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Vipula Singh Navin Rajpal K. Srikanta Murthy 《Journal of the Indian Society of Remote Sensing》2009,37(2):185-199
Image compression forms the backbone for several applications such as storage of images in a database, picture archiving,
TV and facsimile transmission, and video conferencing. Compression of images involves taking advantage of the redundancy in
the data present within an image. This work evaluates the performance of an image compression system based on fuzzy vector
quantization, wavelet-based sub band decomposition and neural network. The vector quantization is often used when high compression
ratios are required. The implementation consists of three steps: first, the image is decomposed into a set of sub bands with
different resolutions corresponding to different frequency bands. Different quantization and coding schemes are used for different
sub bands based on their statistical properties. In the second step, wavelet coefficients corresponding to the 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 was used as input to fuzzy vector quantizer. Image
quality was compared objectively using mean squared error and peak signal to noise ratio along with the visual appearance.
The simulation results show clear performance improvement with respect to decoded picture quality when compared with other
image compression techniques (Liu, 2005; Premaraju, 1996). 相似文献
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