Fractals and chaos
Image compression: a study of the iterated transform method
Signal Processing
Barnsley's scheme for the fractal encoding of images
Journal of Complexity
Fractal image compression: theory and application
Fractal image compression: theory and application
Fast fractal image block coding based on local variances
IEEE Transactions on Image Processing
A review of the fractal image coding literature
IEEE Transactions on Image Processing
A fast encoding algorithm for fractal image compression using the DCT inner product
IEEE Transactions on Image Processing
A fast fractal image coding based on kick-out and zero contrast conditions
IEEE Transactions on Image Processing
Image coding based on a fractal theory of iterated contractive image transformations
IEEE Transactions on Image Processing
A fast classification based method for fractal image encoding
Image and Vision Computing
Novel fractal image encoding algorithm using normalized one-norm and kick-out condition
Image and Vision Computing
Real time fractal image coder based on characteristic vector matching
Image and Vision Computing
An efficient approach to speed up the search process during fractal matching process
AMERICAN-MATH'12/CEA'12 Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics
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In this paper, a fast fractal encoding algorithm using simple classification scheme is proposed. During the encoding process, the range blocks and domain blocks are classified first. Then, each range block is limited to search in the corresponding domain class to find the best match. Since the searching space is reduced, the encoding speed is improved. Three classes of image blocks are defined, which are smooth class, diagonal/sub-diagonal edge class and horizontal/vertical edge class. The classification operation is performed using only the lowest horizontal and vertical DCT coefficients of the given block. Thus the classification scheme is simple and computationally efficient. Moreover, since the classification mechanism is designed according to the edge properties and the intrinsic idea of fractal coding, the quality of the decoded image can be preserved. The thresholds for the classifier are also adaptively determined from the range pool so as to reduce the overhead and to guarantee a stable speedup ratio of 3. Simulation results show that the stable speedup ratio of the proposed algorithm can be achieved and is independent of images while the quality of the decoded image is almost the same as that of the full search method.