Publications

In this paper, we conducted two different studies. Our first study deals with measuring the flickering in HMDs using a selfdeveloped measurement tool. Therefore, we investigated several combinations of software 360˚ video players and framerates. We found out that only 90 fps - content is leading to a ideal and smooth playout without stuttering or black frame insertion. In addition, it should be avoided to playout 360˚ content at lower framerates, especially 25 and 50 fps. In our second study we investigated the influence of higher framerates of various 360˚ - videos on the perceived quality. Doing so, we conducted a subjective test using 12 expert viewers. The participants watched 30 fps native as well as interpolated 90 fps 360˚ content, whether we also rendered two contents published along with the paper. We found out that 90 fps is significantly - improving the perceived quality. Additionally, we compared the performance of three motion interpolation algorithms. From the results it is visible that motion interpolation can be used in post production to improve the perceived quality.

4K television screens or even with higher resolutions are currently available in the market. Moreover video streaming providers are able to stream videos in 4K resolution and beyond. Therefore, it becomes increasingly important to have a proper understanding of video quality especially in case of 4K videos. To this effect, in this paper, we present a study of subjective and objective quality assessment of 4K ultra-high-definition videos of short duration, similar to DASH segment lengths. As a first step, we conducted four subjective quality evaluation tests for compressed versions of the 4K videos. The videos were encoded using three different video codecs, namely H.264, HEVC, and VP9. The resolutions of the compressed videos ranged from 360p to 2160p with framerates varying from 15fps to 60fps. All the source 4K contents used were of 60fps. We included low quality conditions in terms of bitrate, resolution and framerate to ensure that the tests cover a wide range of conditions, and that e.g. possible models trained on this data are more general and applicable to a wider range of real world applications. The results of the subjective quality evaluation are analyzed to assess the impact of different factors such as bitrate, resolution, framerate, and content. In the second step, different state-of-the-art objective quality models were applied to all videos and their performance was analyzed in comparison with the subjective ratings, e.g. using Netflix's VMAF. The videos, subjective scores, both MOS and confidence interval per sequence and objective scores are made public for use by the community for further research.

Considering modern cameras, increasing image resolutions and thousands of images uploaded to sharing platforms there is still reason to have a deeper look into image compression. Especially lossy image compression is always a trade-off between file-size and image quality, where high quality is usually preferred for storage. Beside classical image compression, e.g. JPEG, there is also ongoing development to use video codecs to compress images. We analyze four different video codecs, namely AV1, H.264, H.265 and VP9, in comparison with JPEG. Our evaluation considers classical image quality metrics, e.g. PSNR, and also a modern subjective quality metric, i.e. Netflix's VMAF. We are able to show that modern video codecs can outperform classical JPEG compression both in terms of quality and file-size. For this we used 1133 uncompressed images and applied different encoding settings and estimated image quality.