A Vector Quantization Technique for Image Compression using Modified Fuzzy Possibilistic C-Means with Weighted Mahalanobis Distance
Associate Professor of Computer Science, Erode Arts and Science College, Erode, Tamilnadu, India
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Image Compression is a technique for competently coding digital image by minimizing the number of bits needed for denoting image. Its main goal is to reduce the storage space and cut down the transmission cost and maintain good quality. In the present work, an image is decomposed into image blocks. In this scheme a low bit rate still image compression is performed by compressing the indices of vector quantization and residual codebook is generated by using Modified Fuzzy Possibilistic C-Means with Repulsion and Weighted Mahalanobis Distance. The residual code book is used in this proposed approach which eliminates the distortion in the reconstructed image and enhancing the quality of the image. Experimental results on standard images show that the proposed scheme can give a reconstructed image with high PSNR value than the existing image compression techniques.