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In this study, we present a video search and indexing system based on the state support vector (SVM) network, video graph, and reinforcement agent for recognizing and organizing video events. In order to enhance the recognition performance of the state SVM network, two innovative techniques are presented: state transition correction and transition quality estimation. The classification results are also merged into the video indexing graph, which facilitates the search speed. A reinforcement algorithm with an efficient scheduling scheme significantly reduces both the power consumption and time. The experimental results show the proposed state SVM network was able to achieve a precision rate as high as 83.83% and the query results of the indexing graph reached 80% accuracy. The experiments also demonstrate the performance and feasibility of our system.

H.264, MPEG-4 Part 10, is the latest digital video coding standard that achieves very high data compression by using several new coding features. One of the new features is variable block sizes for interframe coding to increase compression efficiency. However, to achieve this, the H.264 encoder employs a complex mode decision technique based on rate-distortion optimization (RDO) that requires high computational complexity, which significantly increases the encoder complexity. In this paper, we propose a classified region algorithm (CRA) that analyzes the spatial and temporal homogeneity of the block by using cross differences to reduce the number of modes that are required for RDO calculation in inter mode decision. The proposed low computational complexity algorithm significantly reduces the number of inter modes without affecting the video quality. The experimental results show that the proposed method is able to reduce complexity by up to 67% on average with negligible degradation in both objective and subjective quality.

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