Fractals and chaos
Fractal image compression: theory and application
Fractal image compression: theory and application
Solution of an overdetermined system of equations in the l1 norm [F4]
Communications of the ACM
Introduction to Linear Regression Analysis, Solutions Manual (Wiley Series in Probability and Statistics)
A maximum likelihood approach to least absolute deviation regression
EURASIP Journal on Applied Signal Processing
Fractal image compression using visual-based particle swarm optimization
Image and Vision Computing
Fractal image compression based on spatial correlation and hybrid genetic algorithm
Journal of Visual Communication and Image Representation
Novel fractal image encoding algorithm using normalized one-norm and kick-out condition
Image and Vision Computing
A hybrid fractal video compression method
Computers & Mathematics with Applications
Adaptive partition and hybrid method in fractal video compression
Computers & Mathematics with Applications
An improved no-search fractal image coding method based on a fitting plane
Image and Vision Computing
Image coding based on a fractal theory of iterated contractive image transformations
IEEE Transactions on Image Processing
Fractal pursuit for compressive sensing signal recovery
Computers and Electrical Engineering
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In this paper, some similarity measures for fractal image compression (FIC) are introduced, which are robust against noises. In the proposed methods, robust estimation technique from statistics is embedded into the encoding procedure of the fractal inverse problem to find the parameters. When the original image is corrupted by noises, we hope that the proposed scheme is insensitive to those noises presented in the corrupted image. This leads to a new concept of robust estimation of fractal inverse problem. The proposed least absolute derivation (LAD), least trimmed squares (LTS), and Wilcoxon FIC are the first attempt toward the design of robust fractal image compression which can remove the noises in the encoding process. The main disadvantage of the robust FIC is the computational cost. To overcome this drawback, particle swarm optimization (PSO) technique is utilized to reduce the searching time. Simulation results show that the proposed FIC is robust against the outliers in the image. Also, the PSO method can effectively reduce the encoding time while retaining the quality of the retrieved image.