The paper describes a design of a subjective experiment for testing the video quality of High Dynamic Range, Wide Color gamut (HDR-WCG) content at 4K resolution. Due to Covid, testing could not use a lab, so an at-home test procedure was developed. To aim for calibration despite not fully controlling the conditions and settings, we limited subjects to those who had a specific TV model, which we had previously calibrated in our labs. Moreover, we performed the experiment in the Dolby Vision mode (where the various enhancements of the TV are turned OFF by default). A browser approach was used which took control of the TV, and ensure the content was viewed at the native resolution of the TV (e.g., dot-on-dot mode). In addition, we know that video imagery is not ergodic, and there is wide variability in types of low levels features (sharpness, noise, motion, color volume, etc.) that affect both TV and visual system performance. So, a large number of test clips was used (30) and the content was specifically chosen to stress key features. The obtained data is qualitatively similar to an in-lab study and is subsequently used to evaluate several existing objective quality metrics.
The development of audio-visual quality models faces a number of challenges, including the integration of audio and video sensory channels and the modeling of their interaction characteristics. Commonly, objective quality metrics estimate the quality of a single component (audio or video) of the content. Machine learning techniques, such as autoencoders, offer as a very promising alternative to develop objective assessment models. This paper studies the performance of a group of autoencoder-based objective quality metrics on a diverse set of audio-visual content. To perform this test, we use a large dataset of audio-visual content (The UnB-AV database), which contains degradations in both audio and video components. The database has accompanying subjective scores collected on three separate subjective experiments. We compare our autoencoder-based methods, which take into account both audio and video components (multi-modal), against several objective (single-modal) audio and video quality metrics. The main goal of this work is to verify the gain or loss in performance of these single-modal metrics, when tested on audio-visual sequences.
In this paper, we compare the influence of a higherresolution Head-Mounted Display (HMD) like HTC Vive Pro on 360° video QoE to that obtained with a lower-resolution HMD like HTC Vive. Furthermore, we evaluate the difference in perceived quality for entertainment-type 360° content in 4K/6K/8K resolutions at typical high-quality bitrates. In addition, we evaluate which video parts people are focusing on while watching omnidirectional videos. To this aim we conducted three subjective tests. We used HTC Vive in the first and HTC Vive Pro in the other two tests. The results from our tests are showing that the higher resolution of the Vive Pro seems to enable people to more easily judge the quality, shown by a minor deviation between the resulting quality ratings. Furthermore, we found no significant difference between the quality scores for the highest bitrate for 6K and 8K resolution. We also compared the viewing behavior for the same content viewed for the first time with the behavior when the same content is viewed again multiple times. The different representations of the contents were explored similarly, probably due to the fact that participants are finding and comparing specific parts of the 360° video suitable for rating the quality.