Greylevel difference classification algorithm in fractal image compression
Journal of Computer Science and Technology
Fractal coding of color images using earth mover's distance
MobiMedia '06 Proceedings of the 2nd international conference on Mobile multimedia communications
Fractal image compression using visual-based particle swarm optimization
Image and Vision Computing
Video rendering: zooming video using fractals
VLBV'05 Proceedings of the 9th international conference on Visual Content Processing and Representation
The effectiveness of image features based on fractal image coding for image annotation
Expert Systems with Applications: An International Journal
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Fractal image compression has received much attention from the research community because of some desirable properties like resolution independence, fast decoding, and very competitive rate-distortion curves. Despite the advances made, the long computing times in the encoding phase still remain the main drawback of this technique. So far, several methods have been proposed in order to speed-up fractal image coding. We address the problem of choosing the best speed-up techniques for fractal image coding, comparing some of the most effective classification and feature vector methods-namely Fisher (1994), Hurtgen (1993), and Saupe (1995, 1996)-and a new feature vector coding scheme based on the block's mass center. Furthermore, we introduce two new coding schemes combining Saupe with Fisher, and Saupe with mass center coding scheme. Experimental results demonstrate both the superiority of feature vector techniques on classification and the effectiveness of combining Saupe and the mass center coding scheme, an approach that exhibits the best time-distortion curves