Research Background and Competence

Being the part of Master of Science in Media Technology at Gjøvik University College and a student at “The Norwegian Colour and Visual Computing Laboratory”, I have developed interest in the field of color imaging especially human perceptual image quality. Before graduating as Electronic and Information engineer and prior to my enrolment into the Master in Media Technology degree, I had an internship in an electronic company as a software & hardware engineer in China. Working with such company has increased my competence in software and hardware engineering, enhancing my coding skills and quantitative aptitude.

As a Master in Media Technology student, I have completed my studies in Gjøvik University College and The Norwegian Colour and Visual Computing Laboratory which I have specialized in Color Science, Image Processing, Advanced Video Processing, Human Vision and Computer Vision, Color Image Quality, Human Visual System and Perceptual Image

Quality. My last three semesters were completed in The Norwegian Colour and Visual Computing Laboratory where I had the opportunity of studying Advanced topics in Color Imaging, Content Based Indexing & Retrieval, and Color Image Quality & Processing in an Imaging Workflow, meanwhile gained numerous both theoretical and practical lab experiences.

During my master degree, I have worked on various projects individually and also with other peers having different competencies. This has contributed to the improvement of my ability to excel individually and in teams. I have worked on the projects “Instrumental Color Measurement” and “Video Concatenation and Title Credits Production” during my first

semester. In the second semester I have worked on the projects “Integration Marketing Strategy: Management and Innovation” and “Lossy Compression Encoder Based on JPEG Standard”. From my third semester I started working in the field of video processing, color imaging and image quality such as the projects “Intelligent Video event detection for Surveillance Systems”, “Image Quality Improvement in Compressed Domain Based on JPEG Compression Standard”, and “Methods of Image Detail Visibility simulation: Survey and Perceptual Evaluation”. Here I would like to emphasis on few highlight specialized projects.

In the project “Content Based Image Retrieval System” which was supervised by Associate Professor Sule Yildirim Yayilgan, our team firstly developed a system to retrieve images based on the contend and then analyzed the performance of system based on semantic tag query and feature based image matching.

Having studied Selected Topics in Color Imaging I worked on a study “Fog Removal algorithms: Survey and Perceptual Evaluation” under the supervision of Professor Jon Yngve Hardeberg. In this study, after surveyed a lot of existing fog removal methods I selected three state-of-the-art fog removal algorithms to evaluate. Both objective and subjective evaluation approaches are applied and this has never been done in the research field. The innovations of this study open a new domain for assessing for removal algorithms. This study has been extended to a conference paper and accepted as a poster presentation at “4th European Workshop in Visual Information Processing (EUVIP2013)”, IEEE, Paris, France, and June 2013.

Then I worked with Associate Professor Marius Pedersen on a modern work “Improved Simulation of Image Detail Visibility using the Non-Sunsampled Contourlet Transform”. This work is an improvement from a previous work but using a new transformation method. The work was aimed at simulating Human Visual System (HVS) how to recognize the details

in images. The experiment results proved that the new method is promising. The achievement of this work has been written to a conference paper and presented in “Color and Imaging Conference, Society for Imaging Science and Technology”, Albuquerque, New Mexico, USA, and November 2013.

Recently, I just finished my master thesis in “The Norwegian Color and Visual Computing Laboratory”. My task is to design and develop a new image quality database for evaluating image quality metrics. There are three main innovations in this work: (1) brand new reference image selection and assessment guidelines; (2) two gamut mapping distortion types; (3) well controlled experimental viewing conditions especially two different viewing distances. The so called CID:IQ (Color Lab Database: Image Quality) database will be available to the image quality research field. The use of this database for the purposes of academic and research is free of charge. All researchers can use this database to test their own image quality metrics.