关键词: 无参考立体图像质量评价, 卷积神经网络, 视差图

Abstract: This paper proposes a no-reference stereoscopic image quality assessment (SIQA) algorithm based on deep convolution neural network (CNN). The model uses three channels as input, namely left view, right view and their disparity map of the raw color image patches. Each channel consists of a 12-layer depth network, and the local natural scene statistical features of the stereoscopic characteristics are gained by the multi-layer stacking of the convolution layer and the max pool. Finally, the feature vector learned from 3-channels is linearly combined, and the quality score is obtained by the regression of full connection layer. The experimental results on the LIVE 3D Phase I database show that the proposed method is superior to those reported in the JP2K, WN and FF distortion types, and has a better subjective perception consistency.

Key words: no-reference stereoscopic image quality assessment, convolution neural network, disparity map