Normal maps vs. visible images: Comparing classifiers and combining modalities
Journal of Visual Languages and Computing
Study on huber fractal image compression
IEEE Transactions on Image Processing
Novel fractal image encoding algorithm using normalized one-norm and kick-out condition
Image and Vision Computing
Embedding linear transformations in fractal image coding
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Face and ear: a bimodal identification system
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Embedding quality measures in PIFS fractal coding
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
| Hi-index | 0.01 |
Fractals can be an effective approach for several applications other than image coding and transmission: database indexing, texture mapping, and even pattern recognition problems such as writer authentication. However, fractal-based algorithms are strongly asymmetric because, in spite of the linearity of the decoding phase, the coding process is much more time consuming. Many different solutions have been proposed for this problem, but there is not yet a standard for fractal coding. This paper proposes a method to reduce the complexity of the image coding phase by classifying the blocks according to an approximation error measure. It is formally shown that postponing range/slash domain comparisons with respect to a preset block, it is possible to reduce drastically the amount of operations needed to encode each range. The proposed method has been compared with three other fractal coding methods, showing under which circumstances it performs better in terms of both bit rate and/or computing time.