Image Quality Assessment using ANFIS Approach
El-Sayed M. El-Alfy, Mohammed Rehan Riaz · 2014
Due to the increasing use of digital images in electronic systems, image processing is gaining considerable attention nowadays. In this paper, we investigate the ability of adaptive neuro-fuzzy inference system (ANFIS) in assessing the quality of digital images. This is implemented through comparison of the predicted and actual differential mean opinion score (DMOS). Several distinguishing features are extracted and adopted to construct computational classification models that predict the DMOS value. We found that for a 7-input ANFIS network, the predicted DMOS values for distorted images of blur type have a high linear correlation coefficient of 0.9937, a Spearman’s ranked correlation of 0.9902, and RMSE of 3.2%. Moreover, the predicted DMOS values for distorted images of JPEG 2000 compression type have a high linear correlation coefficient of 0.9944, a Spearman’s ranked correlation of 0.9902, and RMSE of 3.32%. This shows that combining the advantages of both neural network and fuzzy systems can be a promising approach for assessing the quality of digital images.