基于卷积神经网络的真实图像质量评价方法

Nowadays, blind image quality assessment (BIQA) has been intensively studied with machine learning, such as support vector machine (SVM). Existing BIQA metrics, however, do not perform robust for various kinds of distortion types,particularly for authentic distortions. We believe this problem is because those frequently used traditional machine learning techniques exploit shallow architectures, which only contain one single layer of nonlinear feature transformation, and thus cannot highly mimic the mechanism of human visual perception to image quality. The recent advance of deep neural network (DCNN) can help to solve this problem, since the DCNN is found to better capture the essential attributes of images. We in this paper therefore introduce a new Deep learning based Image Qualityfor blind quality assessment.