Iteration Free Fractal Image Compression For Color Images Using Vector Quantization, Genetic Algorithm And Simulated Annealing

This research paper on iteration free fractal image compression for color images using the techniques Vector Quantization, Genetic Algorithm and Simulated Annealing is proposed, for lossy compression, to improve the decoded image quality, compression ratio and reduction in coding time. Fractal coding consists of the representation of image blocks through the contractive transformation coefficients, using the self-similarity concept present in the larger domain blocks. Fractal coding achieves high compression ratio but it consumes more time to compress and decompress an image. Different techniques are available to reduce the time consumption and improve the decoded image reliability. But most of them lead to a bad image quality, or a lower compression ratio. Usage of synthetic codebook for encoding using Fractal does not require iteration at decoding and the coding error is determined immediately at the encoder. The techniques Vector Quantization, Genetic Algorithm and Simulated Annealing are used to determine the best domain block that matches the range blocks. The proposed algorithm has the better performance in terms of image quality, bit rate and coding time for Color images. Only the encoding consumes more time but the decoding is very fast.