@article{NoRM_EG2012,
TITLE = {{NoRM}: {No-reference} Image Quality Metric for Realistic Image Synthesis},
AUTHOR = {Herzog, Robert and Cad{\'i}k, Martin and Aydin, Tunc Ozan and Kim, Kwang In and Myszkowski, Karol and Seidel, Hans-Peter},
LANGUAGE = {eng},
ISSN = {0167-7055},
DOI = {10.1111/j.1467-8659.2012.03055.x},
LOCALID = {Local-ID: 673028A8C798FD45C1257A47004B2978-NoRM_EG2012},
PUBLISHER = {Blackwell-Wiley},
ADDRESS = {Oxford},
YEAR = {2012},
DATE = {2012},
ABSTRACT = {Synthetically generating images and video frames of complex 3D scenes using <br>some photo-realistic rendering software is often prone to artifacts and <br>requires expert knowledge to tune the parameters. The manual work required for <br>detecting and preventing artifacts can be automated through objective quality <br>evaluation of synthetic images.<br>Most practical objective quality assessment methods of natural images rely on a <br>ground-truth reference, which is often not available in rendering applications. <br>While general purpose no-reference image quality assessment is a difficult <br>problem, we show in a subjective study that the performance of a dedicated <br>no-reference metric as presented in this paper can match the state-of-the-art <br>metrics that do require a reference. This level of predictive power is achieved <br>exploiting information about the underlying synthetic scene (e.g., 3D surfaces, <br>textures) instead<br>of merely considering color, and training our learning framework with typical <br>rendering artifacts. We show that our method successfully detects various <br>non-trivial types of artifacts such as noise and clamping bias due to <br>insufficient virtual point light sources, and shadow map discretization <br>artifacts. We also briefly discuss an inpainting method for automatic <br>correction of detected artifacts.},
JOURNAL = {Computer Graphics Forum (Proc. EUROGRAPHICS)},
VOLUME = {31},
NUMBER = {2},
PAGES = {545--554},
BOOKTITLE = {EUROGRAPHICS 2012},
EDITOR = {Cignoni, Paolo and Ertl, Thomas},
}