Image Compression Based on Fractal Coding and Wavelet Transform

Image compression involves reducing the size of image data files, while retaining necessary information. Compression is a necessary and essential method for creating image files with manageable and transmittable sizes. There have been many types of compression algorithms developed. This work exploits a hybrid image compression based on fractal coding and wavelet transform.In the proposed method, the RGB image was transformed into  color transform, the goal of this transforming is to prepare the image for encoding process by eliminating any irrelevant information, then the compression technique is started by applying the Haar wavelet transform on the luminance component. The LL approximation subband is recompressed by applying the Partitioned Iterated Function System (PIFS) on it. The LL sub band is partitioned into non overlapped range blocks and overlapped domain blocks (the over lapping is according to the jump step value) using fixed size square blocks Partitioning. The matching technique is used to find the best domain block which satisfies the best map to the range block with minimum error. The detailed subbands (HL, LH and HH) are quantized using a uniform quantization. A new scheme of Run Length Encoding (RLE) is applied on the quantized sub bands. The resulted sub bands are coded again using S-Shift optimizer and S-Shift encoder.The Chrominance components (Cb and Cr) are also compressed using Fractal coding with the same technique that used on the LL sub band.