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    A Deep Learning Approach to No-Reference Image Quality Assessment For Monte Carlo Rendered Images

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    Date
    2018
    Author
    Whittle, Joss
    Jones, Mark W.
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    Abstract
    In Full-Reference Image Quality Assessment (FR-IQA) images are compared with ground truth images that are known to be of high visual quality. These metrics are utilized in order to rank algorithms under test on their image quality performance. Throughout the progress of Monte Carlo rendering processes we often wish to determine whether images being rendered are of sufficient visual quality, without the availability of a ground truth image. In such cases FR-IQA metrics are not applicable and we instead must utilise No-Reference Image Quality Assessment (NR-IQA) measures to make predictions about the perceived quality of unconverged images. In this work we propose a deep learning approach to NR-IQA, trained specifically on noise from Monte Carlo rendering processes, which significantly outperforms existing NR-IQA methods and can produce quality predictions consistent with FR-IQA measures that have access to ground truth images.
    BibTeX
    @inproceedings {handle:10.2312:cgvc20181204,
    booktitle = {Computer Graphics and Visual Computing (CGVC)},
    editor = {{Tam, Gary K. L. and Vidal, Franck},
    title = {{A Deep Learning Approach to No-Reference Image Quality Assessment For Monte Carlo Rendered Images}},
    author = {Whittle, Joss and Jones, Mark W.},
    year = {2018},
    publisher = {The Eurographics Association},
    ISBN = {978-3-03868-071-0},
    DOI = {10.2312/cgvc.20181204}
    }
    URI
    https://diglib.eg.org:443/handle/10.2312/cgvc20181204
    https://doi.org/10.2312/cgvc.20181204
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    • Computer Graphics & Visual Computing (CGVC) 2018

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