No-reference image quality assessment based on hybrid model |
| |
Authors: | Jie Li Jia Yan Dexiang Deng Wenxuan Shi Songfeng Deng |
| |
Affiliation: | 1.Electronic Information School,Wuhan University,Wuhan,China;2.School of Remote Sensing and Information Engineering,Wuhan University,Wuhan,China;3.Shanghai Aerospace Electronic Technology Institute,Minhang,China |
| |
Abstract: | The aim of research on the no-reference image quality assessment problem is to design models that can predict the quality of distorted images consistently with human visual perception. Due to the little prior knowledge of the images, it is still a difficult problem. This paper proposes a computational algorithm based on hybrid model to automatically extract vision perception features from raw image patches. Convolutional neural network (CNN) and support vector regression (SVR) are combined for this purpose. In the hybrid model, the CNN is trained as an efficient feature extractor, and the SVR performs as the regression operator. Extensive experiments demonstrate very competitive quality prediction performance of the proposed method. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|