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

Word Level Multi-Script Identification Using Curvelet Transform in Log-Polar Domain

<p>Nowadays, a number of scripts are used for writing. Script identification finds many applications like sorting and preparing an online database of documents. Identifying these scripts, especially with different orientations and scales, is an important and challenging problem in document image analysis. This paper proposed a new scheme for script identification from word images using skew and scale robust log-polar curvelet features. These word images are first extracted in the form of text-patches from documents using Gaussian filtering. Thereafter, texture features are calculated using curvelet transform in log-polar domain. Log-polar domain is independent of rotation and scale variations, whereas curvelet transform exhibits directional and anisotropic properties. This helps in the extraction of significant features. For experiments, <i>k</i>-nearest neighbor classifier is employed to identify the scripts, as it has zero training time and is simple to implement. Further, statistical significance test is performed by using two more classifiers, namely random forest and support vector machine. Comprehensive experimentations are carried out on ALPH-REGIM, Pati and Ramakrishnan, PHDIndic_11, and proprietary databases containing printed as well as handwritten texts. Here, bi-script, tri-script, and multi-script identification results are reported. Benchmarking analysis illustrated the effectiveness of the proposed method, where a maximum recall rate of 98.76% has been achieved.</p