In this study, a fast block matching search algorithm based on blocks' descriptors and multilevel blocks filtering is introduced. The used descriptors are the mean and a set of centralized low order moments. Hierarchal filtering and MAE similarity measure were adopted to nominate the best similar blocks lay within the pool of neighbor blocks. As next step to blocks nomination the similarity of the mean and moments is used to classify the nominated blocks and put them in one of three sub-pools, each one represents certain nomination priority level (i.e., most, less & least level). The main reason of the introducing nomination and classification steps is a significant reduction in the number of matching instances of the pixels belong to the compared blocks is achieved. Instead of pixels-wise comparisons a set of hierarchal similarity comparisons between few descriptors of the compared blocks is done. The computations of blocks descriptors have linear complexity, O(n) and small number of involved similarity comparisons is required. As final stage, the selected blocks as the best similar blocks according to their descriptors are only pushed to pixel-wise blocks comparison stage. The performance of the proposed system was tested for both cases: (i) without using prediction for assessing the initial motion vector and (ii) with using prediction that based on the determined motion vectors of already scanned neighbor blocks. The test results indicated that the introduced method for both cases (without/ with prediction) can lead to promising results in terms of time and error level; because there is reduction in search time and error level parameters in comparison with exhaustive search and three step search (TSS) algorithms.
