Open AccessA featurebased approach to fractal image compression
Author(s) -
G. Ya. Kis
Publication year - 2020
Publication title -
zv'âzok
Language(s) - English
Resource type - Journals
ISSN - 2412-9070
DOI - 10.31673/2412-9070.2020.052226
Subject(s) - computer science , artificial intelligence , computer vision , fractal , salient , feature (linguistics) , pattern recognition (psychology) , fractal compression , image compression , image processing , mathematics , image (mathematics) , mathematical analysis , linguistics , philosophy
Recently a number of researches have demonstrated performance improvement in the video fractal compression compared to the current video transmission standards (MPEG, H.263, H.264). This article describes a current problem of relatively low fractal encoding speed. Indeed, high computational complexity is a sore point of fractal compression approach. It seems almost every paper on this subject touches the problem of encoding speed. Productive ideas and algorithms can be borrowed from the pattern recognition problem. In the course of recent decades feature points approach in computer vision has been demonstrating good performance in SLAM and pattern recognition. Technology of feature detection, description and tracking is being developed successfully and has effective applications in augmented reality like Android ARCore and IOS ARKit frameworks that are real-time engines. Similarities among parts of video frames are analyzed and used for both image registration and visual scene tracking therefore it fits highly to block matching task. Statistic properties for domain/range blocks matching has been analyzed on the basis of previous investigation for fractal compression. As a result, a simple algorithm is proposed based on computer vision approach. The approach includes a visual feature points extraction, feature descriptors calculation and fast NN-search in descriptor space. The key idea of the proposed approach is as follows. Only a limited number of domain blocks around the most salient points are subject to selection. Other blocks are not essential for matching as transforms would have big Lipschitz constant and will have worse contractive properties. Salient points should be unique as well. Further the descriptors for feature point are calculated. The algorithm has O(N log N) complexity for pixel number in the frame image, however if the number of domain blocks is limited the complexity could be almost linear. Python program for the algorithm test has been developed and shows that reconstruction result is acceptable in terms of encoding speed (< 2 s on 2 GHz CPU) and quality (PSNR) ~25 dB. The result of the proposed approach could be interesting for further improvement both for image and video compression. Further steps for quality increasing are also described.