Full-Reference Predictive Modeling of Subjective Image Quality Assessment with ANFIS

Digital images often undergo through various processing and distortions which subsequently impacts the perceived image quality. Predicting image quality can be a crucial step to tune certain parameters for designing more effective acquisition, transmission, and storage multimedia systems. With the huge number of images captured and exchanged everyday, automatic prediction of image quality that correlates well with human judgment is steadily gaining increased importance. In this paper, we investigate the performance of three combinations of objective metrics for image quality prediction with an adaptive neuro-fuzzy inference system (ANFIS). Images are processed to extract various attributes which are then used to build a predictive model to estimate a differential mean opinion score for different types of distortions. Using a publicly available and subjectively rated image database, the proposed method is evaluated and compared to individual metrics and an existing technique based on correlation and error measures. The results prove that the proposed method can be a promising approach for predicting subjective quality of images.