No-reference image quality assessment based on hybrid model
作者: Jie LiJia YanDexiang DengWenxuan ShiSongfeng Deng
作者单位: 1Wuhan University
2Shanghai Aerospace Electronic Technology Institute
刊名: Signal, Image and Video Processing, 2017, Vol.11 (6), pp.985-992
来源数据库: Springer Nature Journal
DOI: 10.1007/s11760-016-1048-5
关键词: No-reference image quality assessmentConvolutional neural networkSupport vector regressionHybrid modelMachine learning
原始语种摘要: 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.
全文获取路径: Springer Nature  (合作)
影响因子:0.409 (2012)

  • image 
  • reference 基准电压源
  • model 模型
  • assessment 评价
  • quality 品质
  • feature 结构元件
  • computational 计算的
  • perception 知觉
  • network 网络
  • vision 视力