Image compression using fractals for color and textured images

Abstract

This Research work particularly deals with fractal image compression with an idea to minimize the computational requirements to achieve enhanced reproduction of image quality. Problems such as use of fractal geometry for image compression, extension of this concept for color image compression, encoding of video sequences in compression, application of the concept of compression for remote-sensed images, use of wavelets in fractal compression algorithm for enhanced performance, and extension of wavelet-based fractal concept for compression of textured images have been discussed in this study. newlineThe concept of wavelet is combined with this to enhance the performance. Fractal image compression is desirable because of its resolution independence, faster decoding and competitive rate distortion curves. However, the main drawbacks in the Fractal Image compression method, such as longer computation time for encoding and heavy computation for full and exhaustive search, have been alleviated using Partitioned Iterated Function Systems in this study. This method is also used to compress the color and textured images. RGB components in the color images are encoded independently. Here, only the partial distance is calculated while comparing the range with domain blocks. Domain blocks with minimum distance are selected. Thus a high compression ratio can be achieved. newlineFidelity and efficiency of the restored images by fractal techniques are better compared with the previous image compression methods. It has a high compression rate when compared with JPEG and wavelet. The results cover the comparisons of original and reconstructed images, fidelity evaluation and analysis of compression rates on different land covers, different resolutions and discussion on the efficiency of the algorithms. Heavy reduction in encoding-decoding time is achieved by combining the wavelet with fractal. newline