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Sparse representation is efficient to approximately recover signals by a linear composition of a few bases from an over-complete dictionary. However, in the scenario of data compression, its efficiency and popularity are hindered due to the extra overhead for encoding the sparse coefficients. Therefore, how to establish an accurate rate model in sparse coding and dictionary learning becomes meaningful,...
Visual features of images and video frames have become pervasive and maturely developed in extensive research fields such as computer vision and visual search. For realtime retrieval applications, the compact visual features should be transmitted and stored at server side in cloud. These local feature descriptors are characterized by the invariance properties for the variances caused by camera motion,...
With the coming age of big data, the cloud technology, referred to as the computations or applications through the Internet, is dramatically developed. The screen content has become one of the most common data form due to the extraordinary advance of network communication technology, and the JCT-VC has started to develop new standard focusing on improving the efficiency of screen content based on...
In this paper, we propose a new reduced reference image quality assessment algorithm based on the recent advances in sparse coding and representation, particularly, the entropy of primitives (EoP). The EoP is defined in terms of the distribution of the primitives, which form an overcomplete dictionary to represent the natural scene by linear combination. Constructively, we develop a reduced reference...
In this paper an image splicing detection scheme is proposed. The scheme is based on image quality and analysis of variance. Four kinds of noise used to simulated the image quality changes which caused by tampering of images, and analysis of variance is used to selected the image quality measures which are more sensitive to image blind splicing detection. Combined with the characteristic function...
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