REVISITING NATURAL SCENE STATISTICAL MODELING USING DEEP FEATURES FOR OPINION-UNAWARE IMAGE QUALITY ASSESSMENT

Abstract

Opinion-unaware no-reference (OU-NR) methods for image quality assessment (IQA) are of great interest since they can predict visual quality independent of a reference image and knowledge of human quality opinions. Models of image naturalness trained on a corpus of pristine images have shown potential for developing OU-NR methods. However, the extracted features may not match the preferences of the human visual system (HVS). This paper aims to utilize the features of convolutional neural networks to achieve a richer representation of the naturalness space. In addition, the IQA processing steps from training to quality measurement are revisited and the naturalness model is improved by incorporating HVS-inspired criteria. Experimental results show the higher performance and generalizability of the naturalness model - constructed using HVS-aligned deep features - under different distortion types and image contents. The source code of the quality index is available at https://gitlab.com/saeedmp/dni.