Comparative image quality assessment using free energy minimization
It is a straightforward task for human observers to judge the relative quality of two visual signals of the same content, but subject to different type/level of distortions. However, this comparative image quality assessment (C-IQA) problem remains a difficult challenge for the current research of image quality assessment (IQA). In this paper, we propose a C-IQA approach to predict the relative perceptual quality of a pair of images that are possibly subject to different artifact types/levels. The C-IQA algorithm is designed to emulate the process of comparing the relative quality of two visual stimuli as performed by the human visual system (HVS) within the framework of free energy minimization. The brain's internal generative models initialized on the inputs are used to explain the two images. And their relative quality can then be determined through comparing the free energy level of this model-data fitting process. In the existing work of IQA, the full-reference (FR) and reduced-reference (RR) methods need the prior knowledge of the original images while the no-reference (NR) algorithms usually work with a single input image. The C-IQA approach is inherently different from those existing methods in that it takes as input an image pair and predicts their relative quality without using any knowledge about the original image. A computationally efficient solution to the proposed C-IQA scheme based on a linear autoregressive image model is also introduced. Experimental results show that the proposed method achieves about 98% accuracy in line with the subjective ratings when applied on over 300,000 image pairs sampled from the LIVE database, outperforming the FR metrics such as PSNR, SSIM, and some of the most advanced NR IQA algorithms.