Video Quality Assessment for Concurrent Multipath Transfer

The visual quality of images and videos is typically assessed by human experts and yields mean opinion scores (MOS). To reduce cost and time, providing methods for automated image/video quality assessment (IQA/VQA) is desirable for the multimedia and signal processing community at large. Current VQA methods are designed solely to capture aspects of the technical quality of displayed video streams. In addition to such visual quality we aim at methods to characterize images and videos in terms of other perceptual aspects. These aspects include the number and magnitude of eye movements required for viewing the content, the viewers appreciation of the use of color, and the degree of interestingness. Several such components together with visual quality are combined for an overall ration of perceptual quality. Moreover, by investigating the human perceptual process and by understanding psychophysical phenomena, a saliency model will be developed that is based on a Markov model of eye movements. Additionally, we will bring the state-of-the art a step closer to reality by setting up and applying media databases of authentic distortions and diverse content, which stand in contrast to current scientific data sets containing only a small variety of content and artificial distortions. Given an image or video whose visual quality is to be assessed, the question arises as to which IQA/VQA algorithm should be applied. Instead of choosing an algorithm based on a fixed test database, it can be assumed that a better quality assessment is possible when choosing an algorithm based on a particular test database consisting only of images/videos similar to the query image/video.