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Publication - Professor David Bull

    BLIND HIGH DYNAMIC RANGE IMAGE QUALITY ASSESSMENT USING DEEP LEARNING

    Citation

    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). Institute of Electrical and Electronics Engineers (IEEE)

    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.

    Full details in the University publications repository