Adaptive perceptual quantization using a neural network for video coding

Byung-Sun Choi; H. D. Cho; Kyoung Won Lim; Kangwook Chun; Jong Beom Ra

doi:10.1117/12.185897

16 September 1994 Adaptive perceptual quantization using a neural network for video coding

Byung-Sun Choi, H. D. Cho, Kyoung Won Lim, Kangwook Chun, Jong Beom Ra

Proceedings Volume 2308, Visual Communications and Image Processing '94; (1994) https://doi.org/10.1117/12.185897
Event: Visual Communications and Image Processing '94, 1994, Chicago, IL, United States

Abstract

This paper describes a new adaptive quantization algorithm for video sequence coding, which can reflect perceptual characteristics of macroblocks by using a neural network classifier. Multilayer perceptron is adopted as a neural network structure, and the feature parameters and target classes of training macroblocks are prepared for learning. The coding performance based on the neural network classifier is investigated by computer simulation. In comparison with both the non-adaptive quantization scheme and the adaptive one in the MPEG-2 TM5, the proposed scheme is proven to enhance perceptual quality in video coding.

Citation Download Citation

Byung-Sun Choi, H. D. Cho, Kyoung Won Lim, Kangwook Chun, and Jong Beom Ra "Adaptive perceptual quantization using a neural network for video coding", Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); https://doi.org/10.1117/12.185897

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