This is a selection of the major projects. A complete list of publications can be found here.
The new per-pixel image quality dataset with computer graphics artifacts shows disapointing performance of both simple (PSNR, MSE, sCIE-Lab) and advanced (SSIM, MS-SSIM, HDR-VDP-2) quality metrics.
Unlike standard LCD or Plasma displays, a high dynamic range (HDR) display is capable of showing images of very high contrast (up to 250,000:1) and peak brightness (up to 2,400 cd/m^2). As a result, the images and video shown on such as display look very realistic and appealing.
A metric for predicting visible differences (discrimination) and image quality (mean-opinion-score) in high dynamic range images. The metric is carefully calibrated and extensively tested against actual experimental data, ensuring the highest possible accuracy.
HDR images are captured in a single exposure with standard cameras by encoding information about the saturated pixels in a glare. The glare is produced by a cross-screen (star) filter, making it amenable to tomographic reconstruction.
Digital viewfinder in cameras does not show depth-of-field, lack of sharpness or motion blur, which is normally visible in a full-size image. Based on blur-matching experimental data, the algorithm produces lower resolution images while preserving apperent blur.
A method for displaying HDR images with exposure control in a web browser. The project page includes a toolkit for generating web pages with HDR images.
Color distortions due to tone-mapping are corrected by adjusting image color saturation. A model of color adjustment is built based on the data from an appearance-matching experiment.
A tone-mapping algorithm that is controlled by a super-threshold visual metric. The solution takes into account display limitations, such as brightness, black level, and reflected ambient light.
A visual metric that is mostly invariant to contrast and tone-scale manipulations. It can detect loss of visible contrast, amplification of invisible contrast and contrast reversal.
A semi-automatic method for classification of reflective and emissive objects in video, followed by brightness enhancement for display on high dynamic range displays.
A generic model of a local tone-mapping operator is fit to a pair of LDR and HDR images. The model is applied for backward compatible HDR image compression, analysis of tone-mapping operators and synthesis of new operators from existing ones.
The perceived brightness of the glare illusion is measured in a brightness matching experiment. A simple Gaussian convolution is shown to produce similar brightness boost as a physically correct PSF model of the eye optics.