Multiple Level Feature-Based Universal Blind Image Quality Assessment Model

Jongyoo Kim, Anh Duc Nguyen, Sewoong Ahn, Chong Luo, Sanghoon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

The direct use of a deep convolutional neural network (CNN) in no-reference image quality assessment (NR-IQA) usually struggles for a good performance due to a lack of training data, which can be alleviated by transfer learning. However, depending on the similarity between the source and target tasks, the final performance differs vastly. In particular, various kinds of distortion types exist in IQA, which requires different kinds of features to predict visual quality. In this paper, to make the transferred model robust to various distortion types, we propose a Multiple-level Feature-based Image Quality Assessor (MFIQA) which considers multiple levels of features simultaneously. Through rigorous experiments, we prove that MFIQA consistently yields state-of-the-art performance regardless of the distortion types including synthetic and authentic corruption.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PublisherIEEE Computer Society
Pages291-295
Number of pages5
ISBN (Electronic)9781479970612
DOIs
Publication statusPublished - 2018 Aug 29
Event25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
Duration: 2018 Oct 72018 Oct 10

Publication series

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

Conference

Conference25th IEEE International Conference on Image Processing, ICIP 2018
CountryGreece
CityAthens
Period18/10/718/10/10

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Kim, J., Nguyen, A. D., Ahn, S., Luo, C., & Lee, S. (2018). Multiple Level Feature-Based Universal Blind Image Quality Assessment Model. In 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings (pp. 291-295). [8451346] (Proceedings - International Conference on Image Processing, ICIP). IEEE Computer Society. https://doi.org/10.1109/ICIP.2018.8451346