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Proceedings Paper

Ordered Kohonen vector quantization for very low rate interframe video coding
Author(s): Hui Liu
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Paper Abstract

The new interframe video coding algorithm is presented using the topological ordering property of a self-organizing vector quantization (VQ). This algorithm utilizes the Kohonen learning algorithm to train a super VQ codebook which transforms the statistical characteristics of the training motion-compensated frame difference video signals into a 2D topologically ordered array. A new finite state VQ (FSVQ) scheme is proposed to make use of the correspondence between the image interblock correlation and the geometrical closeness of the codevectors in the ordered super codebook. A small state codebook is dynamically predicted purely based on the positions of codevectors used to encode the neighboring image blocks in the current frame as well as in the previous frame. Thus, this new FSVQ significantly reduces the computational complexity at the encoder and preserves the advantages of a simple VQ decoder. The experimental results show that the prediction accuracy ranges from 70 to 95%, depending on the moving information in a frame. It achieves an average bit rate of 0.082 bits per pixel with high image quality (37.86 dB) for the standard test image sequence `Miss America.' This algorithm is amenable to VLSI implementation because of its simple design, low memory requirement, and low computational complexity.

Paper Details

Date Published: 17 April 1995
PDF: 10 pages
Proc. SPIE 2419, Digital Video Compression: Algorithms and Technologies 1995, (17 April 1995); doi: 10.1117/12.206395
Show Author Affiliations
Hui Liu, QMS, Inc. (United States)

Published in SPIE Proceedings Vol. 2419:
Digital Video Compression: Algorithms and Technologies 1995
Arturo A. Rodriguez; Robert J. Safranek; Edward J. Delp, Editor(s)

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