Sparse Representation through Multi Sparse Representation through Multi Sparse Representation through Multi Sparse Representation through Multi--Resolution esolution esolution Transform for Image Coding Transform for Image Coding Transform for Image Coding Transform for Image Coding
P. Arockia Jansi Rani · 2013
Having a compact basis is useful both for compression and for designing efficient numerical algorithms. In this paper, a new image coding scheme using a multi-resolution transform known as Bandelet Transform that provides an optimally compact basis for images by exploring their directional characteristics is proposed. As this process results in a sparse representation, Zero Vector Pruning is applied in-order to extract the non-zero coefficients. Further the geometric interpixel redundancies present in the transformed coefficients are removed. The psycho-visual redundancies are removed using simple Vector Quantization (VQ) process. Finally, Huffman encoder is used to encode the significant coefficients. The proposed compression method beats the standard wavelet based algorithms in terms of mean-square-error (MSE) and visual quality, especially in the low-rate compression regime. A gain in the bit-rate of about 0.81 bpp over the wavelet based algorithms is achieved yielding similar quality factor.