electrical Engineering, Columbia University, NY, NY

Traditionally, rate-distortion (R-D) theory has been concerned with providing bounds on the optimal performance for various classes of coding algorithms and sources. In this thesis we depart from that approach in two ways. First, our objective are operational R-D results, i.e. we study algorithms that can find the optimal solution for a given coder configuration and known inputs, without relying on modeling either the encoder or the source. Second, we seek to explore explicitly other parameters that determine the achievable R-D performance, namely, the encoding delay and complexity, and the memory at the encoder.

We compute the optimal solution even if it requires too much complexity, memory or delay to be considered in a practical setting. Optimal schemes serve as a benchmark and can also be the basis for heuristic methods which provide slightly suboptimal but more efficient performance. More specifically we study the following topics:

(i) Optimal buffer constrained quantization. We find optimal solutions for the buffer control problem in a deterministic framework by assuming a long encoding delay. Our solution, based on dynamic programming, also leads us to short delay, lower complexity heuristics. (Chapter 2).

(ii) Rate control and policing constraints for video transmission over ATM networks. We study the problem of optimizing the source quality as in (i), while taking into account network considerations. (Chapter 3)

(iii) Optimization of dependent quantization environments. Optimal bit allocation results are presented for dependent quantization schemes (e.g. DPCM, predictive motion compensated video coding, MPEG). (Chapter 4)

(iv) Rate-delay trade-offs in a multiresolution image database system. We study how the bit allocation in a multiresolution coding system can be chosen so as to minimize the end-to-end query delay in browsing through a set of images. (Chapter 5).

(v) Adaptive quantization without side information. We propose a backward adaptive quantization algorithm where the input distribution is ``learned'' from past quantized samples. This allows adaptation to sources with unknown or time-varying input distribution. (Chapter 6).

Thesis (Postscript format)

Thesis Index

Index, List of Figures/Tables, Title, Acknowledgements

Chapter 1: Introduction and Motivation

Chapter 2: Optimal Buffer Constrained Independent Bit Allocation

Chapter 3: Rate Constraints for Packet Video Transmission Based on Joint Source/Network Criteria

Chapter 4: Optimal Bit Allocation for Dependent Quantization

Chapter 5: Modeling and Optimization of a Multiresolution Image Retrieval System

Chapter 6: Adaptive Quantization Without Side Information

Note: References are included at the end of Chapter 6

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Last modified: Mon May 21 10:54:45 PDT 2001