Fractals and chaos
Fractal image compression: theory and application
Fractal image compression: theory and application
Introduction to Linear Regression Analysis, Solutions Manual (Wiley Series in Probability and Statistics)
Schema genetic algorithm for fractal image compression
Engineering Applications of Artificial Intelligence
Fractal image compression using visual-based particle swarm optimization
Image and Vision Computing
Technique for fractal image compression using genetic algorithm
IEEE Transactions on Image Processing
A fast encoding algorithm for fractal image compression using the DCT inner product
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A range/domain approximation error-based approach for fractal image compression
IEEE Transactions on Image Processing
Efficient Huber-Markov Edge-Preserving Image Restoration
IEEE Transactions on Image Processing
Image compression and recovery through compressive sampling and particle swarm
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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
A novel gray image representation using overlapping rectangular NAM and extended shading approach
Journal of Visual Communication and Image Representation
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In this paper, a new similarity measure for fractal image compression (FIC) is introduced. In the proposed Huber fractal image compression (HFIC), the linear Huber regression technique from robust statistics is embedded into the encoding procedure of the fractal image compression. When the original image is corrupted by noises, we argue that the fractal image compression scheme should be insensitive to those noises presented in the corrupted image. This leads to a new concept of robust fractal image compression. The proposed HFIC is one of our attempts toward the design of robust fractal image compression. The main disadvantage of HFIC is the high computational cost. To overcome this drawback, particle swarm optimization (PSO) technique is utilized to reduce the searching time. Simulation results show that the proposed HFIC is robust against outliers in the image. Also, the PSO method can effectively reduce the encoding time while retaining the quality of the retrieved image.