A Novel Approach for Image Compression using Matching Pursuit Signal Approximation and Simulated Annealing

Signal approximation using a linear combination of basis from an overcomplete dictionary has been proven to be an NP-Hard problem. By selecting a smaller number of basis than the span of the signal, we achieve lossy compression in exchange for a small reconstruction error. Several algorithms have been researched that reduce the complexity of the selection problem, sacrificing the optimality of the solution. The matching pursuit (MP) algorithm has been used for signal approximation for over a decade. Many variations have been proposed and implemented to enhance the performance of the algorithm. However, its greedy nature renders it sub-optimal. An enhancement for the MP algorithm is proposed that uses concepts from simulated annealing in improving the performance in terms of compression ratio and reconstructed quality. The algorithm is then applied to image signals. Results show superior compression to image compression standards for the same quality.