Multitask convolutional neural network for no-reference image quality assessment

Abstract

We propose a multitask convolutional neural network (CNN) for general no-reference image quality assessment (NR-IQA). We decompose the task of rating image quality into two subtasks, namely distortion identification and distortion-level estimation, and then combine the results of the two subtasks to obtain a final image quality score. Unlike conventional multitask convolutional networks, wherein only the early layers are shared and the subsequent layers are different for each subtask, our model shares almost all the layers by integrating a dictionary into the CNN. Moreover, it is trained in an end-to-end manner, and all the parameters, including the weights of the convolutional layers and the codewords of the dictionary, are simultaneously learned from the loss function. We test our method on widely used image quality databases and show that its performance is comparable with those of state-of-the-art general-purpose NR-IQA algorithms.