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Journal of Electronic Imaging

Multiscale video compression using adaptive finite-state vector quantization
Author(s): Heesung Kwon; Mahesh Venkatraman; Nasser M. Nasrabadi
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Paper Abstract

We investigate the use of vector quantizers (VQs) with memory to encode image sequences. A multiscale video coding technique using adaptive finite-state vector quantization (FSVQ) is presented. In this technique, a small codebook (subcodebook) is generated for each input vector from a much larger codebook (supercodebook) by the selection (through a reordering procedure) of a set of appropriate codevectors that is the best representative of the input vector. Therefore, the subcodebook dynamically adapts to the characteristics of the motion-compensated frame difference signal. Several reordering procedures are introduced, and their performance is evaluated. In adaptive FSVQ, two different methods, predefined thresholding and rate-distortion cost optimization, are used to decide between the supercodebook and subcodebook for encoding a given input vector. A cache-based vector quantizer, a form of adaptive FSVQ, is also presented for very-low-bit-rate video coding. An efficient bit-allocation strategy using quadtree decomposition is used with the cache-based VQ to compress the video signal. The proposed video codec outperforms H.263 in terms of the peak signal-to-noise ratio and perceptual quality at very low bit rates, ranging from 5 to 20 kbps. The picture quality of the proposed video codec is a significant improvement over previous codecs, in terms of annoying distortions (blocking artifacts and mosquito noises), and is comparable to that of recently developed wavelet-based video codecs. This similarity in picture quality can be explained by the fact that the proposed video codec uses multiscale segmentation and subsequent variable-rate coding, which are conceptually similar to wavelet-based coding techniques. The simplicity of the encoder and decoder of the proposed codec makes it more suitable than wavelet-based coding for real-time, very-low-bit-rate video applications.

Paper Details

Date Published: 1 October 1998
PDF: 15 pages
J. Electron. Imaging. 7(4) doi: 10.1117/1.482660
Published in: Journal of Electronic Imaging Volume 7, Issue 4
Show Author Affiliations
Heesung Kwon, SUNY/Buffalo (United States)
Mahesh Venkatraman, SUNY/Buffalo (United States)
Nasser M. Nasrabadi, U.S. Army Research Lab. (United States)


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