Fractal Image Compression Scheme Using Biogeography Based Optimization on Color Images

The main problem with fractal image compression is that it takes a lot of computational time for searching blocks (domain block and range block) and then compares these blocks. There are many optimization techniques which are used to improve efficiency of fractal image compression. Some of these are particle swarm optimization, ant colony optimization and biogeography based optimization. In this paper the technique of biogeography based optimization (BBO) is applied for fractal image compression (FIC).With the help of this evolutionary algorithm effort is made to reduce the search complexity of matching between range block and domain block. The main drawback of FIC is that it involves more computational time due to global search. In order to improve the computational time and also the satisfactory quality of the decoded image, BBO algorithm is proposed. Investigational outcome show that the BBO is a better method than the traditional comprehensive search method in terms of encoding time. Results are calculated from wavelet based fractal image compression than BBO is applied over it to decrease the encode time and get better visual quality of image. In this type of compression first color image is split into three matrixes R, G, B and then compression is applied over individual matrix. These matrixes are then merged together after compression to reconstruct the compressed image. In this paper compression time (encoding time) of fractal image compression is reduced which is applied over color image.