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Summary form only given. In this paper we propose a video coding optimization strategy for H.264/AVC encoder which uses HVS (Human Visual System) theory to improve the visual quality. It is based on a basic rule that sensitivity of distortion is different according to the image property. Most existing coding strategies which allocate fixed parameters to each unit within a frame are not reasonable...
Multi-modality, the unique and important property of video data, is typically ignored in existing video adaptation processes. To solve this problem, we propose a novel approach, named multi-modality transfer based on multi- graph optimization (MMT-MGO) in this paper, which leverages multi-modality knowledge generalized by auxiliary classifiers in the source domain to assist multi-graph optimization...
In computer vision and multimedia search, it is common to use multiple features from different views to represent an object. For example, to well characterize a natural scene image, it is essential to find a set of visual features to represent its color, texture, and shape information and encode each feature into a vector. Therefore, we have a set of vectors in different spaces to represent the image...
A novel achievement to optimum bit allocation for H.264/AVC is presented, which is different from JVT-O016 by using a simple linear rate instead of the well-known MPEG-4 Q2 model. To find the global optimum, we resort to Lagrange optimization technique and develop a close-form formula to the optimum problem. It is shown via extensive experiments that the new rate control (RC) scheme exceeds JVT-G012,...
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