HDR-VDP is a visual metric that compares a pair of images (a reference and a test image) and predicts:
- Visibility - what is the probability that the differences between the images are visible for an average observer;
- Quality - what is the quality degradation with the respect to the reference image, expressed as a mean-opinion-score.
The metric can be used for testing fidelity (e.g. how distracting are image compression distortions), or visibility (is the information sufficiently visible).
The image on the right shows how two input images, a reference image (upper left) and a distorted image (lower left), are processed with the HDR-VDP-2 to produce a probability of detection map, overall probability value Pdet (from 0 to 1), and a quality predictor QMOS (from 0 to 100). The probability of detection map tells how likely we will notice a difference between the two images. Red color denotes high probability, green - low probability. As the distortion is an interleaved pattern of noise and blur, the highest probability of detection is either in the flat areas (for noise) or in the high contrast areas (for blur).
Although there are dozens of visible difference metrics that serve a similar purpose, the HDR-VDP-2 (Visual Difference Predictor for HDR images) has several unique advantages:
- It works with a full range of luminance values that can be found in the real-world (HDR images), not only for the luminance range that can be shown on a standard display.
- The complete source code of the metric is available (but do not forget to cite us :-)
- It produces separate predictions for visibility and quality. These two measures serve very different purpose and are not necessarily very well correlated.
- Is extensively tested and calibrated against actual measurements (see Calibration datasets and reports below) to ensure the highest possible accuracy.
The HDR-VDP-2 works within the complete range of luminance the human eye can see. An input to the metric is a pair high dynamic range (HDR) images, or a pair ordinary 8-bits-per-color images, converted to the actual luminance values assuming a certain display model. The proposed metric takes into account the aspects of that are relevant for viewing high contrast stimuli, such as scattering of the light in the optics (OTF), the photoreceptor non-linear response to light, and local adaptation.
What is new in HDR-VDP-2
HDR-VDP-2 is a major revision of the original HDR-VDP. The entire architecture of the metric and the visual model have been changed to improve the accuracy of the predictions. The most important changes are:
- The metric predicts both visibility (detection/discrimination) and image quality (mean-opinion-score).
- The metric is based on new CSF measurements, made in the consistent viewing conditions for a large variety of background luminance and spatial frequencies.
- The new metric models L-, M-, S-cone and rod sensitivities and is sensitive to different spectral characteristics of the incoming light.
- Photoreceptor light sensitivity is modelled separately for cones and rods, though L- and M- cones share the same characteristic.
- The intra-ocular light scatter function (glare) has been improved by fitting to the experimental data.
- The metric uses a steerable pyramid rather than cortex transform to decompose image into spatially- and orientation-selective bands. Steerable filter introduces less ringing and in the general case is computationally more efficient.
- The new model of contrast masking introduces inter-band masking and the effect of CSF flattening.
- A simple spatial-integration formula using probability summation is used to account for the effect of stimuli size.
The previous version of the HDR-VDP can be still found at the MPI web-pages and in the SourceForge file archive.
- 28 January 2013 - We published a paper with the details of the CSF measurements (see [Kim et al. 2013] below).
- 30 August 2011 - HDR-VDP-2.1.1 released
This is a minor release that fixes the equation for the CSF, which was inconsistent with the paper. New parameter values are provided for the fixed CSF.
- 17 June 2011 - HDR-VDP-2.1 released
Revision 2.1 fixes an important bug that caused the nCSF to remain fixed below 1 cd/m^2. To extend the operational dynamic range, the CSF was measured at additional luminance level of 0.002 cd/m^2. The CSF was also measured for all observers, resulting in a more accurate CSF function fit. The predictions are improved for majority of datasets.
- 10 May 2011 - HDR-VDP-2.0 matlab code available for download.
- 28 April 2011 - Together with the release of HDR-VDP-2, the project home page was moved to Trac wiki. Wiki will hopefully be easier to maintain.
- 30 March 2011 - Our paper on HDR-VDP-2 (see Literature below) has been accepted for SIGGRAPH 2011 conference in Vancouver. This is the highest esteem conference in computer graphics. Many thanks for the entire team who worked on the project and for those who participated in the experiments.
After installing HDR-VDP-2 check the documentation for the hdrvdp matlab function ("doc hdrvdp" in matlab). Make also sure to check the Frequently Asked Questions.
If you find the metric useful, please cite the paper below and include the version number, for example "HDR-VDP-2.1.3 [Mantiuk et al., 2013]". The version number should be included in order to make sure that your results can be reproduced. As new data sets become available, we will be updating the HDR-VDP-2 code and its calibration parameters and releasing new versions, but the older version will still be available for download. The HDR-VDP-2 version can be queried by calling the function hdrvdp_version. It will return a fractional number, such as 2.13, which should be interpreted as release 2.1.3.
- HDR-VDP-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions
- Rafał Mantiuk, Kil Joong Kim, Allan G. Rempel and Wolfgang Heidrich.
- In: ACM Transactions on Graphics (Proc. of SIGGRAPH'11), 30(4), article no. 40, 2011
- DOI 10.1145/1964921.1964935 pre-print PDF
- SIGGRAPH presentation slides
- 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.
The details on the CSF measurements can be found in:
- Measurements of achromatic and chromatic contrast sensitivity functions for an extended range of adaptation luminance
- Kil Joong Kim, Rafał Mantiuk, Kyoung Ho Lee.
- In: Proc. of Human Vision and Electronic Imaging XVIII, IS&T/SPIE's Symposium on Electronic Imaging, article no. 8651-47, 2013
- pre-print PDF
Help and support
If you have a question or would like to report a problem with the HDR-VDP-2, you can post your question on the HDR-VDP discussion group. Note that the group is moderated because of the large amount of SPAM, so that you may need to wait a day or two before your post appears on the group.
If you represent a company, we encourage that you enquire about about a consulting service arrangement by e-mailing [email@example.com]. Such an arrangement will ensure confidentially and much broader form of support, including an advise on the best use of the metric in a particular application, customizations as well as custom calibration and testing. We are also looking for the sponsors of studentships (Msc and Phd), which is a longer-term but also more cost effective form of technology transfer, especially for UK-based companies.
The current version of the HDR-VDP-2 is available as a matlab code and can be downloaded from SourceForge.
HDR-VDP-2 matlab code requires Eero Simoncelli's matlabPyrTools, which can be downloaded from here. Note that version 1.4 (2009-12-17) of that toolbox contains a bug that prevents HDR-VDP-2 from running. A fixed version of the toolbox can be downloaded from here, or alternatively a MatlabPyrTools patch can be applied to the original toolbox sources.
Calibration datasets and reports
The great care was taken to calibrate the HDR-VDP-2 with the experimental data. Check the calibration reports for the current release of the metric.
We plan to release selected data sets so that others can benchmark their metrics against HDR-VDP-2. The data sets will be available in the next few months. Send an e-mail to me in case you need these data sets sooner.