Entropy-constrained predictive residual vector quantization

Syed A. Rizvi; Lin-Cheng Wang; Nasser M. Nasrabadi

doi:10.1117/12.228223

8 December 1995 Entropy-constrained predictive residual vector quantization

Syed A. Rizvi, Lin-Cheng Wang, Nasser M. Nasrabadi

Proceedings Volume 2605, Coding and Signal Processing for Information Storage; (1995) https://doi.org/10.1117/12.228223
Event: Photonics East '95, 1995, Philadelphia, PA, United States

Abstract

A major problem with a VQ based image compression scheme is its codebook search complexity. Recently, a new VQ scheme called predictive residual vector quantizer (PRVQ) was proposed which has a performance very close to that of the predictive vector quantizer (PVQ) with very low search complexity. This paper presents a new variable-rate VQ scheme called entropy-constrained PRVQ (EC-PRVQ), which is designed by imposing a constraint on the output entropy of the PRVQ. We emphasized the design of EC-PRVQ for bit rates ranging from 0.2 bpp to 1.00 bpp. This corresponds to the compression ratios of 8 through 40, which are likely to be used by most of the real life applications permitting lossy compression. The proposed EC-PRVQ is found to give a good rate-distortion performance and clearly outperforms the state-of-the-art image compression algorithms developed by Joint Photographic Experts Group (JPEG). The robustness of EC-PRVQ is demonstrated by encoding several test images taken from outside the training data. EC-PRVQ not only gives better performance than JPEG, at a manageable encoder complexity, but also retains the inherent simplicity of VQ decoder.

Citation Download Citation

Syed A. Rizvi, Lin-Cheng Wang, and Nasser M. Nasrabadi "Entropy-constrained predictive residual vector quantization", Proc. SPIE 2605, Coding and Signal Processing for Information Storage, (8 December 1995); https://doi.org/10.1117/12.228223

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KEYWORDS

Distortion

Computer programming

Image compression

Quantization

Algorithm development

Image quality

Neural networks

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