SUMMARYThe deblocking filter in high-efficiency video coding (HEVC) has huge computational complexity because of its high content-adaptive coding structure as well as high-definition. Parallelization for it based on massively parallel architectures such as graphics processing unit becomes an urgent demand. However, a large number of conditional branches and data dependencies severely hinder its efficient parallelization. In this paper, a novel parallel optimization strategy based on graphics processing unit is presented for concurrent deblocking in HEVC/H.265 standard to improve the parallel performance. First, by reducing various conditional branches, a normalization mechanism for instruction stream based on feature vector is proposed, which improves the efficiency of boundary strength computation dramatically. The idea can also be applied to edge discrimination. Second, a parallel mechanism based on an adaptive post-correction is presented to process vertical and horizontal edges filtering concurrently, which improves the processing speed obviously, while producing negligible quality loss. Experimental results show that the strategy presented outperforms the existing state-of-the-art method with accelerating factor up to 32.
Efficient searching for resources has become a challenging task with less network bandwidth consumption in unstructured peer-to-peer (P2P) networks. Heuristic search mechanism is an effective method which depends on the previous searches to guide future ones. In the proposed methods, searching for high-repetition resources is more effective. However, the performances of the searches for nonrepetition or low-repetition or rare resources need to be improved. As for this problem, considering the similarity between social networks and unstructured P2P networks, we present a credibility search algorithm based on different queries according to the trust production principle in sociology and psychology. In this method, queries are divided into familiar queries and unfamiliar queries. For different queries, we adopt different ways to get the credibility of node to its each neighbor. And then queries should be forwarded by the neighbor nodes with higher credibility. Experimental results show that our method can improve query hit rate and reduce search delay with low bandwidth consumption in three different network topologies under static and dynamic network environments.
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