A novel fast fractal image compression based on reinforcement learning. (6th December 2019)

• Record Type: Journal Article
• Authors: Varghese, Bejoy; Krishnakumar, S.
• Is Part Of: International journal of computational vision and robotics. Volume 9:Number 6(2019)
• Page Start: 559
• Page End: 568
• Publication Date: 2019-12-06
• Subjects: machine learning -- image compression -- reinforcement learning -- fractal coding; Computer vision -- Periodicals; Robotics -- Periodicals; Artificial intelligence -- Periodicals; 006.3705
• Languages: English
• ISSNs: 1752-9131

Abstract

The concept of digital image compression is of considerable interest in the area of transmission and storage of images. The recent research in this area explores the combination of different coding techniques to achieve a better compression ratio without compromising the image quality. Fractal-based coding techniques got the attention of the research community from the very earlier days of data compression. However, those methods are computationally intensive at that time because of the exhaustive search involved to select a transformation sequence. In this paper, we propose a system that replaces the current domain range comparison in the fractal compression with a reinforcement learning technique that reduces the compression time and increases the PSNR. The system will learn from the output of the exhaustive algorithm in the initial state and discard the combinatorial search after trained on a data set. The recommended method shows a good improvement in the compression ratio, PSNR and compression time.