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.

Supplementary Material

MP4 File (tp013_11.mp4)

Download
25.98 MB

References

[1]

Artal, P., and Navarro, R. 1994. Monochromatic modulation transfer function of the human eye for different pupil diameters: an analytical expression. J. Opt. Soc. Am. A 11, 1, 246--249.

[2]

Aydin, T. O., Mantiuk, R., Myszkowski, K., and Seidel, H.-P. 2008. Dynamic range independent image quality assessment. ACM Trans. on Graphics (SIGGRAPH'08) 27, 3, 69.

Digital Library

[3]

Aydin, T. O., Čadík, M., Myszkowski, K., and Seidel, H.-P. 2010. Video quality assessment for computer graphics applications. ACM Trans. Graph. 29, 161:1--161:12.

Digital Library

[4]

Barten, P. G. J. 1999. Contrast sensitivity of the human eye and its effects on image quality. SPIE Press.

[5]

Blackwell, H. 1946. Contrast thresholds of the human eye. Journal of the Optical Society of America 36, 11, 624--632.

[6]

CIE. 1951. In CIE Proceedings, vol. 1, 37.

[7]

Daly, S., Co, E., and Rochester, N. 1994. A visual model for optimizing the design of image processingalgorithms. In Proc. of IEEE ICIP, vol. 2, 16--20.

[8]

Daly, S. 1990. Application of a noise-adaptive contrast sensitivity function to image data compression. Optical Engineering 29, 08, 977--987.

[9]

Daly, S. 1993. Digital Images and Human Vision. MIT Press, ch. The Visible Differences Predictor: An Algorithm for the Assessment of Image Fidelity, 179--206.

Digital Library

[10]

De Valois, R., Albrecht, D., and Thorell, L. 1982. Spatial frequency selectivity of cells in macaque visual cortex. Vision Research 22, 5, 545--559.

[11]

Ferwerda, J., Pattanaik, S., Shirley, P., and Greenberg, D. 1996. A model of visual adaptation for realistic image synthesis. In Proc. of SIGGRAPH 96, 249--258.

Digital Library

[12]

Foley, J. 1994. Human luminance pattern-vision mechanisms: masking experiments require a new model. Journal of the Optical Society of America A 11, 6, 1710--1719.

[13]

Georgeson, M., and Georgeson, J. 1987. Facilitation and masking of briefly presented gratings: time-course and contrast dependence. Vision Research 27, 3, 369--379.

[14]

Georgeson, M. A., and Sullivan, G. D. 1975. Contrast constancy: deblurring in human vision by spatial frequency channels. J. Physiol. 252, 3 (Nov.), 627--656.

[15]

He, S., and MacLeod, D. 1998. Contrast-modulation flicker: Dynamics and spatial resolution of the light adaptation process. Vision Res 38, 7, 985--1000.

[16]

Hess, R., Sharpe, L., and Nordby, K. 1990. Night Vision: Basic, Clinical and Applied Aspects. Cambridge University Press.

[17]

Ijspeert, J., van den Berg, T., and Spekreijse, H. 1993. An improved mathematical description of the foveal visual point spread function with parameters for age, pupil size and pigmentation. Vision research 33, 1, 15--20.

[18]

ITU-R-BT.500-11, 2002. Methodology for the subjective assessment of the quality of television pictures.

[19]

Lee, B., Dacey, D., Smith, V., and Pokorny, J. 1999. Horizontal cells reveal cone type-specific adaptation in primate retina. Proceedings of the National Academy of Sciences of the United States of America 96, 25, 14611.

[20]

Lovell, P., Párraga, C., Troscianko, T., Ripamonti, C., and Tolhurst, D. 2006. Evaluation of a multiscale color model for visual difference prediction. ACM Transactions on Applied Perception (TAP) 3, 3, 155--178.

Digital Library

[21]

Lubin, J. 1995. A visual discrimination model for imaging system design and evaluation. World Scientific Publishing Company, 245.

[22]

Lukin, A. 2009. Improved Visible Differences Predictor Using a Complex Cortex Transform. International Conference on Computer Graphics and Vision (GraphiCon).

[23]

MacLeod, D., Williams, D., and Makous, W. 1992. A visual nonlinearity fed by single cones. Vision Res 32, 2, 347--63.

[24]

Mantiuk, R., Daly, S., Myszkowski, K., and Seidel, H. 2005. Predicting visible differences in high dynamic range images: model and its calibration. In Proc. SPIE, vol. 5666, 204--214.

[25]

Mantiuk, R., Daly, S., and Kerofsky, L. 2008. Display adaptive tone mapping. ACM Transactions on Graphics (Proc. of SIGGRAPH) 27, 3, 68.

