Fractal Video Coding Using Fast Normalized Covariance Based Similarity Measure

Fast normalized covariance based similarity measure for fractal video compression with quadtree partitioning is proposed in this paper. To increase the speed of fractal encoding, a simplified expression of covariance between range and overlapped domain blocks within a search window is implemented in frequency domain. All the covariance coefficients are normalized by using standard deviation of overlapped domain blocks and these are efficiently calculated in one computation by using two different approaches, namely, FFT based and sum table based. Results of these two approaches are compared and they are almost equal to each other in all aspects, except the memory requirement. Based on proposed simplified similarity measure, gray level transformation parameters are computationally modified and isometry transformations are performed using rotation/reflection properties of IFFT. Quadtree decompositions are used for the partitions of larger size of range block, that is, 16 × 16, which is based on target level of motion compensated prediction error. Experimental result shows that proposed method can increase the encoding speed and compression ratio by 66.49% and 9.58%, respectively, as compared to NHEXS method with increase in PSNR by 0.41 dB. Compared to H.264, proposed method can save 20% of compression time with marginal variation in PSNR and compression ratio.

Performance improving of fractal encoding based on perceptual hash functions.

The application of fractal encodingfor image recognition is considered, system requirements to computing resources of classic fractal encoding system implementation is analyzed. The proposal for improving performance characteristics is suggested and implemented, obtained results and comparison of two implementations are provided in the last part of article.

Fast Fractal Image Coding Method Based on RMSE and DCT Classification

To solve the problem of long time consuming in the fractal encoding process, a fast fractal encoding algorithm based on RMSE (Root mean square error) and DCT (Discrete Cosine Transform) classification is proposed. During the encoding process, firstly, the image is divided into range blocks and domain blocks by quadtree partition according to RMSE, then, according to DCT coefficients of image block, three classes of image blocks are defined, which are smooth class, horizontal/vertical edge class, diagonal/sub-diagonal class. At last, every range block is limited to search the best matched block in the corresponding domain block class, and the fractal coding are recorded until the process is completed. When searching the best matched block, the nearest neighbor block will be found in the sense of RMSE in the ordered codebook, and the best matched block will be further found in the vicinity of the nearest neighbor block. The experimental results show that the proposed algorithm can efficiently reduce the search space and shorten the encoding time, while achieving the same reconstructed image quality as that of the full search method.