A large-scale evaluation of the bitstream-based video-quality model ITU-T P.1204.3 on gaming content. - In: IEEE 22nd International Workshop on Multimedia Signal Processing, (2020), insges. 6 S.
The streaming of gaming content, both passive and interactive, has increased manifolds in recent years. Gaming contents bring with them some peculiarities which are normally not seen in traditional 2D videos, such as the artificial and synthetic nature of contents or repetition of objects in a game. In addition, the perception of gaming content by the user is different from that of traditional 2D videos due to its pecularities and also the fact that users may not often watch such content. Hence, it becomes imperative to evaluate whether the existing video quality models usually designed for traditional 2D videos are applicable to gaming content. In this paper, we evaluate the applicability of the recently standardized bitstream-based video-quality model ITU-T P.1204.3 on gaming content. To analyze the performance of this model, we used 4 different gaming datasets (3 publicly available + 1 internal) not previously used for model training, and compared it with the existing state-of-the-art models. We found that the ITU P.1204.3 model out of the box performs well on these unseen datasets, with an RMSE ranging between 0.38 - 0.45 on the 5-point absolute category rating and Pearson Correlation between 0.85 - 0.93 across all the 4 databases. We further propose a full-HD variant of the P.1204.3 model, since the original model is trained and validated which targets a resolution of 4K/UHD-1. A 50:50 split across all databases is used to train and validate this variant so as to make sure that the proposed model is applicable to various conditions.
https://doi.org/10.1109/MMSP48831.2020.9287055
Between the frames - evaluation of various motion interpolation algorithms to improve 360˚ video quality. - In: 2020 IEEE International Symposium on Multimedia, (2020), S. 65-72
With the increasing availability of 360˚ video content, it becomes important to provide smoothly playing videos of high quality for end users. For this reason, we compare the influence of different Motion Interpolation (MI) algorithms on 360˚ video quality. After conducting a pre-test with 12 video experts in [3], we found that MI is a useful tool to increase the QoE (Quality of Experience) of omnidirectional videos. As a result of the pretest, we selected three suitable MI algorithms, namely ffmpeg Motion Compensated Interpolation (MCI), Butterflow and Super-SloMo. Subsequently, we interpolated 15 entertaining and realworld omnidirectional videos with a duration of 20 seconds from 30 fps (original framerate) to 90 fps, which is the native refresh rate of the HMD used, the HTC Vive Pro. To assess QoE, we conducted two subjective tests with 24 and 27 participants. In the first test we used a Modified Paired Comparison (M-PC) method, and in the second test the Absolute Category Rating (ACR) approach. In the M-PC test, 45 stimuli were used and in the ACR test 60. Results show that for most of the 360˚ videos, the interpolated versions obtained significantly higher quality scores than the lower-framerate source videos, validating our hypothesis that motion interpolation can improve the overall video quality for 360˚ video. As expected, it was observed that the relative comparisons in the M-PC test result in larger differences in terms of quality. Generally, the ACR method lead to similar results, while reflecting a more realistic viewing situation. In addition, we compared the different MI algorithms and can conclude that with sufficient available computing power Super-SloMo should be preferred for interpolation of omnidirectional videos, while MCI also shows a good performance.
https://doi.org/10.1109/ISM.2020.00017
Binaural evaluation of sound quality and quality of experience. - In: The technology of binaural understanding, (2020), S. 393-434
The chapter outlines the concepts of Sound Quality and Quality of Experience (QoE). Building on these, it describes a conceptual model of sound quality perception and experience during active listening in a spatial-audio context. The presented model of sound quality perception considers both bottom-up (signal-driven) as well as top-down (hypothesis-driven) perceptual functional processes. Different studies by the authors and from the literature are discussed in light of their suitability to help develop implementations of the conceptual model. As a key prerequisite, the underlying perceptual ground-truth data required for model training and validation are discussed, as well as means for deriving these from respective listening tests. Both feature-based and more holistic modeling approaches are analyzed. Overall, open research questions are summarized, deriving trajectories for future work on spatial-audio Sound Quality and Quality of Experience modeling.
Multi-model standard for bitstream-, pixel-based and hybrid video quality assessment of UHD/4K: ITU-T P.1204. - In: IEEE access, ISSN 2169-3536, Bd. 8 (2020), S. 193020-193049
https://doi.org/10.1109/ACCESS.2020.3032080
Towards analysing the interaction between quality and storytelling for event video recording. - In: 2020 Twelfth International Conference on Quality of Multimedia Experience (Qomex), (2020), insges. 4 S.
https://doi.org/10.1109/QoMEX48832.2020.9123113
Are you still watching? Streaming video quality and engagement assessment in the crowd. - In: 2020 Twelfth International Conference on Quality of Multimedia Experience (Qomex), (2020), insges. 6 S.
https://doi.org/10.1109/QoMEX48832.2020.9123148
Let the music play: an automated test setup for blind subjective evaluation of music playback on mobile devices. - In: 2020 Twelfth International Conference on Quality of Multimedia Experience (Qomex), (2020), insges. 4 S.
https://doi.org/10.1109/QoMEX48832.2020.9123092
Bitstream-based model standard for 4K/UHD: ITU-T P.1204.3 - model details, evaluation, analysis and open source implementation. - In: 2020 Twelfth International Conference on Quality of Multimedia Experience (Qomex), (2020), insges. 6 S.
https://doi.org/10.1109/QoMEX48832.2020.9123110
Prenc - predict number of video encoding passes with machine learning. - In: 2020 Twelfth International Conference on Quality of Multimedia Experience (Qomex), (2020), insges. 6 S.
https://doi.org/10.1109/QoMEX48832.2020.9123108
Development and evaluation of a test setup to investigate distance differences in immersive virtual environments. - In: 2020 Twelfth International Conference on Quality of Multimedia Experience (Qomex), (2020), insges. 4 S.
https://doi.org/10.1109/QoMEX48832.2020.9123077