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An algorithmic approach based on Fuzzy Vector Quantization and Wavelet decomposition for Image Compression

机译：一种基于模糊矢量量化和图像压缩小波分解的算法方法

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Due to limitation in storage and transmission capacity, image compression has become inevitable for wide class of applications video conferencing, interactive education and numerous other areas. The objective of the paper is to evaluate the performance of an image compression system based on fuzzy vector quantization, wavelet sub band decomposition and neural network. Vector quantization is often used when high compression ratios are required. The implementation consists of three steps. First, image is decomposed into a set of sub bands with different resolution corresponding to different frequency bands. Different quantization and coding schemes are used for different sub bands based on their statistical properties. At the second step, the wavelet coefficients corresponding to lowest frequency band are compressed by differential pulse code modulation (DPCM) and the coefficients corresponding to higher frequency bands are compressed using neural network. Finally the result of the second step is used as input to fuzzy vector quantizer. Image quality is compared objectively using peak signal to noise ratio along with the visual appearance. The simulation results show clear performance improvement with respect to decoded picture quality as compared to other image compression techniques [29-30].
机译：由于存储和传输容量的限制，图像压缩对于广泛的应用程序视频会议，互动教育和许多其他地区都变得不可避免。本文的目的是评估基于模糊矢量量化，小波子带分解和神经网络的图像压缩系统的性能。当需要高压缩比时通常使用矢量量化。实施包括三个步骤。首先，图像被分解成具有与不同频带对应的不同分辨率的一组子带。基于其统计属性，不同的量化和编码方案用于不同的子带。在第二步中，对应于最低频带的小波系数通过差分脉冲码调制（DPCM）来压缩，并且使用神经网络压缩与较高频带对应的系数。最后，第二步的结果用作模糊矢量量化器的输入。使用峰值信号与视觉外观相比，使用峰值信号与视觉出现进行客观地进行比较。相比于其他的图像压缩技术[29-30]仿真结果表明相对于经解码的画面质量明确的性能提高。

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