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Proceedings Paper

Stationary vector quantization approach for image coding
Author(s): Rong-Hauh Ju; I-Chang Jou; Mu-King Tsay; Bor-Shenn Jeng; Tsann-Shyong Liu; Kou-Sou Kan
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Paper Abstract

Vector Quantizer encoding process is based on a codebook designed to minimize some performance criteria. The codebook is formed through the use of long training sequences, which are considered to be in the same class as the source data to be encoded. With the penalty of a long training, the approach is successfully used to encode the speech and image signals. In this paper, we describe a model which generates image signals suitable for coding at a stationary codebook. In this model, the image signal is represented by a zero mean Gaussian stochastic process. Each block of n*n samples of a stochastic process is encoded into one out of M randomly generated Gaussion sequence of length n*n by minimizing the signal to noise ratio. We find out that the model can achieve an acceptable quality of coded image at low bit rates and low complexity.

Paper Details

Date Published: 1 June 1990
PDF: 8 pages
Proc. SPIE 1244, Image Processing Algorithms and Techniques, (1 June 1990); doi: 10.1117/12.19511
Show Author Affiliations
Rong-Hauh Ju, National Central Univ. (Taiwan)
I-Chang Jou, Ministry of Communications (Taiwan)
Mu-King Tsay, National Central Univ. (Taiwan)
Bor-Shenn Jeng, Ministry of Communications (Taiwan)
Tsann-Shyong Liu, Ministry of Communications (Taiwan)
Kou-Sou Kan, Ministry of Communications (Taiwan)

Published in SPIE Proceedings Vol. 1244:
Image Processing Algorithms and Techniques
Robert J. Moorhead; Keith S. Pennington, Editor(s)

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