%0 Journal Article
%T Sparse Representation through Multi-Resolution Transform for Image Coding
%A Dr. P. Arockia Jansi Rani
%J International Journal of Computer Science and Network
%D 2013
%I IJCSN publisher
%X Having a compact basis is useful both for compression and fordesigning efficient numerical algorithms. In this paper, a newimage coding scheme using a multi-resolution transform knownas Bandelet Transform that provides an optimally compact basisfor images by exploring their directional characteristics isproposed. As this process results in a sparse representation,Zero Vector Pruning is applied in-order to extract the non-zerocoefficients. Further the geometric interpixel redundanciespresent in the transformed coefficients are removed. Thepsycho-visual redundancies are removed using simple VectorQuantization (VQ) process. Finally, Huffman encoder is used toencode the significant coefficients. The proposed compressionmethod beats the standard wavelet based algorithms in terms ofmean-square-error (MSE) and visual quality, especially in thelow-rate compression regime. A gain in the bit-rate of about0.81 bpp over the wavelet based algorithms is achieved yieldingsimilar quality factor.
%K Bandelet Transform
%K Multi- Resolution
%K Psycho- Visual Redundancy
%K Vector Quantization
%K Zero Vector Pruning
%U http://ijcsn.org/IJCSN-2013/2-1/IJCSN-2013-2-1-69.pdf