JOURNAL ARTICLE

Image sequence coding using adaptive finite-state vector quantization

Wen-Tsuen ChenRuey‐Feng ChangJia-Shung Wang

Year: 1992 Journal:   IEEE Transactions on Circuits and Systems for Video Technology Vol: 2 (1)Pages: 15-24   Publisher: Institute of Electrical and Electronics Engineers

Abstract

A coding algorithm must have the ability to adapt to changing image characteristics for image sequences. An adaptive finite-state vector quantization (FSVQ) in which the bit rate and the encoding time can be reduced is described. In order to improve the image quality and avoid producing a wrong state for an input vector, a threshold is used in FSVQ to decide whether to switch to a full searching VQ. The codebook is conditionally replenished according to a distortion threshold at a later time to reflect the local statistics of the current frame. After the codebook is replenished, one can quickly reconstruct the state codebooks of FSVQ using the state codebook selection algorithm. In the experiments, the improvement over the static SMVQ is up to 2.40 dB at nearly the same bit rate and the encoding time is only one-ninth the time required by the static SMVQ. Moreover, the improvement over the static VQ is up to 2.91 dB, and the encoding time is only three-fifths the time required by the static VQ for the image sequence 'Claire'.< >

Keywords:
Codebook Vector quantization Linde–Buzo–Gray algorithm Coding (social sciences) Algorithm Encoding (memory) Mathematics Sequence (biology) Quantization (signal processing) Computer science Pattern recognition (psychology) Artificial intelligence Biology

Metrics

17
Cited By
4.44
FWCI (Field Weighted Citation Impact)
23
Refs
0.95
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Topics

Advanced Data Compression Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Image and Signal Denoising Methods
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Image Enhancement Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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