Fractal image compression using upper bound on scaling parameter. (January 2018)

• Record Type: Journal Article
• Authors: Roy, Swalpa Kumar; Kumar, Siddharth; Chanda, Bhabatosh; Chaudhuri, Bidyut B.; Banerjee, Soumitro
• Is Part Of: Chaos, solitons and fractals. Volume 106(2018)
• Page Start: 16
• Page End: 22
• Publication Date: 2018-01
• Subjects: Fractal coding speedup -- Scaling parameter upper-bound -- Image data compression; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2017.11.013
• Languages: English
• ISSNs: 0960-0779

Abstract

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.