(untitled) (International Journal of Innovative Research in Computer Science & Technology, 2018)
Generally the fractal image compression is a new process in the images compression. It is a block based image compression technique, which detects and decodes the existing similarities between different regions in the image. The main disadvantage of FIC is that the encoding time is comparatively very high, w here as the decoding time is very short. An artificial intelligence technique like neural network is used to reduce the search space and encoding time for the MRI images with an algorithm called as “back propagation” neural network algorithm. Initially, MRI image is divided into ranges and domains of fixed size. The best matched domain is selected for each range block and its range index and best matched domain index are produced, which acts as input to the expert system and which results reduced the sets of matched domain blocks. The neural network is then trained with these resultant values. This trained net is now used to compress other MRI images which lead to a very less encoding time. During the decoding phase, the transformation parameters are recursively applied to any random original image, which then converges to the fractal image after some changes. The simulation results show that the performance of this Neural Network based FIC is really. This paper shows the neural network based FIC which produces high development in encoding time without corrupting the image quality when compared to normal FIC.