Hui D, Lyons D F, Neuhoff D L
Dept. of Electr. Eng. and Comput. Sci., Michigan Univ., Ann Arbor, MI 48109, USA.
IEEE Trans Image Process. 1998;7(4):477-95. doi: 10.1109/83.663492.
This paper introduces methods for reducing the table storage required for encoding and decoding with unstructured vector quantization (UVQ) or tree-structured vector quantization (TSVQ). Specifically, a low-storage secondary quantizer is used to compress the code vectors (and test vectors) of the primary quantizer. The relative advantages of uniform and nonuniform secondary quantization are investigated. A Linde-Buzo-Gray (LBG) like algorithm that optimizes the primary UVQ codebook for a given secondary codebook and another that jointly optimizes both primary and secondary codebooks are presented. In comparison to conventional methods, it is found that significant storage reduction is possible (typically a factor of two to three) with little loss of signal-to-noise ratio (SNR). Moreover, when reducing dimension is considered as another method of reducing storage, it is found that the best strategy is a combination of both. The method of secondary quantization is also applied to TSVQ to reduce the table storage required for both encoding and decoding. It is shown that by exploiting the correlation among the test vectors in the tree, both encoder and decoder storage can be significantly reduced with little loss of SNR--by a factor of about four (or two) relative to the conventional method of storing test vectors (or test hyperplanes).
本文介绍了使用非结构化矢量量化（UVQ）或树形结构矢量量化（TSVQ）进行编码和解码时减少表格存储所需的方法。具体而言，使用低存储量的二级量化器来压缩一级量化器的码矢量（以及测试矢量）。研究了均匀和非均匀二级量化的相对优势。提出了一种类似林德 - 布佐 - 格雷（LBG）的算法，该算法针对给定的二级码本优化一级UVQ码本，还提出了另一种同时优化一级和二级码本的算法。与传统方法相比，发现可以在信噪比（SNR）损失很小的情况下显著减少存储量（通常为两到三倍）。此外，当将降维视为减少存储的另一种方法时，发现最佳策略是两者结合。二级量化方法也应用于TSVQ，以减少编码和解码所需的表格存储。结果表明，通过利用树中测试矢量之间的相关性，相对于传统的存储测试矢量（或测试超平面）的方法，编码和解码器的存储量都可以在SNR损失很小的情况下显著减少——大约减少四倍（或两倍）。
