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.

本文介绍了一种基于卷积神经网络和池化网络的盲图像质量评估模型，能够有效地模拟人类视觉系统的特性，并通过全参考图像质量评估方法获得的中间相似度图来预测从扭曲的图像中像素之间的质量。最终结果通过深池化网络回归为得分，该方法表现优于许多现有的盲图像质量评估方法。

盲预测相似质量图用于图像质量评估