Digital Library

[26]

Mantiuk, R., Rempel, A. G., and Heidrich, W. 2009. Display considerations for night and low-illumination viewing. In Proc. of APGV '09, 53--58.

Digital Library

[27]

Marimont, D., and Wandell, B. 1994. Matching color images: The effects of axial chromatic aberration. Journal of the Optical Society of America A 11, 12, 3113--3122.

[28]

Meese, T., and Summers, R. 2007. Area summation in human vision at and above detection threshold. Proceedings of the Royal Society B: Biological Sciences 274, 2891--2900.

[29]

Myszkowski, K., Rokita, P., and Tawara, T. 1999. Perceptually-informed accelerated rendering of high quality walkthrough sequences. Rendering Techniques 99, 5--18.

Digital Library

[30]

Pattanaik, S. N., Ferwerda, J. A., Fairchild, M. D., and Greenberg, D. P. 1998. A multiscale model of adaptation and spatial vision for realistic image display. In Proc. of SIGGRAPH'98, 287--298.

Digital Library

[31]

Pattanaik, S., Tumblin, J., Yee, H., and Greenberg, D. 2000. Time-dependent visual adaptation for realistic image display. In Proc. of SIGGRAPH'00, 47--54.

Digital Library

[32]

Ponomarenko, N., Battisti, F., Egiazarian, K., Astola, J., and Lukin, V. 2009. Metrics performance comparison for color image database. In 4th int. workshop on video processing and quality metrics for consumer electronics (QoMEX).

[33]

Ramanarayanan, G., Ferwerda, J., Walter, B., and Bala, K. 2007. Visual equivalence: towards a new standard for image fidelity. ACM Trans. on Graphics (SIGGRAPH'07), 76.

Digital Library

[34]

Ramasubramanian, M., Pattanaik, S. N., and Greenberg, D. P. 1999. A perceptually based physical error metric for realistic image synthesis. In Proc. of SIGGRAPH '99, 73--82.

Digital Library

[35]

Richter, T. 2009. On the mDCT-PSNR image quality index. In Quality of Multimedia Experience, 2009. QoMEx, 53--58.

[36]

Ritschel, T., Ihrke, M., Frisvad, J. R., Coppens, J., Myszkowski, K., and Seidel, H.-P. 2009. Temporal Glare: Real-Time Dynamic Simulation of the Scattering in the Human Eye. Computer Graphics Forum 28, 2, 183--192.

[37]

Rohaly, A., Ahumada Jr, A., and Watson, A. 1997. Object detection in natural backgrounds predicted by discrimination performance and models. Vision Research 37, 23, 3225--3235.

[38]

Rovamo, J., Kukkonen, H., and Mustonen, J. 1998. Foveal optical modulation transfer function of the human eye at various pupil sizes. Journal of the Optical Society of America A 15, 9, 2504--2513.

[39]

Sheikh, H., Sabir, M., and Bovik, A. 2006. A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms. IEEE Transactions on Image Processing 15, 11, 3440--3451.

Digital Library

[40]

Simoncelli, E., and Freeman, W. 2002. The steerable pyramid: a flexible architecture for multi-scale derivative computation. In Proceedings., International Conference on Image Processing, IEEE Comput. Soc. Press, vol. 3, 444--447.

Digital Library

[41]

Smith Jr, R., and Swift, D. 1985. Spatial-frequency masking and Birdsalls theorem. Journal of the Optical Society of America A 2, 9, 1593--1599.

[42]

Stockman, A., and Sharpe, L. 2000. The spectral sensitivities of the middle-and long-wavelength-sensitive cones derived from measurements in observers of known genotype. Vision Res 40, 13, 1711--1737.

[43]

Stromeyer, C. F., and Julesz, B. 1972. Spatial-Frequency Masking in Vision: Critical Bands and Spread of Masking. Journal of the Optical Society of America 62, 10 (Oct.), 1221.

[44]

van den Berg, T., IJspeert, J., and de Waard, P. 1991. Dependence of intraocular straylight on pigmentation and light transmission through the ocular wall. Vision Res 31, 7-8, 1361--7.

[45]

Vos, J., and van den Berg, T. 1999. Report on disability glare. CIE Research Note 135, 1.

[46]

Wang, Z., and Bovik, A. C. 2006. Modern Image Quality Assessment. Morgan & Claypool.

[47]

Wang, Z., Simoncelli, E., and Bovik, A. 2003. Multi-scale structural similarity for image quality assessment. In Asilomar Conference on Signals, Systems & Computers, 2003, 1398--1402.

