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Conference contribution 161 views

A Deep Learning Approach to No-Reference Image Quality Assessment For Monte Carlo Rendered Images / Joss Whittle; Mark W. Jones

Computer Graphics and Visual Computing (CGVC) 2018

Swansea University Author: Jones, Mark

  • Accepted Manuscript under embargo until: 13th September 2019

DOI (Published version): 10.2312/cgvc.20181204

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 process...

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Published in: Computer Graphics and Visual Computing (CGVC) 2018
Published: 2018
URI: https://cronfa.swan.ac.uk/Record/cronfa43567
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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.
College: College of Science