Analysis of Smooth and Complex Domain Blocks Classification for Fractal Image Compression
Author Affiliations
- 1Department of Computer Applications, Bhilai Institute of Technology, Durg, Chhattisgarh, India
- 2Depatment of Mathematics, Bhilai Institute of Technology, Durg, Chhattisgarh, India
Res. J. Computer & IT Sci., Volume 4, Issue (6), Pages 12-13, June,20 (2016)
Abstract
Fractal Image Compression has been an old and high compression method of still image compression, but it is being a slow method due to a computation complexity for searching the matching block from large domain. This paper proposed an analysis of domain blocks classification for speeding up the encoding time. For the analysis, three methods: fractal dimension (FD), linear interpolation (LI) and discrete cosine transform (DCT), are used for block complexity measure. The Experiments compares the methods with smooth and complex domain blocks classification.
References
- Barnsley M.F. (1988)., Fractal Everywhere (2nd Edition)., Academic Press, Boston, New York.
- Jacquin A.E. (1992)., Image Coding Based on Fractal Theory of Iterated Contractive Image Transformation., IEEE Transaction, 1(1), 18-30.
- Fisher Y. (1994)., Fractal Image Compression., Fractals, 2(3), 325-334.
- Conic A. and Compos C.F.J. (1996)., An Efficient Box-counting Fractal Dimension Approach for Experimental Image Variation Characterization., Proceeding of IWS IP’96-3rd International Workshop in Signal and Image Processing, Elsevier Science, Manchester, UK, 665-668.
- Sarkar N. and Choudhuri B.B. (1992)., An Efficient Approach to Estimate Fractal Dimension of Textural Images., Pattern Reorganization, (25), 1035-1041.