Improved entropy of primitive for visual information estimation
Sparse representation has been observed to be highly efficient in dealing with rich, varied and directional information in natural scenes. Based on the statistical analysis of primitives in sparse coding, the entropy of primitive (EoP) was proposed for measuring visual information of images, and its changing tendency has been shown to be highly relevant with the human visual system (HVS). But the sparse coefficient energy was ignored when calculating EoP, which may be critical in accounting for the primitive characteristics. To tackle this, an improved EoP is developed in this work via ℓ2 norm calculation. We further give mathematical derivations for its convergence verification. Experimental evaluations have also demonstrated that the improved EoP can achieve more stable convergence tendencies, which is consistent with the perceptual experiences.