Feature-segmentation strategy based convolutional neural network for no-reference image quality assessment

Abstract

Convolutional neural networks (CNN) have been shown to deliver outstanding performance for image quality assessment (IQA). Most CNN models are trained using small image patches with a fixed resolution of 32 × 32. However, more information of image content and human visual system should be taken into account. This paper proposes a post segmentation based CNN model for no-reference quality assessment without any pre-processing. The network consists of five convolutional layers with max pooling, one special fully connected layer feature-segmentation and one output layer. This paper adopts the feature-segmentation strategy to assure enough training data. We modify the structure of fully connected layer and regard every feature vector of the last pooling maps as an independent sample to train our proposed model. In this way, the raw images can be fed into the input and do not need to be split into patches, to avoid any hand-crafted features. Moreover, the size of the input images is not fixed, and the size of the extracted feature vector is invariant. Experiments on LIVE, CSIQ and TID2008 databases demonstrate that our approach has high consistency with the subjective evaluation scores. The experimental results show that the proposed network outperforms state-of-the-art no-reference IQA algorithms and is comparable to some full-reference IQA algorithms.