Signal, Image and Video Processing

, Volume 11, Issue 6, pp 985–992 | Cite as

No-reference image quality assessment based on hybrid model

  • Jie Li
  • Jia Yan
  • Dexiang Deng
  • Wenxuan Shi
  • Songfeng Deng
Original Paper


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.


No-reference image quality assessment Convolutional neural network Support vector regression Hybrid model Machine learning 



This work has been supported by Natural Science Foundation of China (No. 61501334).


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Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Jie Li
    • 1
  • Jia Yan
    • 1
  • Dexiang Deng
    • 1
  • Wenxuan Shi
    • 2
  • Songfeng Deng
    • 3
  1. 1.Electronic Information SchoolWuhan UniversityWuhanChina
  2. 2.School of Remote Sensing and Information EngineeringWuhan UniversityWuhanChina
  3. 3.Shanghai Aerospace Electronic Technology InstituteMinhangChina

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