Simple, fast codebook training algorithm by entropy sequence for vector quantization

Chao-yang Pang; Shaowen Yao; Zhang Qi; Shi-xin Sun; Jingde Liu

doi:10.1117/12.442923

26 September 2001 Simple, fast codebook training algorithm by entropy sequence for vector quantization

Chao-yang Pang, Shaowen Yao, Zhang Qi, Shi-xin Sun, Jingde Liu

Proceedings Volume 4551, Image Compression and Encryption Technologies; (2001) https://doi.org/10.1117/12.442923
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

Abstract

The traditional training algorithm for vector quantization such as the LBG algorithm uses the convergence of distortion sequence as the condition of the end of algorithm. We presented a novel training algorithm for vector quantization in this paper. The convergence of the entropy sequence of each region sequence is employed as the condition of the end of the algorithm. Compared with the famous LBG algorithm, it is simple, fast and easy to be comprehended and controlled. We test the performance of the algorithm by typical test image Lena and Barb. The result shows that the PSNR difference between the algorithm and LBG is less than 0.1dB, but the running time of it is at most one second of LBG.

Citation Download Citation

Chao-yang Pang, Shaowen Yao, Zhang Qi, Shi-xin Sun, and Jingde Liu "Simple, fast codebook training algorithm by entropy sequence for vector quantization", Proc. SPIE 4551, Image Compression and Encryption Technologies, (26 September 2001); https://doi.org/10.1117/12.442923

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