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Company: rafm
Date Added: June 19, 2013  |  Visits: 229


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HDR-VDP is a visual metric that compares a pair of images (a reference and a test image) and predicts:<br /><br /> * Visibility - what is the probability that the differences between the images are visible for an average observer;<br /> * Quality - what is the quality degradation with the respect to the reference image, expressed as a mean-opinion-score. <br /><br />The metric can be used for testing fidelity (e.g. how distracting are image compression distortions), or visibility (is the information sufficiently visible).<br /><br />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).<br /><br />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:<br /><br /> * 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.<br /> * The complete source code of the metric is available (but do not forget to cite us :-)<br /> * It produces separate predictions for visibility and quality. These two measures serve very different purpose and are not necessarily very well correlated.<br /> * Is extensively tested and calibrated against actual measurements (see Calibration datasets and reports) below to ensure the highest possible accuracy. <br /><br />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 high contrast vision, such as scattering of the light in the optics (OTF), the photoreceptor non-linear response to light, and local adaptation.

Requirements: No special requirements
Platforms: Mac, Mac OSX
Keyword: Advantages Calibrated Complete Correlated Display Extensively Forget Measures Necessarily Predictions Predictor Produces Separate Source Tested Unique
Users rating: 0/10

License: Freeware Size: 81.92 KB
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