Robust and Efficient ‘RGB’ based Fractal Image Compression: Flower Pollination based Optimization

Fractal image compression uses the property of selfsimilarity in an image and utilizes the partitioned iterated function system to encode it. Fractal image compression is attractive because of high compression ratio, fast decompression and multi-resolution properties. The main drawback of Fractal Image Compression is the high computational cost and is the poor retrieved image qualities. To overcome this drawback, we design a new algorithm which is based on Pollination Based Optimization which is used to classify the phantom, satellite and rural image dataset. Flower Pollination Based Optimization is nature inspired algorithm which decreases the search complexity of matching between range block and domain block. Also, the optimization technique has effectively reduced the encoding time while retaining the quality of the image. Peak signal to noise ratio, entropy, compression ratio and mean square error is found for phantom, rural and satellite images data set. This new method showed improved highly accurate results. General terms Optimization, Soft Computing