Publications

Adaptive streaming is fast becoming the most widely used method for video delivery to the end users over the internet. The ITU-T P.1203 standard is the first standardized quality of experience model for audiovisual HTTP-based adaptive streaming. This recommendation has been trained and validated for H.264 and resolutions up to and including full-HD. The paper provides an extension for the existing standardized short-term video quality model mode 0 for new codecs i.e., H.265, VP9 and AV1 and resolutions larger than full-HD (e.g. UHD-1). The extension is based on two subjective video quality tests. In the tests, in total 13 different source contents of 10 seconds each were used. These sources were encoded with resolutions ranging from 360p to 2160p and various quality levels using the H.265, VP9 and AV1 codecs. The subjective results from the two tests were then used to derive a mapping/correction function for P.1203.1 to handle new codecs and resolutions. It should be noted that the standardized model was not re-trained with the new subjective data, instead only a mapping/correction function was derived from the two subjective test results so as to extend the existing standard to the new codecs and resolutions.

The quality of images and videos is usually examined with well established subjective tests or instrumental models. These often target content transmitted over the internet, such as streaming or videoconferences and address the human preferential experience. In the area of high-quality motion pictures, however, other factors are relevant. These mostly are not error-related but aimed at the creative image design, which has gained comparatively little attention in image and video quality research. To determine the perceptual dimensions underlying movie-type video quality, we combine sensory evaluation techniques extensively used in food assessment - Degree of Difference test and Free Choice Profiling - with more classical video quality tests. The main goal of this research is to analyze the suitability of sensory evaluation methods for high-quality video assessment. To understand which features in motion pictures are recognizable and critical to quality, we address the example of image texture properties, measuring human perception and preferences with a panel of image-quality experts. To this aim, different capture settings were simulated applying sharpening filters as well as digital and analog noise to exemplary source sequences. The evaluation, involving Multidimensional Scaling, Generalized Procrustes Analysis as well as Internal and External Preference Mapping, identified two separate perceptual dimensions. We conclude that Free Choice Profiling connected with a quality test offers the highest level of insight relative to the needed effort. The combination enables a quantitative quality measurement including an analysis of the underlying perceptual reasons.

When enjoying video streaming services, users expect high video quality in various situations, including mobile phone connections with low bandwidths. Furthermore, the user's interest in consuming new large-size data content, such as high resolution/frame rate material or 360 degree videos, is gaining as well. To deal with such challenges, modern encoders adaptively reduce the size of the transmitted data. This in turn requires automated video quality monitoring solutions to ensure a sufficient quality of the material delivered. We present a no-reference video quality model; a model that does not require the original reference material, which is convenient for application in the field. Our approach uses a pretrained classification DNN in combination with hierarchical sub-image creation, some state-of-the-art features and a random forest model. Furthermore, the model can process UHD content and is trained on a large ground-truth data set, which is generated using a state-of-the-art full-reference model. The proposed model achieved a high quality prediction accuracy, comparable to a number of full-reference metrics. Thus our model is a proof-of-concept for a successful no-reference video quality estimation.