IMAGE QUALITY ASSESSMENT BY USING AR PREDICTION ALGORITHM WITH INTERNAL GENERATIVE MECHANISM?
C. Naga Venkat Raam, K. Lakshmi Bhavani?
Journal Title:International Journal of Computer Science and Mobile Computing - IJCSMC
The main aim of Objective image quality assessment (IQA) is to evaluate image quality consistently
with human perception. We have different types of perceptual IQA metrics but they cannot accurately represents
the degradations from different types of distortions, e.g., existing structural similarity metrics perform well on
content dependent distortions and gives the better peak signal-to-noise ratio (PSNR) but it is not well on
content-independent distortions. In this paper, we integrate the merits of the existing IQA metrics with the guide
of the recently revealed internal generative mechanism (IGM). The IGM indicates that the human visual system
actively predicts sensory information and tries to avoid residual uncertainty for image perception and
understanding. Motivated by the IGM theory, here we assume an autoregressive prediction algorithm to
decompose an input scene into two portions, the predicted portion with the predicted visual content and the
disorderly portion with the residual content. Distortions on the predicted portion causes to degrade the primary
visual information, and structural similarity procedures are employed to measure its degradation; distortions on
the disorderly portion mainly change the uncertain information and the PNSR is employed for it. Based on the
noise energy deployment on the two portions, finally we mix the two evaluation results to acquire the overall
quality score. Simulation results show better performance comparable with the state-of-the-art quality metrics.