Image Entropy of Primitive and visual quality assessment
Recently, the concept of Entropy of Primitive (EoP) has been proposed to measure the image visual information. Some successful EoP based application also be developed. In this paper, we further explore the concept of EoP and propose an improved version: the L1 norm based EoP. Our EoP takes full account of the properties of a dictionary's layered structure and the characteristic of a basis pursuit method. Experimental results show that the L1 norm based EoP is superior to the L0 norm based one in measuring the image visual information. The curve of L1 norm based EoP holds a more consistent monotonicity with SSIM, its values is not trapped in the local convergence and the convergence value is less than that of the L0 norm based one. With the convergence characteristics of EoP, we further explore its application in stereoscopic image quality assessment (SIQA). With EoP as monocular cue and mutual information of primitive (MIP) as binocular cue, the relative entropy between the original stereoscopic image and the distorted one is used to compute the quality score by a prediction function which is trained using support vector regression (SVR). Extensive experimental results show that our new EoP based SIQA outperforms many state-of-the-art on the LIVE phase II databases.