Abstract
The use of Stereoscopic 3D (S3D) videos has been popular in commercial markets with ongoing developments in the field of visual entertainment in recent years. A wide variety of projection methods of 3D video content is currently available, such as projection to a panoramic screen and projection of omnidirectional video content from head mounted displays using Virtual Reality (VR) technology. This article investigates the Quality of Experience (QoE) and associated Visually Induced Motion Sickness (VIMS) caused by the viewing of S3D videos. The investigations used three different projection screens: a 3D flat screen, a 3D panoramic screen in a hemispherical shaped room and a VR headset. Several assessment methods including a Simulator Sickness Questionnaire (SSQ), ElectroEncephaloGraphy (EEG), and measurement tools for eye blink rate detection were applied to measure the QoE experienced by viewers. The SSQ scores were also compared with the behavioral data such as attention and meditation levels and enjoyment ratings acquired from different video content and projection screens. The results indicate that the projection screen is a key factor affecting the level of visual fatigue, VIMS and QoE assessments, which are discussed in-depth in the article.
Highlights
The recent growth of Stereoscopic 3D (S3D) technology has led to the commercial production of 3D movies
The results revealed that the viewing distance and the synchronization of the projection of video sequences in the Data Arena may contribute to less visual fatigue, resulting in lower total Sickness Questionnaire (SSQ) scores
The results showed that both the device and video sequence factors were not significant
Summary
The recent growth of Stereoscopic 3D (S3D) technology has led to the commercial production of 3D movies. Research studies have found that people may experience visual fatigue after watching stereoscopic movies or images. There is an existing body of research to discuss QoE experiments with different projection screens These QoE experiments can generally be classified into three types: image quality assessment models, subjective evaluations among participants and adding stimulus for comparison. A further study by Narciso et al [10] proposed adding smell as an additional stimulus to measure the influence of presence, cybersickness and fatigue in VR environments. Duan et al [11] conducted a perceptual quality assessment of omnidirectional images through the collection of viewing directions, subjective quality scores and eye-tracking information into a database for omnidirectional image quality assessment This database was compared with other existing image quality assessment databases. Singla et al [12] compared the QoE of omnidirectional content viewed through different
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