JOURNAL ARTICLE

<title>Variable-rate predictive residual vector quantization</title>

Syed A. RizviNasser M. NasrabadiLincheng Wang

Year: 1995 Journal:   Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE Vol: 2501 Pages: 500-511   Publisher: SPIE

Abstract

A major problem with a VQ based image compression scheme is its codebook search complexity. Recently a Predictive Residual Vector Quantizer (PRVQ) was proposed in Ref. 8. This scheme has a very low search complexity and its performance is very close to that of the Predictive Vector Quantizer (PVQ). This paper presents a new VQ scheme called Variable-Rate PRVQ (VR-PRVQ) which is designed by imposing a constraint on the output entropy of the PRVQ. The proposed VR-PRVQ is found to give an excellent rate-distortion performance and clearly outperforms the state-of-the-art image compression algorithm developed by Joint Photographic Experts Group (JPEG).

Keywords:
Vector quantization Codebook Residual Algorithm Computer science Computational complexity theory Data compression Artificial intelligence Image compression Entropy (arrow of time) Linde–Buzo–Gray algorithm Rate–distortion theory Quantization (signal processing) JPEG Pattern recognition (psychology) Mathematics Image (mathematics) Image processing

Metrics

1
Cited By
0.47
FWCI (Field Weighted Citation Impact)
0
Refs
0.64
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
Advanced Image Processing Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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