TOWARDS A DESCRIPTIVE DEPTH INDEX FOR 3D CONTENT: MEASURING PERSPECTIVE DEPTH CUES

International audience3D quality of experience (QoE) in nature is a multidimensional problem and involves many factors that contribute to the global quality rating such as image quality, depth perception and visual discomfort. One important aspect for the development and evaluation of 3D processing techniques is the selection of appropriate 3D content. To this aim it is necessary to develop computational methods that can automatically measure the 3D characteristics of a scene, similar to the spatial and temporal information indices commonly used for assessing 2D content. The presented work is one step in the development of such a depth index (DI) which will target the evaluation of the depth-related characteristics of 3D video sequences. The paper focuses on the linear perspective as one of the major monocular depth cues. It compares two distinct approaches for measuring the strength of perspective depth cues and analyzes their limits on a 2D image dataset with associated subjective ratings

Perceptual preference of S3D over 2D for HDTV in dependence of video quality and depth

International audience3D video quality of experience (QoE) is a multidimensional problem and many factors contribute to the global experience by the user. Due to this multidimensionality, this paper evaluates the integral 3D video QoE and relates it with image quality and depth. Subjective tests have been conducted using paired comparison to evaluate 3D QoE and the preference of 3D over 2D with different combinations of coding conditions. Depth scores were available from previous work and were used to check their relation with 3DQoE; the difference between 2D and 3D QoE is found to be a function of the picture quality, and the desired preference of 3D presentation over 2D can be reached when pictorial quality is high enough (VQM score lower than 0.24). A factor ranging from 0.08 to 0.76 with a mean of 0.71 between pictorial quality and preference of 3D was also found

AVQBits-adaptive video quality model based on bitstream information for various video applications

The paper presents AVQBits, a versatile, bitstream-based video quality model. It can be applied in several contexts such as video service monitoring, evaluation of video encoding quality, of gaming video QoE, and even of omnidirectional video quality. In the paper, it is shown that AVQBits predictions closely match video quality ratings obained in various subjective tests with human viewers, for videos up to 4K-UHD resolution (Ultra-High Definition, 3840 x 2180 pixels) and framerates up 120 fps. With the different variants of AVQBits presented in the paper, video quality can be monitored either at the client side, in the network or directly after encoding. The no-reference AVQBits model was developed for different video services and types of input data, reflecting the increasing popularity of Video-on-Demand services and widespread use of HTTP-based adaptive streaming. At its core, AVQBits encompasses the standardized ITU-T P.1204.3 model, with further model instances that can either have restricted or extended input information, depending on the application context. Four different instances of AVQBits are presented, that is, a Mode 3 model with full access to the bitstream, a Mode 0 variant using only metadata such as codec type, framerate, resoution and bitrate as input, a Mode 1 model using Mode 0 information and frame-type and -size information, and a Hybrid Mode 0 model that is based on Mode 0 metadata and the decoded video pixel information. The models are trained on the authors’ own AVT-PNATS-UHD-1 dataset described in the paper. All models show a highly competitive performance by using AVT-VQDB-UHD-1 as validation dataset, e.g., with the Mode 0 variant yielding a value of 0.890 Pearson Correlation, the Mode 1 model of 0.901, the hybrid no-reference mode 0 model of 0.928 and the model with full bitstream access of 0.942. In addition, all four AVQBits variants are evaluated when applying them out-of-the-box to different media formats such as 360° video, high framerate (HFR) content, or gaming videos. The analysis shows that the ITU-T P.1204.3 and Hybrid Mode 0 instances of AVQBits for the considered use-cases either perform on par with or better than even state-of-the-art full reference, pixel-based models. Furthermore, it is shown that the proposed Mode 0 and Mode 1 variants outperform commonly used no-reference models for the different application scopes. Also, a long-term integration model based on the standardized ITU-T P.1203.3 is presented to estimate ratings of overall audiovisual streaming Quality of Experience (QoE) for sessions of 30 s up to 5 min duration. In the paper, the AVQBits instances with their per-1-sec score output are evaluated as the video quality component of the proposed long-term integration model. All AVQBits variants as well as the long-term integration module are made publicly available for the community for further research

Rule-Based combination of video quality metrics

Lately, several algorithms have been proposed to automatically estimate the quality of video sequences, even some have been included in international standards. However, the majority only provide high performance under particular conditions and with certain types of degradations. Therefore, some proposals have been presented setting out the combination of various quality metrics to improve the performance and the range of application. In this paper, a rule-based combination of standardized metrics is presented, in contrast to most of these type of approaches based on combinational models. The proposed system consists of a first stage in which the type of degradation affecting the video quality is identified to be caused by coding impairments or transmission errors. Then, the most appropriate metric for that distortion is applied. Specifically, VQM and VQuad have been considered for coding and transmission distortions, respectively. The results show that the overall performance is better than using the quality metrics individually

Power Reduction Opportunities on End-User Devices in Quality-Steady Video Streaming

This paper uses a crowdsourced dataset of online video streaming sessions to investigate opportunities to reduce the power consumption while considering QoE. For this, we base our work on prior studies which model both the end-user's QoE and the end-user device's power consumption with the help of high-level video features such as the bitrate, the frame rate, and the resolution. On top of existing research, which focused on reducing the power consumption at the same QoE optimizing video parameters, we investigate potential power savings by other means such as using a different playback device, a different codec, or a predefined maximum quality level. We find that based on the power consumption of the streaming sessions from the crowdsourcing dataset, devices could save more than 55% of power if all participants adhere to low-power settings.Comment: 4 pages, 3 figure

Cross-timescale experience evaluation framework for productive teaming

This paper presents the initial concept for an evaluation framework to systematically evaluate productive teaming (PT). We consider PT as adaptive human-machine interactions between human users and augmented technical production systems. Also, human-to-human communication as part of a hybrid team with multiple human actors is considered, as well as human-human and human-machine communication for remote and mixed remote- and co-located teams. The evaluation comprises objective, performance-related success indicators, behavioral metadata, and measures of human experience. In particular, it considers affective, attentional and intentional states of human team members, their influence on interaction dynamics in the team, and researches appropriate strategies to satisfyingly adjust dysfunctional dynamics, using concepts of companion technology. The timescales under consideration span from seconds to several minutes, with selected studies targeting hour-long interactions and longer-term effects such as effort and fatigue. Two example PT scenarios will be discussed in more detail. To enable generalization and a systematic evaluation, the scenarios’ use cases will be decomposed into more general modules of interaction

Videoconference fatigue: a conceptual analysis

Videoconferencing (VC) is a type of online meeting that allows two or more participants from different locations to engage in live multi-directional audio-visual communication and collaboration (e.g., via screen sharing). The COVID-19 pandemic has induced a boom in both private and professional videoconferencing in the early 2020s that elicited controversial public and academic debates about its pros and cons. One main concern has been the phenomenon of videoconference fatigue. The aim of this conceptual review article is to contribute to the conceptual clarification of VC fatigue. We use the popular and succinct label “Zoom fatigue” interchangeably with the more generic label “videoconference fatigue” and define it as the experience of fatigue during and/or after a videoconference, regardless of the specific VC system used. We followed a structured eight-phase process of conceptual analysis that led to a conceptual model of VC fatigue with four key causal dimensions: (1) personal factors, (2) organizational factors, (3) technological factors, and (4) environmental factors. We present this 4D model describing the respective dimensions with their sub-dimensions based on theories, available evidence, and media coverage. The 4D-model is meant to help researchers advance empirical research on videoconference fatigue