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A novel search algorithm based on L/sub 2/-norm pyramid of codewords for fast vector quantization encoding

机译:一种基于L / Sub 2 / -norm金字塔的​​新型搜索算法,用于快速矢量量化编码

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Vector quantization for image compression requires expensive encoding time to find the closest codeword to the input vector. This paper presents a fast algorithm to speed up the closest codeword search process in vector quantization encoding. By using an appropriate topological structure of the codebook, we first derive a condition to eliminate unnecessary matching operations from the search procedure. Then, based on this elimination condition, a fast search algorithm is suggested. Simulation results show that with little preprocessing and memory cost, the proposed search algorithm significantly reduces the encoding complexity while maintaining the same encoding quality as that of the full search algorithm. It is also found that the proposed algorithm outperforms the existing search algorithms.
机译:图像压缩的矢量量化需要昂贵的编码时间来找到输入向量的最近的码字。本文提出了一种快速算法,可以加快向量量化编码中最近的码字搜索过程。通过使用码本的适当拓扑结构,我们首先导出了消除搜索过程中不必要的匹配操作的条件。然后,基于该消除条件,建议了快速搜索算法。仿真结果表明,随着预处理和内存成本很少,所提出的搜索算法显着降低了编码复杂度,同时保持与完整搜索算法相同的编码质量。还发现所提出的算法优于现有的搜索算法。

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