基于自适应小波包和多阶段矢量量化的图像压缩

S. Esakkirajan, T. Veerakumar, N. Malmurugan, P. Navaneethan
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引用次数: 4

摘要

提出了一种基于自适应小波包和多级矢量量化的图像编码方法。小波包是小波变换的推广,能够提供任意频率分辨率来满足信号的频谱特性。图像属性、滤波器和代价函数是选择小波包基常用的三个主要因素。本文通过奇异值分解选择最优基。选择最佳树后,采用多级矢量量化方法对最佳树的系数进行量化。实验结果表明,小波包变换与二进小波变换相比具有一致的改进效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Compression using Adaptive Wavelet Packet and Multistage Vector Quantization
This paper presents a new image coding technique using adaptive wavelet packet and multistage vector quantization. Wavelet packets are generalization of wavelet transform, capable of providing arbitrary frequency resolution to meet signal's spectral behavior. Image properties, filter and cost function are the three prime factors which are commonly used to select wavelet packet basis. In this paper, the best basis is selected through singular value decomposition. After selecting the best tree, the coefficients of the best tree are quantized using multistage vector quantization. Experimental results show that wavelet packet transform brings consistent improvement over dyadic wavelet transform.
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