使用可变块大小的有限状态分层表查找矢量量化的自适应编码

S. Mehrotra, N. Chaddha, R. Gray
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引用次数: 0

摘要

提出了一种基于内存的自适应矢量量化算法。利用编码后相邻像素块之间的内存,可以利用相邻像素块之间的相关性来减少冗余。我们使用有限状态矢量量化来提供存储器。为了利用图像的非平稳性进一步提高性能,我们在编码中使用可变块大小。这是通过使用四叉树数据结构来表示基于可变块大小的编码来完成的。为了降低编码复杂度,我们使用分层表查找方案来代替所有的全搜索编码器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive coding using finite state hierarchical table lookup vector quantization with variable block sizes
We present an algorithm for performing adaptive vector quantization with memory. By using the memory between adjacent blocks which are encoded, we can take advantage of the correlation between adjacent blocks of pixels to reduce the redundancy. We use finite state vector quantization to provide the memory. To further improve the performance by exploiting nonstationarities in the image, we use variable block sizes in the encoding. This is done by using a quadtree data structure to represent an encoding based on variable block sizes. To reduce the encoding complexity, hierarchical table lookup schemes are used to replace all the full search encoders.
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