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Title: Image compression based on finite-state vector quantization

Abstract

Compression of imagery is becoming increasingly important in digital transmission and storage. Recently, vector quantization (VQ) has emerged as a popular tool in image-compression algorithms. VQ is a powerful pattern-matching technique which jointly encodes blocks (or vectors) of input samples or features, using a codebook of prototype patterns. A technique of enchancing the power of VQ is to use information obtained from previously encoded vectors in the encoding of each successive input vector. In this work, the author studies new algorithms based on FSVQ for monochrome image compression at rates below 0.5 bpp. He introduces a novel formulation of the state and state-transition rule which uses a perceptually based classifier. Two types of finite-state VQ coders are studied: fixed-rate coders where each input block is encoded with the same number of bits, and variable-rate coders where the number of bits used to encode each block is varied according to the detail in the input image.

Authors:
Publication Date:
Research Org.:
California Univ., Santa Barbara, CA (USA)
OSTI Identifier:
5145770
Resource Type:
Thesis/Dissertation
Resource Relation:
Other Information: Thesis (Ph. D.)
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; DIGITAL SYSTEMS; IMAGE PROCESSING; VECTOR PROCESSING; QUANTIZATION; ALGORITHMS; MATHEMATICAL LOGIC; PROCESSING; PROGRAMMING; 990200* - Mathematics & Computers

Citation Formats

Aravind, R. Image compression based on finite-state vector quantization. United States: N. p., 1988. Web.
Aravind, R. Image compression based on finite-state vector quantization. United States.
Aravind, R. Fri . "Image compression based on finite-state vector quantization". United States.
@article{osti_5145770,
title = {Image compression based on finite-state vector quantization},
author = {Aravind, R},
abstractNote = {Compression of imagery is becoming increasingly important in digital transmission and storage. Recently, vector quantization (VQ) has emerged as a popular tool in image-compression algorithms. VQ is a powerful pattern-matching technique which jointly encodes blocks (or vectors) of input samples or features, using a codebook of prototype patterns. A technique of enchancing the power of VQ is to use information obtained from previously encoded vectors in the encoding of each successive input vector. In this work, the author studies new algorithms based on FSVQ for monochrome image compression at rates below 0.5 bpp. He introduces a novel formulation of the state and state-transition rule which uses a perceptually based classifier. Two types of finite-state VQ coders are studied: fixed-rate coders where each input block is encoded with the same number of bits, and variable-rate coders where the number of bits used to encode each block is varied according to the detail in the input image.},
doi = {},
url = {https://www.osti.gov/biblio/5145770}, journal = {},
number = ,
volume = ,
place = {United States},
year = {1988},
month = {1}
}

Thesis/Dissertation:
Other availability
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