Blind Image Quality Assessment via Vector Regression and Object Oriented Pooling

Jie Gu, Gaofeng Meng, Judith A. Redi, Shiming Xiang, Chunhong Pan

Research output: Contribution to journalArticleScientificpeer-review

34 Citations (Scopus)


This paper presents an effective method based on Vector Regression and Object oriented Pooling (VROP) for blind image quality assessment (BIQA). Unlike previous models which map the extracted features directly to a quality score, the proposed vector regression framework yields a vector of belief scores for the input image. We explore the uncertainty factors in quality assessment and design the belief scores to measure the confidences of an image to be assigned to the corresponding quality grades. Moreover, we propose an object oriented pooling strategy to further improve the performance by incorporating semantic information of image contents. According to this strategy, regions occupied by objects will be assigned more weights in the pooling phase, leading to a more accurate quality assessment. Extensive experiments on benchmark datasets demonstrate that our approach achieves state-of-the-art performance and shows a great generalization ability.

Original languageEnglish
Pages (from-to)1140-1153
Number of pages14
JournalIEEE Transactions on Multimedia
Issue number5
Publication statusPublished - 2018


  • convolutional neural network
  • Feature extraction
  • Image quality
  • Image quality assessment
  • Neural networks
  • Object detection
  • Object oriented modeling
  • object oriented pooling
  • perceptual image quality
  • Proposals
  • Quality assessment
  • vector regression


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