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Proceedings Paper

Adaptive perceptual quantization using a neural network for video coding
Author(s): Byung-Sun Choi; H. D. Cho; Kyoung Won Lim; Kangwook Chun; Jong Beom Ra
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Paper 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.

Paper Details

Date Published: 16 September 1994
PDF: 10 pages
Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); doi: 10.1117/12.185897
Show Author Affiliations
Byung-Sun Choi, Korea Advanced Institute of Science and Technology (South Korea)
H. D. Cho, Korea Advanced Institute of Science and Technology (South Korea)
Kyoung Won Lim, Korea Advanced Institute of Science and Technology (South Korea)
Kangwook Chun, Korea Advanced Institute of Science and Technology (South Korea)
Jong Beom Ra, Korea Advanced Institute of Science and Technology (South Korea)


Published in SPIE Proceedings Vol. 2308:
Visual Communications and Image Processing '94
Aggelos K. Katsaggelos, Editor(s)

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