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In this paper, we propose a novel concept in evaluating the visual information when perceiving natural images—the entropy of primitive (EoP). Sparse representation has been successfully applied in a wide variety of signal processing and analysis applications due to its high efficiency in dealing with rich varied and directional information contained in natural scenes. Inspired by this observation,...
In this paper, we propose a novel image set compression approach based on sparse coding with an ordered dictionary learned from perceptually informative signals. For a group of similar images, one representative image is first selected and transformed into wavelet domain, and then its AC components are utilized as samples to train an over-complete dictionary. In order to improve compression efficiency,...
In this paper, a novel full-reference (FR) image quality assessment (IQA) metric based on sparse representation is proposed. Sparse representation has been widely applied in many applications such as image denoising and restoration. It is a high-efficiency way in representing sparse and redundant natural images. Also it has been shown to be highly related to the human visual perception, which is characterized...
Recently, a novel concept referred to as Entropy of Primitive (EoP) has been proposed for evaluating the visual information of natural images. The idea originates from the sparse representation, which has been successfully applied in a wide variety of signal processing and analysis tasks. This is because of the high efficiency of sparse representation in dealing with rich, varied and directional information...
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