research-article

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

Authors:

Rafał Mantiuk,

Allan G. Rempel,

Wolfgang HeidrichAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 30, Issue 4

Article No.: 40, Pages 1 - 14

https://doi.org/10.1145/2010324.1964935

Published: 25 July 2011 Publication History

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.

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Index Terms

HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions
1. Computing methodologies
  1. Computer graphics

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 30, Issue 4

July 2011

829 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2010324

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Copyright © 2011 ACM.

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Publication History

Published: 25 July 2011

Published in TOG Volume 30, Issue 4

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Liu DLi XLi SChen YWang XMa J(2025)Full-reference light field image quality assessment based on multi-feature interactive fusion networkJournal of Electronic Imaging10.1117/1.JEI.34.1.01300834:01Online publication date: 1-Jan-2025
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Liu YTian YWang SZhang XKwong S(2024)Overview of High-Dynamic-Range Image Quality AssessmentJournal of Imaging10.3390/jimaging1010024310:10(243)Online publication date: 27-Sep-2024
https://doi.org/10.3390/jimaging10100243
Jamil S(2024)Review of Image Quality Assessment Methods for Compressed ImagesJournal of Imaging10.3390/jimaging1005011310:5(113)Online publication date: 8-May-2024
https://doi.org/10.3390/jimaging10050113
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