A high effort in Quality of Experience (QoE) research has been put into subjective assessment to determine the perceived quality of video. Most laboratory experiments follow guidelines from the ITU-T Recommendations, which suggest Absolute Category Rating (ACR) as a method to conduct such experiments. However, this method of video assessment radically limits confounding variables, is unnatural, and is far from how people cope with quality degradation and the cues they receive in these situations. This paper addresses this issue and proposes a more realistic subjective experiment based on the participant's behavior. Instead of passively rating the degraded quality of silent videos, we created the possibility to react to the annoying quality of chosen Netflix movies and reward participants by increasing the quality to the best possible. To cope with the data obtained, we adapted the method of fitting psychometric functions known in neuroscience, auditory science, animal science, and psychology. As a result, we obtained a more comprehensive image of the participants and their perceived quality, including their consistency, lapses, and differences. We estimated the parameters using Maximum Likelihood Estimation (MLE) and evaluated the goodness-of-fit of three S-shaped functions: Weibull, cumulative normal, and logistic. In the end, as the most common, we analyze the basic properties of the fitted functions, such as the Point of Subjective Equality (PSE), confidence intervals, and slope (β). We examine their role in describing individual differences among 34 subjects.

Recently an impressive development in immersive technologies, such as Augmented Reality (AR), Virtual Reality (VR) and 360${^\circ }$video, has been witnessed. However, methods for quality assessment have not been keeping up. This paper studies quality assessment of 360${^\circ }$video from the cross-lab tests (involving ten laboratories and more than 300 participants) carried out by the Immersive Media Group (IMG) of the Video Quality Experts Group (VQEG). These tests were addressed to assess and validate subjective evaluation methodologies for 360${^\circ }$video. Audiovisual quality, simulator sickness symptoms, and exploration behavior were evaluated with short (from 10 seconds to 30 seconds) 360${^\circ }$sequences. The following factors’ influences were also analyzed: assessment methodology, sequence duration, Head-Mounted Display (HMD) device, uniform and non-uniform coding degradations, and simulator sickness assessment methods. The obtained results have demonstrated the validity of Absolute Category Rating (ACR) and Degradation Category Rating (DCR) for subjective tests with 360${^\circ }$videos, the possibility of using 10-second videos (with or without audio) when addressing quality evaluation of coding artifacts, as well as any commercial HMD (satisfying minimum requirements). Also, more efficient methods than the long Simulator Sickness Questionnaire (SSQ) have been proposed to evaluate related symptoms with 360${^\circ }$videos. These results have been instrumental for the development of the ITU-T Recommendation P.919. Finally, the annotated dataset from the tests is made publicly available for the research community.

Cross talk is one of the most important parameters of the 3D displays' quality. Different cross talk definitions exist, which makes cross talk measurement and comparison difficult. We take a step back and focus on a detailed 3D display luminance analysis. The conclusions we draw from the temporal luminance analysis can be used to propose an effective approach to cross talk measurements. In scope of the presented work we have measured four different 3D displays.