BLIND HIGH DYNAMIC RANGE IMAGE QUALITY ASSESSMENT USING DEEP LEARNING

Sen Jia, Yang Zhang, Dimitris Agrafiotis, David Bull

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

11 Citations (Scopus)
139 Downloads (Pure)

Abstract

In this paper we propose a No-Reference Image Quality Assessment (NR-IQA) method on High Dynamic Range (HDR) images by combining deep Convolutional Neural Networks (CNNs) with saliency maps. The proposed method utilises the power of deep CNN architectures to extract quality features which can be applied cross HDR and Standard Dynamic Range (SDR) domains. To introduce human visual system to CNNs, a saliency map algorithm is used to select a subset of salient image patches to evaluate on. Our CNN-based method delivers a state-of-the-art performance in HDR NR-IQA experiment, competitive with full reference IQA methods.
Original languageEnglish
Title of host publicationIEEE International Conference on Image Processing (ICIP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)9781509021758
ISBN (Print)9781509021765
DOIs
Publication statusPublished - 22 Feb 2018

Publication series

NameIEEE International Conference on Image Processing (ICIP)
PublisherIEEE
ISSN (Electronic)2381-8549

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  • Cite this

    Jia, S., Zhang, Y., Agrafiotis, D., & Bull, D. (2018). BLIND HIGH DYNAMIC RANGE IMAGE QUALITY ASSESSMENT USING DEEP LEARNING. In IEEE International Conference on Image Processing (ICIP) (IEEE International Conference on Image Processing (ICIP)). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICIP.2017.8296384