Digital images have become omnipresent in modern life. The popularity and affordability of handheld imaging devices, especially smartphones, along with the rapid development of social media and social networking services such as Facebook, Flickr, and Instagram have made images a popular and integral part of everyday communication. With the development of visual media transmission systems, it is becoming increasingly important to improve visual applications in order to meet the quality expectations of end users. For this aim, it is important to evaluate the image quality directly from the user’s perspective, which is known as quality of experience.
During December 2017, I have visited Prof. Dietmar Saupe’s group at the University of Konstanz, Germany. In my first week I had the chance to give a talk about Image quality assessment based on visual perception as part of the Lecture Series “Visual Computing” of the SFB-TRR 161. During the month I was mainly working on the image quality assessment of differently distorted images.
Organized by the RWTH Aachen University, together with the researchers from RWTH Aachen University, Leibniz University of Hannover, University of Bristol, etc., the 3dt Summer School on Video Compression and Processing (SVCP) was held at Abdij Rolduc (Kerkrade, Netherlands) from 3th to 5th July. In this three days’ summer school, people in the area of video coding and processing exchanged knowledge and ideas.
During this spring, I spent two months at the Multimedia Signal Processing Group led by Prof. Dr. Dietmar Saupe at the University of Konstanz. During this stay I had the opportunity to meet many researchers who work in blind image and video quality assessment and pursue my research work.
Franz Hahn is a PhD student in the Multimedia Signal Processing Group of Prof. Dietmar Saupe at the University of Konstanz. During his project activities in the SFB-TRR 161 he is concerned with the topic of image and video quality assessment. Using Eye Tracking he aims to develop a predictive model that improves the quality of images by keeping the data size the same.
Humans are the end users of visual media. Therefore, in order to develop an effective quantitative assessment of visual computing quality, one must take into account how humans perceive visual quality. For example, in image compression, an adaptive bitrate allocation that favors the image foreground can be expected to increase the visual quality of decoded images.