使用一种增强的LBG算法来减少矢量量化中的码本误差

Jörg Haber, H. Seidel
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引用次数: 8

摘要

提出了对著名的LBG (Linde, Buzo和Gray, 1980)算法的改进,用于矢量量化中码本的生成。我们的算法,我们称之为ILBG(迭代LBG)算法,在典型应用中显著降低了LBG算法的码本误差。在我们的实验中,我们能够在几个额外的迭代步骤中实现高达75%的码本错误减少。在有损图像压缩的情况下,这种误差的减少反过来导致峰值信噪比(PSNR)增加2-3 dB。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using an enhanced LBG algorithm to reduce the codebook error in vector quantization
Presents a modification of the well-known LBG (Linde, Buzo and Gray, 1980) algorithm for the generation of codebooks in vector quantization. Our algorithm, which we call the ILBG (iterated LBG) algorithm, reduces the codebook error of the LBG algorithm drastically in typical applications. In our experiments, we were able to achieve up to a 75% reduction of the codebook error in only a few additional iteration steps. In the context of lossy image compression, this error reduction in turn leads to an increase of 2-3 dB in the peak signal-to-noise ratio (PSNR).
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