Image Quality Assessment using ANFIS Approach

Abstract

Due to the increasing use of digital images in electronic systems, it becomes important to evaluate the degradation in image quality during acquisition, processing, storage and transmission. In this paper, we investigate the ability of the adaptive neuro-fuzzy inference system (ANFIS) for quality assessment of digital images with respect to original (reference) images. Several metrics for objective quality assessment are calculated and used as inputs to an adaptive fuzzy inference system which in turn estimates a differential mean opinion score (DMOS) for different types of distortions. The predicted values are compared with the actual DMOS values using correlation and error measures. With 7-input ANFIS network, the results show that predicted DMOS values are highly correlated to the actual values using a publicly available and subjectively rated image database. For example, for distorted images due to JPEG 2000 compression, the attained results for correlation coefficient, Spearman's ranked correlation, and RMSE are 0.9944, 0.9902, and 3.32, respectively. These results show that combining the advantages of neural networks with fuzzy systems can be a promising approach for predicting the subjective quality of digital images.