Entropy of primitive: From sparse representation to visual information evaluation

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, 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 hypothesis. 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-down just-noticeable difference (JND) profile is proposed. The simulation results have shown that the EoP-based JND outperforms the state-of-the-art JND models according to the subjective evaluation.