Authors:
Nilavra Bhattacharya
1
;
Swalpa Kumar Roy
1
;
Utpal Nandi
2
and
Soumitro Banerjee
3
Affiliations:
1
Indian Institute of Engineering Science & Technology, India
;
2
Vidyasagar University, India
;
3
Indian Institute of Science Education and Research, India
Keyword(s):
Fractal Image Compression, Fisher Classification, IFS, PIFS, Hierarchical Classification, Block Classification.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Image and Video Coding and Compression
;
Image Formation and Preprocessing
;
Segmentation and Grouping
Abstract:
In fractal image compression (FIC) an image is divided into sub-images (domains and ranges), and a range
is compared with all possible domains for similarity matching. However this process is extremely time-consuming.
In this paper, a novel sub-image classification scheme is proposed to speed up the compression
process. The proposed scheme partitions the domain pool hierarchically, and a range is compared to only those
domains which belong to the same hierarchical group as the range. Experiments on standard images show that
the proposed scheme exponentially reduces the compression time when compared to baseline fractal image
compression (BFIC), and is comparable to other sub-image classification schemes proposed till date. The
proposed scheme can compress Lenna (512x512x8) in 1.371 seconds, with 30.6 dB PSNR decoding quality
(140x faster than BFIC), without compromising compression ratio and decoded image quality.