Blind image quality assessment for multiply distorted images via convolutional neural networks
The past decade has witnessed a growing development of Image Quality Assessment (IQA) techniques. However, the researches of IQA with multiple distortion types are still limited especially on blind image quality assessment methods. In this paper, a Convolutional Neural Network (CNN) based method is proposed to predict the quality of multiply distorted images without references. Inspired by the early human visual model, the proposed CNN based method combines feature learning and regression for estimating the quality of multiply distorted images. The proposed network consists of one convolutional layer, one pooling layer with max and average pooling, two full connection layers and one softmax classification layer. With this network structure, the relationship between the accuracy of CNN and the prediction monotonicity of IQA is explored. Experimental results on the newly released LIVE multiply distorted image quality database verify the effectiveness of the proposed CNN based method.
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)