Publications

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.

This paper presents a method for developing a consensus vocabulary to describe and evaluate the visual experience of videos. As a first result, a vocabulary characterizing the specific look of cinema-type video is presented. Such a vocabulary can be used to relate perceptual features of professional high-end image and video quality of experience (QoE) with the underlying technical characteristics and settings of the video systems involved in the creative content production process. For the vocabulary elicitation, a combination of different survey techniques was applied in this work. As the first step, individual interviews were conducted with experts of the motion picture industry on image quality in the context of cinematography. The data obtained from the interviews was used for the subsequent Real-time Delphi survey, where an extended group of experts worked out a consensus on key aspects of the vocabulary specification. Here, 33 experts were supplied with the anonymized results of the other panelists, which they could use to revise their own assessment. Based on this expert panel, the attributes collected in the interviews were verified and further refined, resulting in the final vocabulary proposed in this paper. Besides an attribute-based sensory evaluation of high-quality image, video and film material, applications of the vocabulary are the development of dimension-based image and video quality models, and the analysis of the multivariate relationship between quality-relevant perceptual attributes and technical system parameters.