Blind Image Quality Assessment with a Probabilistic Quality Representation

Hui Zeng, Lei Zhang, Alan C. Bovik

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

18 Citations (Scopus)

Abstract

Most existing blind image quality assessment (BIQA) methods learn a regression model to predict scalar quality scores. Such a scheme ignores the fact that an image will receive divergent subjective scores from different subjects, which cannot be adequately represented by a single scalar number. This is particularly true on complex, real-world distorted images. However, the more informative score distributions are unavailable in existing image quality assessment (IQA) databases and can be potentially noisy when limited number of opinions are collected on each image. This paper proposes a probabilistic quality representation (PQR) and employs a more robust loss function to train deep BIQA models. Using a very straightforward implementation, the proposed method is shown to not only speed up the convergence of deep model training, but also greatly improve the quality prediction accuracy relative to scalar quality score regression methods under the same setting. The source code is available at https://github.com/HuiZeng/BIQA-Toolbox.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PublisherIEEE Computer Society
Pages609-613
Number of pages5
ISBN (Electronic)9781479970612
DOIs
Publication statusPublished - 29 Aug 2018
Event25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
Duration: 7 Oct 201810 Oct 2018

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference25th IEEE International Conference on Image Processing, ICIP 2018
Country/TerritoryGreece
CityAthens
Period7/10/1810/10/18

Keywords

  • Image quality assessment
  • Image quality representation
  • Score distribution

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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