For the source code as well as the latest and most up-to-date information,
Sourceforge home page.
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
both 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.
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 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 consistent
viewing conditions for a large variety of background luminances
and spatial frequencies.
The new metric models L-, M-, and 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 characteristics.
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 a stimuli size.
The previous version of the HDR-VDP can be still found at the
and in the SourceForge file archive.
Last modified: Tue Jun 21 18:49:15 PDT 2011