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1 June 1990 Stationary vector quantization approach for image coding
Rong-Hauh Ju, I-Chang Jou, Mu-King Tsay, Bor-Shenn Jeng, Tsann-Shyong Liu, Kou-Sou Kan
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Proceedings Volume 1244, Image Processing Algorithms and Techniques; (1990) https://doi.org/10.1117/12.19511
Event: Electronic Imaging: Advanced Devices and Systems, 1990, Santa Clara, CA, United States
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.
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Rong-Hauh Ju, I-Chang Jou, Mu-King Tsay, Bor-Shenn Jeng, Tsann-Shyong Liu, and Kou-Sou Kan "Stationary vector quantization approach for image coding", Proc. SPIE 1244, Image Processing Algorithms and Techniques, (1 June 1990); https://doi.org/10.1117/12.19511
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KEYWORDS
Image compression
Distortion
Image quality
Quantization
Stochastic processes
Signal generators
Computer programming
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