Blind 3D Image Quality Assessment Based on Multi-scale Feature Learning

3D image quality assessment (3D-IQA) plays an important role in 3D multimedia applications. In recent years, convolutional neural networks (CNN) have been widely used in various images processing tasks and achieve excellent performance. In this paper, we propose a blind 3D-IQA metric based on multi-scale feature learning by using multi-column convolutional neural networks (3D-IQA-MCNN). To address the problem of limited 3D-IQA dataset size, we take patches from the left image and right image as input and use the full-reference (FR) IQA metric to approximate a reference ground-truth for training the 3D-IQA-MCNN. Then we put the patches from left image and right image into the pre-trained 3D-IQA-MCNN and obtain two quality feature vectors based on multi-scale. Finally, by regressing the quality feature vectors onto the subjective mean opinion score (MOS), the visual quality of 3D images is predicted. Experimental results show that the proposed method achieves high consistency with human subjective assessment and outperforms several state-of-the-art 3D-IQA methods.