Improved Fractal Image Compression Based on Robust Feature Descriptors

Abstract

Fractal image compression is one of the most promising techniques for image compression due to advantages such as resolution independence and fast decompression. It exploits the fact that natural scenes present self-similarity to remove redundancy and obtain high compression rates with smaller quality degradation compared to traditional compression methods. The main drawback of fractal compression is its computationally intensive encoding process, due to the need for searching regions with high similarity in the image. Several approaches have been developed to reduce the computational cost to locate similar regions. In this work, we propose a method based on robust feature descriptors to speed up the encoding time. The use of robust features provides more discriminative and representative information for regions of the image. When the regions are better represented, the search for similar parts of the image can be reduced to focus only on the most likely matching candidates, which leads to reduction on the computational time. Our experimental results show that the use of robust feature descriptors reduces the encoding time while keeping high compression rates and reconstruction quality.

Fast sparse fractal image compression

“…We implemented sevveral existing accelerating methods with c++ in the SFIC scheme, including the hog feature-based method [ 36 ], the polar angle and the normalized root mean square error (NRMS)-based method [ 37 ], and the APCC-based only sorting method [ 16 ]. The comparisons among these SFIC schemes with different accelerating technologies are given in Fig 7 .…”

Computational complexity of fractal image compression algorithm

“…Images block with better discriminative and representative information is easy to map as compared to other image blocks [44–46]. Articles in literary works [20, 47–49] claimed to improve encoding speed of FIC by using the robust features for efficient representation of image blocks.…”

A Novel Fractal Image Compression Scheme With Block Classification and Sorting Based on Pearson's Correlation Coefficient

“…In this subsection, the proposed APCC-based classification and sorting scheme is compared with some other fast FIC schemes: the Fisher's 72 classes scheme (Fisher72) [15], the HOG-based clustering scheme (HOG-FIC) [16], Kovacs's classification scheme (Kovacs) [17], the variance-based sorting scheme (VBFC) [20], the one norm-based kick-out scheme (kickout) [21], and the only-sorting scheme proposed in Section IV in this paper (only-sorting). All these schemes, except kickout scheme, are implemented with the optimized energy function introduced in Subsection A of Section VI, which provides a fair comparison of performance between them.…”