Fractal image compression
Image compression: a study of the iterated transform method
Signal Processing
A review of the fractal image compression literature
ACM SIGGRAPH Computer Graphics
An optimal algorithm for approximate nearest neighbor searching
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Breaking the Time Complexity of Fractal Image Compression
Breaking the Time Complexity of Fractal Image Compression
Advances in fractal compression for multimedia applications
Multimedia Systems
Greylevel difference classification algorithm in fractal image compression
Journal of Computer Science and Technology
IEEE Transactions on Visualization and Computer Graphics
Classification Based Speed-Up Methods for Fractal Image Compression on Multicomputers
ParNum '99 Proceedings of the 4th International ACPC Conference Including Special Tracks on Parallel Numerics and Parallel Computing in Image Processing, Video Processing, and Multimedia: Parallel Computation
Image magnification based on a blockwise adaptive Markov random field model
Image and Vision Computing
Classification of face images using local iterated function systems
Machine Vision and Applications
Toward real time fractal image compression using graphics hardware
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
The effectiveness of image features based on fractal image coding for image annotation
Expert Systems with Applications: An International Journal
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In fractal image compression the encoding step is computationally expensive. A large number of sequential searches through a list of domains (portions of the image) are carried out while trying to find the best match for another image portion. Our theory developed here shows that this basic procedure of fractal image compression is equivalent to multi-dimensional nearest neighbor search. This result is useful for accelerating the encoding procedure in fractal image compression. The traditional sequential search takes linear time whereas the nearest neighbor search can be organized to require only logarithmic time. The fast search has been integrated into an existing state-of-the-art classification method thereby accelerating the searches carried out in the individual domain classes. In this case we record acceleration factors from 1.3 up to 11.5 depending on image and domain pool size with negligible or minor degradation in both image quality and compression ratio. Furthermore, as compared to plain classification our method is demonstrated to be able to search through larger portions of the domain pool without increased the computation time.