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 data present within an image. A fundamental goal of image compression is to reduce the bit rate for transmission and storage while maintaining an acceptable fidelity or image quality. Existing VQ algorithms however, suffer from a number of practical problems, e.g. codebook initialization, long search process, and getting trapped in local minima. This paper presents an adaptive vector quantization algorithm which uses a neuro-fuzzy clustering technique for optimizing the distortion measure. The fuzzy approach forms the basis for accurately optimizing each codevector by determining the fuzzy centroid of each class. In addition, a multiresolution wavelet decomposition scheme is adopted to make the image better suited for compression and to enable its progressive transmission.
Efficient image coding using multiresolution wavelet transform and vector quantization
1996-01-01
595013 byte
Conference paper
Electronic Resource
English
Efficient Image Coding Using Multiresolution Wavelet Transform and Vector Quantization
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