What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
Rafal Mantiuk spends much of his time researching Computer vision, Artificial intelligence, High dynamic range, Luminance and Tone mapping.
His research is interdisciplinary, bridging the disciplines of Brightness and Computer vision.
Rafal Mantiuk conducts interdisciplinary study in the fields of Artificial intelligence and Contouring through his works.
His High dynamic range research includes themes of Image quality, Data compression and Computer graphics, Rendering.
His research integrates issues of Ranking and Data mining in his study of Image quality.
His studies deal with areas such as Image processing, Contrast and Human visual system model as well as Tone mapping.
His most cited work include:
- HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions (440 citations)
- Display adaptive tone mapping (319 citations)
- A perceptual framework for contrast processing of high dynamic range images (225 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Computer vision, High dynamic range, Luminance and Tone mapping.
His Artificial intelligence study frequently links to adjacent areas such as High-dynamic-range imaging.
His Computer vision research includes elements of Brightness and Computer graphics.
His High dynamic range research incorporates elements of Pixel and Iterative reconstruction.
He has included themes like Liquid-crystal display, Backlight, Human eye and Retargeting in his Luminance study.
His Tone mapping study integrates concerns from other disciplines, such as Image processing, High-dynamic-range video, Video processing and Human visual system model.
He most often published in these fields:
- Artificial intelligence (78.80%)
- Computer vision (69.57%)
- High dynamic range (43.48%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (78.80%)
- Computer vision (69.57%)
- High dynamic range (43.48%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Computer vision, High dynamic range, Luminance and Optics.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Pattern recognition.
His work on Rendering and Image resolution as part of general Computer vision research is often related to Visibility, thus linking different fields of science.
His High dynamic range research is included under the broader classification of Dynamic range.
His Luminance research integrates issues from sRGB, Gamut and Contrast.
His Image processing research is multidisciplinary, incorporating perspectives in Tone mapping and Probabilistic logic.
Between 2018 and 2021, his most popular works were:
- Near‐Eye Display and Tracking Technologies for Virtual and Augmented Reality (44 citations)
- Overview and evaluation of the JPEG XT HDR image compression standard (38 citations)
- Single-Frame Regularization for Temporally Stable CNNs (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
His scientific interests lie mostly in Artificial intelligence, Computer vision, Optics, Luminance and Chromatic scale.
His Artificial intelligence study frequently draws connections to adjacent fields such as Pattern recognition.
Rendering, Lossless JPEG, Image compression, JPEG File Interchange Format and Quantization are the primary areas of interest in his Computer vision study.
His work on High dynamic range, Gamut and High luminance as part of general Optics research is frequently linked to Melanopsin, thereby connecting diverse disciplines of science.
His research in High dynamic range intersects with topics in Visual optics, Light level and Chromatic contrast.
His study looks at the relationship between Luminance and fields such as Contrast, as well as how they intersect with chemical problems.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
