The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions
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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Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Multiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2004) · The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003 · doi:10.1109/acssc.2003.1292216 · Cited by 6,253
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
Sheikh, H. R., Sabir, M. F., & Bovik, A. C. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881959 · Cited by 2,966
Quest: A Bayesian adaptive psychometric method
Watson, A. B., & Pelli, D. G. (1983) · Perception & Psychophysics · doi:10.3758/bf03202828 · Cited by 2,410
Spatial frequency selectivity of cells in macaque visual cortex
De Valois, R. L., Albrecht, D. G., & Thorell, L. G. (1982) · Vision Research · doi:10.1016/0042-6989(82)90113-4 · Cited by 1,489
Modern Image Quality Assessment
Wang, Z., & Bovik, A. C. (2006) · Synthesis Lectures on Image, Video, and Multimedia Processing · doi:10.1007/978-3-031-02238-8 · Cited by 967
Stockman, A., & Sharpe, L. T. (2000) · Vision Research · doi:10.1016/s0042-6989(00)00021-3 · Cited by 878
Contrast Thresholds of the Human Eye
Blackwell, H. R. (1946) · Journal of the Optical Society of America · doi:10.1364/josa.36.000624 · Cited by 869
Contrast Sensitivity of the Human Eye and Its Effects on Image Quality
Barten, P. G. (1999) · SPIE eBooks · doi:10.1117/3.353254 · Cited by 808
Digital images and human vision
(1994) · Choice Reviews Online · doi:10.5860/choice.31-5492 · Cited by 656
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Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00068 · Cited by 14,623
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Yan, Q., Gong, D., Shi, Q., van den Hengel, A., Shen, C., Reid, I., & Zhang, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00185 · Cited by 313
QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey
Barakabitze, A. A., Barman, N., Ahmad, A., Zadtootaghaj, S., Sun, L., Martini, M. G., & Atzori, L. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2958784 · Cited by 239
Metadata
- Authors
- Mantiuk, R., Kim, K. J., Rempel, A. G., & Heidrich, W.
- Venue
- ACM Transactions on Graphics
- Published
- 2011-07
- Volume
- 30
- Issue
- 4
- Pages
- 1-14
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W3138218344
- Cited by
- 685
- References
- 39
- FWCI
- 16.91
- Type
- journal article
- Language
- en
- Open access
- closed
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- Mantiuk, R., Kim, K. J., Rempel, A. G., & Heidrich, W. (2011). HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions. ACM Transactions on Graphics. https://doi.org/10.1145/2010324.1964935
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