3D Quality Of Experience Research Articles

Automatically optimised stereoscopic camera control based on an assessment of 3D video quality of experience

Stereoscopic 3D (S3D) technologies have gained significant attention due to their wide applications. However, producing high-quality S3D content is still a challenging task that requires careful handling to achieve the artistic intent and maintain visual comfort. In this study, we present an automatically controlled stereoscopic camera controller that specifically addresses the challenges in S3D content production. The key idea that distinguishes our method from the existing work is that our method aims to predict the 3D Quality of Experience (QoE) in the production stage so that the optimised camera parameters can be obtained automatically. To this end, considering two interconstraint indicators, i.e., visual comfort and perceived depth, we collect and label a data set of S3D video scene clips and generate a 3D video QoE assessment model that can guide the optimisation of the stereoscopic camera parameters. We describe how to implement our system into a modern production pipeline that has been used in some projects, including commercial ones. The experimental results, including the user studies, demonstrate that our system enhances the perceived depth without creating visual fatigue and that our controller can make the production of S3D content easier and more efficient.

The Effect of Frame Rate on 3D Video Quality and Bitrate

Increasing the frame rate of a 3D video generally results in improved Quality of Experience (QoE). However, higher frame rates involve a higher degree of complexity in capturing, transmission, storage, and display. The question that arises here is what frame rate guarantees high viewing quality of experience given the existing/required 3D devices and technologies (3D cameras, 3D TVs, compression, transmission bandwidth, and storage capacity). This question has already been addressed for the case of 2D video, but not for 3D. The objective of this paper is to study the relationship between 3D quality and bitrate at different frame rates. Our performance evaluations show that increasing the frame rate of 3D videos beyond 60 fps may not be visually distinguishable. In addition, our experiments show that when the available bandwidth is reduced, the highest possible 3D quality of experience can be achieved by adjusting (decreasing) the frame rate instead of increasing the compression ratio. The results of our study are of particular interest to network providers for rate adaptation in variable bitrate channels.

Multimodal Interactive Continuous Scoring of Subjective 3D Video Quality of Experience

People experience a variety of 3D visual programs, such as 3D cinema, 3D TV and 3D games, making it necessary to deploy reliable methodologies for predicting each viewer's subjective experience. We propose a new methodology that we call multimodal interactive continuous scoring of quality (MICSQ). MICSQ is composed of a device interaction process between the 3D display and a separate device (PC, tablet, etc.) used as an assessment tool, and a human interaction process between the subject(s) and the separate device. The scoring process is multimodal, using aural and tactile cues to help engage and focus the subject(s) on their tasks by enhancing neuroplasticity. Recorded human responses to 3D visualizations obtained via MICSQ correlate highly with measurements of spatial and temporal activity in the 3D video content. We have also found that 3D quality of experience (QoE) assessment results obtained using MICSQ are more reliable over a wide dynamic range of content than obtained by the conventional single stimulus continuous quality evaluation (SSCQE) protocol. Moreover, the wireless device interaction process makes it possible for multiple subjects to assess 3D QoE simultaneously in a large space such as a movie theater, at different viewing angles and distances. We conducted a series of interesting 3D experiments showing the accuracy and versatility of the new system, while yielding new findings on visual comfort in terms of disparity, motion and an interesting relation between the naturalness and depth of field (DOF) of a stereo camera.

Visual quality assessment algorithms: what does the future hold?

Creating algorithms capable of predicting the perceived quality of a visual stimulus defines the field of objective visual quality assessment (QA). The field of objective QA has received tremendous attention in the recent past, with many successful algorithms being proposed for this purpose. Our concern here is not with the past however; in this paper we discuss our vision for the future of visual quality assessment research. We first introduce the area of quality assessment and state its relevance. We describe current standards for gauging algorithmic performance and define terms that we will use through this paper. We then journey through 2D image and video quality assessment. We summarize recent approaches to these problems and discuss in detail our vision for future research on the problems of full-reference and no-reference 2D image and video quality assessment. From there, we move on to the currently popular area of 3D QA. We discuss recent databases, algorithms and 3D quality of experience. This yet-nascent technology provides for tremendous scope in terms of research activities and we summarize each of them. We then move on to more esoteric topics such as algorithmic assessment of aesthetics in natural images and in art. We discuss current research and hypothesize about possible paths to tread. Towards the end of this article, we discuss some other areas of interest including high-definition (HD) quality assessment, immersive environments and so on before summarizing interesting avenues for future work in multimedia (i.e., audio-visual) quality assessment.