Low rate image coding using vector quantization

Citation

Makur, Anamitra (1990) Low rate image coding using vector quantization. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/GEP0-NG36. https://resolver.caltech.edu/CaltechETD:etd-02282007-110244

Abstract

This thesis deals with the development and analysis of a computationally simple vector quantization image compression system for coding monochrome images at low bit rate. Vector quantization has been known to be an effective compression scheme when a low bit rate is desirable, but the intensive computation required in a vector quantization encoder has been a handicap in using it for low rate image coding. The present work shows that, without substantially increasing the coder complexity, it is indeed possible to achieve acceptable picture quality while attaining a high compression ratio. Several modifications to the conventional vector quantization coder are proposed in the thesis. These modifications are shown to offer better subjective quality when compared to the basic coder. Distributed blocks are used instead of spatial blocks to construct the input vectors. A class of input-dependent weighted distortion functions is used to incorporate psychovisual characteristics in the distortion measure. Computationally simple filtering techniques are applied to further improve the decoded image quality. Finally, unique designs of the vector quantization coder using electronic neural networks are described, so that the coding delay is reduced considerably. Except for the basics of the vector quantization described in the first chapter, each chapter is independent from the others because each chapter deals with a separate aspect of the coder. Therefore, each chapter beyond the first can be read separately.

Thesis Files