Neural based domain and range pool partitioning using Fractal Coding for nearly lossless Medical Image Compression

This work results from a fractal image compression based on iterated transforms and machine learning modeling. In this work an improved quasi-losses fractal coding scheme is addressed to preserve the rich features of the medical image as the domain blocks and to generate the remaining part of the image from it based on fractal transformations. Machine learning based model is used for improving the performance of the fractal coding scheme and also to reduce the encoding computational complexity. The performance of the proposed algorithm is evaluated in terms of compression ratio, PSNR and encoding computation time, with standard fractal coding for MRI image datasets of size 512×512 over various thresholds. The results show the increase in encoding speed, outperforming some of the currently existing methods thereby ensuring the possibility of using fractal based image compression algorithms for medical image compression. Key-Words: Image Compression, Iterated transforms, Fractal Image Compression, Medical Image, Fractals