ESR2 – Altynay Karydova

Altynay Kadyrova

Country of Origin: Kazakhstan
Host Institution: NTNU

Background:

Altynay finished school with golden medal award and after graduated from Kazakh-British Technical University with the major in Information Systems. During her studies, she did several professional internships in PwC, KPMG companies in tax and legal services department as well as volunteering internships in Estonia and Indonesia. After her Bachelors degree, she was employed by Hewlett Packard (HP) company where she worked in communication field (servers, storage and routers). During HP, Altynay got the opportunity to do research in South Korea and she decided to go to Korea and did research about Artificial Intelligence, Noise Communication, Cognitive Radio Sensor Networks there. Then she started her Masters in Colour Science. During her Masters, she did summer internship in Olympus Corporation in Tokyo on Deep Learning based project. Her master thesis project was about color quality control of liquid foundations which she did in L’Oreal in Paris.

Aspirations within projects:

Nowadays, it is becoming vital to model HVS workflow in many industries. Also, one of her research interests is human perception and with the help of this project she is motivated to do deeper research on HVS and its role in material reproduction. To create relevant IQM with HVS model and guidelines for subjective and objective quality assessment are the main outcomes of this project. Thus, she would like to contribute into the research field.

ESR2: Quality assessment for material appearance

Main Supervisor:

Co-Supervisor(s):

Objectives

Develop more meaningful and useful image quality metrics that better estimate how good material appearance (2.5D printed images) serve as visual representations of the objects they depict.

  • Establish guidelines for subjective and objective evaluation of material reproduction.
  • Modelling the human visual system for material reproduction.
  • Estimating perceptual image quality by using objective image quality metrics based on the human visual system for material reproduction.