Fractal image compression using upper bound on scaling parameter. (January 2018) - Roy, Swalpa Kumar; Kumar, Siddharth; Chanda, Bhabatosh; Chaudhuri, Bidyut B.; Banerjee, Soumitro
Highlights: The proposed method provides enough speed-up in image encoding. It is faster than many fast variants of Fractal compression methods. Our method is unique to exploit analytically found upper-bound for speed-up. The upper bound embedded in some fast methods can make them faster. Abstract: This paper presents a novel approach to calculate the affine parameters of fractal encoding, in order to reduce its computational complexity. A simple but efficient approximation of the scaling parameter is derived which satisfies all properties necessary to achieve convergence. It allows us to substitute to the costly process of matrix multiplication with a simple division of two numbers. We have also proposed a modified horizontal-vertical (HV) block partitioning scheme, and some new ways to improve the encoding time and decoded quality, over their conventional counterparts. Experiments on standard images show that our approach yields performance similar to the state-of-the-art fractal based image compression methods, in much less time.