Blind Stereoscopic Image Quality Assessment Based on Hierarchical Learning

IEEE Access

Tsung-Jung Liu¹  Ching-Ti Lin¹  Hsin-Hua Liu²  Soo-Chang Pei² 

[1] Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan;Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan;

关键词: Hierarchical learning;    image quality assessment;    no reference;    stereoscopic images;   

DOI  :  10.1109/ACCESS.2018.2890304

来源: DOAJ

【 摘 要 】

We proposed a blind image quality assessment model which used classification and prediction for three-dimensional (3D) image quality assessment (denoted as CAP-3DIQA) that can automatically evaluate the quality of stereoscopic images. First, in the classification stage, the model separated the distorted images into several subsets according to the types of image distortions. This process will assign the images with the same distortion type to the same group. After the classification stage, the classified distorted image set is fed into the image quality predictor that contains five different perceptual channels which predict the image quality score individually. Finally, we used the regression module of the support vector machine to evaluate the final image quality score, where the input of the regression model is the combination of five channel’s outputs. The model, we proposed is tested on three public and popular databases, which are LIVE 3D Image Quality Database Phase I, LIVE 3D Image Quality Database Phase II, and MCL 3D Image Quality Database. The experimental results show that our proposed model leads to significant performance improvement on quality prediction for stereoscopic images compared with other existing state-of-the-art quality metrics.

【 授权许可】

Unknown