Entropy of Primitive: From Sparse Representation to Visual Information Evaluation
Abstract: In this paper, we propose a novel concept in evaluating the visual information when perceiving natural images images-the the entropy of primitive (EoP). (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, observa in this paper, the visual signal can be decomposed into structural and nonstructural layers according to the visual importance of sparse primitives. Accordingly, the EoP is developed in measuring the visual information. It has been found that the EoP changing tendency in image sparse representation is highly relevant with the hierarchical perceptual cognitive process of human eyes. Extensive mathematical explanations as well as experimental verifications have been presented in order to support the hyp hypothesis. othesis. The robustness of the EoP is evaluated in terms of varied block sizes. The dictionary universality is also studied by employing both universal and adaptive dictionaries. With the convergence characteristics of the EoP, a novel top top-down just-noticeable able difference (JND) profile is proposed. The simulation results have shown that the EoP-based EoP JND outperforms the state-of--the-art art JND models according to the subjective evaluation.