NDLI: HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions

Content Provider	ACM Digital Library
Author	Mantiuk, Rafat Rempel, Allan G. Kim, Kil Joong Heidrich, Wolfgang
Copyright Year	2011
Abstract	Visual metrics can play an important role in the evaluation of novel lighting, rendering, and imaging algorithms. Unfortunately, current metrics only work well for narrow intensity ranges, and do not correlate well with experimental data outside these ranges. To address these issues, we propose a visual metric for predicting visibility (discrimination) and quality (mean-opinion-score). The metric is based on a new visual model for all luminance conditions, which has been derived from new contrast sensitivity measurements. The model is calibrated and validated against several contrast discrimination data sets, and image quality databases (LIVE and TID2008). The visibility metric is shown to provide much improved predictions as compared to the original HDR-VDP and VDP metrics, especially for low luminance conditions. The image quality predictions are comparable to or better than for the MS-SSIM, which is considered one of the most successful quality metrics. The code of the proposed metric is available on-line.
Starting Page	1
Ending Page	14
Page Count	14
File Format	PDF
ISSN	07300301
e-ISSN	15577368
DOI	10.1145/2010324.1964935
Volume Number	30
Issue Number	4
Journal	ACM Transactions on Graphics (TOG)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-07-25
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	High dynamic range Image quality Visual metric Visual model Visual perception
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design

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