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An enhanced fractal image compression integrating quantized quadtrees and entropy coding

Fractal compression is a lossy compression method for digital images based on fractals rather than pixels, which are best suited for textures and natural images. It works on self- similarity property in various fractions of images, relying on the fact that parts of an image often resemble other parts of the same image. It takes long encoding time and affects the image quality. This paper introduces an improved model integrating quantized quad trees and entropy coding used for fractal image compression. Quantized quad tree method divides the quantized original gray level image into various blocks depending on a threshold value besides the properties of the features presented in image. Entropy coding is applied for improving the compression quality. Simulation results show that the quantized quad trees and entropy coding improved compression ratios and quality derived from the fractal image compression with range block and iterations technique. Different quantitative measures can be found by passing images of different format and dimensions.