Multi-level Fusion Based Deep Convolutional Network for Image Quality Assessment

Image quality assessment aims to design effective models to automatically predict the perceptual quality score of a given image that is consistent with human cognition. In this paper, we propose a novel end-to-end multi-level fusion based deep convolutional neural network for full-reference image quality assessment (FR-IQA), codenamed MF-IQA. In MF-IQA, we first extract features with the help of edge feature fusion for both distorted images and the corresponding reference images. Afterwards, we apply multi-level feature fusion to evaluate a number of local quality indices, and then they would be pooled into a global quality score. With the proposed multi-level fusion and edge feature fusion strategy, the input images and the corresponding feature maps can be better learned and thus help produce more accurate and meaningful visual perceptual predictions. The experimental results and statistical comparisons on three IQA datasets demonstrate that our framework achieves the state-of-the-art prediction accuracy in contrast to most existing algorithms.