[48]

Wang, Z., Bovik, A., Sheikh, H., and Simoncelli, E. 2004. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing 13, 4, 600--612.

Digital Library

[49]

Watson, A., and Ahumada Jr, A. 2005. A standard model for foveal detection of spatial contrast. Journal of Vision 5, 9, 717--740.

[50]

Watson, A., and Pelli, D. 1983. QUEST: A Bayesian adaptive psychometric method. Perception & Psychophysics 33, 2, 113--120.

[51]

Watson, A., and Solomon, J. 1997. Model of visual contrast gain control and pattern masking. Journal of the Optical Society of America A 14, 9, 2379--2391.

[52]

Watson, A. 1987. The cortex transform: Rapid computation of simulated neural images. Computer Vision, Graphics, and Image Processing 39, 3, 311--327.

Digital Library

[53]

Whitaker, D., Steen, R., and Elliott, D. 1993. Light scatter in the normal young, elderly, and cataractous eye demonstrates little wavelength dependency. Optometry and Vision Science 70, 11, 963--968.

Cited By

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
https://doi.org/10.1117/1.JEI.34.1.013008
Shen XPan LNi ZHe YYang WWang SKwong S(2025)Breaking Boundaries: Unifying Imaging and Compression for HDR Image CompressionIEEE Transactions on Image Processing10.1109/TIP.2025.352736534(510-521)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1109/TIP.2025.3527365
Liu JWang YLu SYu GWang XFeng P(2025)SAAChain: release and storage platform of digital works based on non-fungible tokensThe Computer Journal10.1093/comjnl/bxae104Online publication date: 18-Jan-2025
https://doi.org/10.1093/comjnl/bxae104
Show More Cited By

Index Terms

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

Recommendations

Comparison of single image HDR reconstruction methods — the caveats of quality assessment
SIGGRAPH '22: ACM SIGGRAPH 2022 Conference Proceedings

As the problem of reconstructing high dynamic range (HDR) images from a single exposure has attracted much research effort, it is essential to provide a robust protocol and clear guidelines on how to evaluate and compare new methods. In this work, we ...
HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions
SIGGRAPH '11: ACM SIGGRAPH 2011 papers

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 ...
Dataset and Metrics for Predicting Local Visible Differences

A large number of imaging and computer graphics applications require localized information on the visibility of image distortions. Existing image quality metrics are not suitable for this task as they provide a single quality value per image. Existing ...

Comments

Information & Contributors

Information

Published In

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

Issue’s Table of Contents

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 July 2011

Published in TOG Volume 30, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Engineering and Physical Sciences Research Council

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

610
Total Citations
View Citations
2,981
Total Downloads

Downloads (Last 12 months)189
Downloads (Last 6 weeks)13

Reflects downloads up to 26 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

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
https://doi.org/10.1117/1.JEI.34.1.013008
Shen XPan LNi ZHe YYang WWang SKwong S(2025)Breaking Boundaries: Unifying Imaging and Compression for HDR Image CompressionIEEE Transactions on Image Processing10.1109/TIP.2025.352736534(510-521)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1109/TIP.2025.3527365
Liu JWang YLu SYu GWang XFeng P(2025)SAAChain: release and storage platform of digital works based on non-fungible tokensThe Computer Journal10.1093/comjnl/bxae104Online publication date: 18-Jan-2025
https://doi.org/10.1093/comjnl/bxae104
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
Hsieh KWang C(2024)Optimal Weighted Modulus: A Secure and Large-Capacity Data-Hiding Algorithm for High Dynamic Range ImagesElectronics10.3390/electronics1301020713:1(207)Online publication date: 2-Jan-2024
https://doi.org/10.3390/electronics13010207
Lin HLin YLin WChang C(2024)Reconstructing High Dynamic Range Image from a Single Low Dynamic Range Image Using Histogram LearningApplied Sciences10.3390/app1421984714:21(9847)Online publication date: 28-Oct-2024
https://doi.org/10.3390/app14219847
Cui MWang ZWang DZhao BLi XWooldridge MDy JNatarajan S(2024)Color event enhanced single-exposure HDR imagingProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i2.27904(1399-1407)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i2.27904
Ashraf MMantiuk RChapiro AWuerger S(2024)castleCSF — A contrast sensitivity function of color, area, spatiotemporal frequency, luminance and eccentricityJournal of Vision10.1167/jov.24.4.524:4(5)Online publication date: 4-Apr-2024
https://doi.org/10.1167/jov.24.4.5
Cui JJiang WPeng ZPan ZCao ZCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Exposure Completing for Temporally Consistent Neural High Dynamic Range Video RenderingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680935(10027-10035)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680935
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents