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

Joss Whittle, Mark W. Jones

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.

Source Materials

PDF iconAuthor pre-print
MP4 iconJoss' conference presentation
PDF iconPresentation slides

DOI

10.2312/cgvc.20181204
https://dx.doi.org/10.2312/cgvc.20181204

Citation

Joss Whittle, Mark W. Jones. A Deep Learning Approach to No-Reference Image Quality Assessment For Monte Carlo Rendered Images. In Proceedings Computer Graphics and Visual Computing (CGVC) 2018. pp. 23-31 doi:10.2312/cgvc.20181204

Bibtex

@INPROCEEDINGS{MC_IQA_Deep_ML, 
author={Joss Whittle and Mark W. Jones}, 
booktitle={Computer Graphics and Visual Computing (CGVC) 2018}, 
title={A Deep Learning Approach to No-Reference Image Quality Assessment For Monte Carlo Rendered Images}, 
year={2018}, 
pages={23--31}, 
DOI = {10.2312/cgvc.20181204},
month={Sept},}