Deep Networks for Perceptual Image Quality Assessment for IRYStec Perceptual Display Platform

As most imagery is ultimately displayed to humans via physical display panels under a variety of viewing conditions, it is important to evaluate the quality of perceived imagery in addition to the quality of digital imagery alone. Conventional Image Quality Assessment (IQA) methods typically do not consider the variety of viewing conditions and make generalized assumptions about the viewer. The currently proposed research thus intends to define a method to evaluate the quality of displayed imagery, while considering various viewing conditions and the characteristics of the viewer.

Image Enhancement for Color Deficient People

I have been recently graduated from the School of Electrical Engineering and Computer Science of University of Ottawa. I did my PhD in Image Color Processing, and I found out the available position with Irystec Company is fit to my knowledge and experience. As a PhD candidate, I had the opportunity to gain extensive experience in Image Processing, Computer Vision and Machine Learning fields. Conducting research on the image processing subject as my
PhD thesis topic, elevate my knowledge in design, coding, and testing. My M.Sc.

Color management for ultra-bright conditions in OLED Displays

The objective of the proposed research project is to develop a realistic color appearance model based on the human visual systemÂ’s functioning that addresses the issue of reflections under high luminance levels. This will be incorporated into algorithms used in the color reproduction and retargeting algorithms of OLED displays. This model should give rise to reduced power consumption in OLED displays, while maintaining a high perceived quality of images. The project will also explore the effect of display reflections from OLED screens.