Blind Predicting Similar Quality Map for Image Quality Assessment
This addresses the problem of assessing image quality without reference images for applications in computer vision, though it appears incremental as it builds on existing methods.
The paper tackles blind image quality assessment by proposing a model that predicts pixel-by-pixel quality maps from distorted images using a fully convolutional neural network and pooling network, achieving results that outperform state-of-the-art methods.
A key problem in blind image quality assessment (BIQA) is how to effectively model the properties of human visual system in a data-driven manner. In this paper, we propose a simple and efficient BIQA model based on a novel framework which consists of a fully convolutional neural network (FCNN) and a pooling network to solve this problem. In principle, FCNN is capable of predicting a pixel-by-pixel similar quality map only from a distorted image by using the intermediate similarity maps derived from conventional full-reference image quality assessment methods. The predicted pixel-by-pixel quality maps have good consistency with the distortion correlations between the reference and distorted images. Finally, a deep pooling network regresses the quality map into a score. Experiments have demonstrated that our predictions outperform many state-of-the-art BIQA methods.