Performance Analysis of Iteration-Free Fractal Image Coding : Using Vector Quantization, Genetic Algorithm and Simulated Annealing Techniques

In fractal image compression the encoding step is computationally expensive. A large number of sequential searches through a list of domain from the domain pool are carried out while trying to find the best matches for image portions called ranges. Vector Quantization using Linde Buzo Gray algorithm helps in reducing the redundant domain blocks. The mean image constructed using the range blocks was used as the domain pool for the construction of the synthetic codebook. In order to reduce the time consumption the mean, edge strength and texture feature of each range block are determined and compared with the domains in the codebook in order to reduce the redundant domain blocks for each range block. Genetic Algorithm (GA) and Simulated Annealing (SA) are optimization techniques, hence it is proposed to use GA and SA for finding the best match of the domain block to the range block. It is observed that the proposed technique using GA achieves excellent performance in image quality and SA with reduction in computation time. The performance analysis of all the three proposed techniques in iteration-free fractal image compression is reported and discussed in detail in this book.