Fast fractal image compression using feature vector matching

This paper presents a fast algorithm for fractal image compression. The algorithm uses quadtree partitioning to partition an image into image blocks of different sizes. Each of the image blocks is normalized to have zero mean and unity variance, and represented by a feature vector of dimension 16. The feature vectors, which can provide an accurate representation of the image blocks, are composed of the means and/or variances of each of the rows and columns. The k-d tree structure is used to partition the feature vectors of the domain blocks. This arrangement allows the search of the best matched domain block for a range block efficiently and accurately. An efficient encoding approach for low complexity range blocks is also proposed, which encodes the mean of a range block without searching the domain blocks. Moreover, during the range-domain matching process, a simple but very efficient search by using the property of zero contrast value is introduced, which can further improve the encoding time and compression ratio, especially in high compression ratio. This can lead to an improvement in encoding time and an increase in compression ratio, while maintaining comparable image quality. Experimental results show that the run-time required by our proposed algorithm is over 200 times faster than that of a full search.