模糊网格矢量量化图像

T. Haddad, A. Yongaçoğlu
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引用次数: 1

摘要

本文介绍了一种新的网格矢量量化系统(TVQ)码本搜索算法。该算法是在符号- map信道解码算法的基础上开发的,该算法对数据压缩进行了改进,以提供与软失真相关的可靠性信息。根据率失真理论,利用软信息推导出一种码本搜索算法,该算法能够解决与LBG算法相关的问题。该算法遵循软关联规则是模糊的,但在向全局最小失真点下降的过程中是确定性的。虽然推导是一般的,但该算法在灰度图像上进行了测试,该图像提供了一个非凸方误差失真表面。如仿真部分所示,新算法提供了更低能量的码本(/spl sim/0.8 dB增益),同时对使用短训练图像序列的初始码本的敏感性明显降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy trellis vector quantization of images
This paper introduces a new codebook search algorithm for trellis vector quantization systems (TVQ). The development of the new algorithm is based on the symbol-MAP channel decoding algorithm, which is modified for data compression to deliver soft distortion-related reliability information. Following a rate-distortion theoretic approach, the soft information is used to derive a codebook search algorithm that is capable of solving the problems associated with the LBG algorithm. The derived algorithm is fuzzy in the sense that it follows a soft association rule, however, it is deterministic in the descent towards the global minimum distortion point. Although the derivation is general, the algorithm is tested using gray-scale images, which provide a nonconvex square-error distortion surface. As shown in the simulation section, the new algorithm provides lower energy codebooks (/spl sim/0.8 dB gain), while being significantly less sensitive to initial codebooks using short training image sequences.
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