Abstract: Medical images play a vital role in the area of medicine. It is important to store the medical images for future
reference. So, there is a need for compressing of medical images for storage and communication purpose. Over the last few
decades, many image compression methods have been introduced. They give high compression ratio with loss of quality of
image. Medical images should always be stored in lossless format. There are several lossless compression techniques using
which, original images can be restored. The objective of image compression is to reduce the redundancy of the image and to
store or transmit data in an efficient form. The different compression algorithm currently in use in medical imaging, one
such type of image compression is Fractal Image Compression (FIC). These FIC techniques commonly use the optimization
techniques to find the optimal best solution. The aim of the FIC is to divide the image into pieces or sections and then finds
self-similar ones. It produces high compression ratio, fast decompression in short amount of time. In this paper, Flower
Pollination Based Optimization approach is used for fractal image compression. This optimization technique effectively
reduces the encoding time while retaining the quality of the image. Here, Flower pollination algorithm (FPA) is compared
with Genetic algorithm (GA) and their performances are analyzed in terms of compression ratio, encoding time and Peak
Signal to Noise Ratio (PSNR).
Keywords: Fractal image compression, medical image, flower pollination algorithm
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