Quality Of Experience Ratings Research Articles

Automated Customization of On-Device Inference for Quality-of-Experience Enhancement

The rapid uptake of intelligent applications is pushing deep learning (DL) capabilities to mobile devices. However, the heterogeneities in device capacity, DNN performances, and user preferences make it challenging to provide satisfactory Quality of Experience (QoE) to mobile users. This paper studies automated customization for DL inference on mobile devices (termed as on-device inference), and our goal is to enhance user QoE by configuring the on-device inference with an appropriate DNN for users under different usage scenarios. The core of our method is a DNN selection module that learns user QoE patterns on-the-fly and identifies the best-fit DNN for on-device inference with the learned knowledge. It leverages an online learning algorithm, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NeuralUCB</i> , that has excellent generalization ability for handling various user QoE patterns. We also embed the knowledge transfer technique in NeuralUCB to expedite the learning process. However, NeuralUCB frequently solicits QoE ratings from users, which incurs non-negligible inconvenience. To address this problem, we design feedback solicitation schemes to reduce the number of QoE solicitations while maintaining the learning efficiency of NeuralUCB. A pragmatic problem, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aggregated QoE</i> , is further investigated to improve the practicality of our framework. We conduct experiments on both synthetic and real-world data. The results indicate that our method efficiently learns the user QoE pattern with few solicitations and provides drastic QoE enhancement for mobile devices.

Statistical methods and models based on quality of experience distributions

Due to biased assumptions on the underlying ordinal rating scale in subjective Quality of Experience (QoE) studies, Mean Opinion Score (MOS)-based evaluations provide results, which are hard to interpret and can be misleading. This paper proposes to consider the full QoE distribution for evaluating, reporting, and modeling QoE results instead of relying on MOS-based metrics derived from results based on ordinal rating scales. The QoE distribution can be represented in a concise way by using the parameters of a multinomial distribution without losing any information about the underlying QoE ratings, and even keeps backward compatibility with previous, biased MOS-based results. Considering QoE results as a realization of a multinomial distribution allows to rely on a well-established theoretical background, which enables meaningful evaluations also for ordinal rating scales. Moreover, QoE models based on QoE distributions keep detailed information from the results of a QoE study of a technical system, and thus, give an unprecedented richness of insights into the end users’ experience with the technical system. In this work, existing and novel statistical methods for QoE distributions are summarized and exemplary evaluations are outlined. Furthermore, using the novel concept of quality steps, simulative and analytical QoE models based on QoE distributions are presented and showcased. The goal is to demonstrate the fundamental advantages of considering QoE distributions over MOS-based evaluations if the underlying rating data is ordinal in nature.

A Quality of Experience assessment of haptic and augmented reality feedback modalities in a gait analysis system.

Gait analysis is a technique that is used to understand movement patterns and, in some cases, to inform the development of rehabilitation protocols. Traditional rehabilitation approaches have relied on expert guided feedback in clinical settings. Such efforts require the presence of an expert to inform the re-training (to evaluate any improvement) and the patient to travel to the clinic. Nowadays, potential opportunities exist to employ the use of digitized "feedback" modalities to help a user to "understand" improved gait technique. This is important as clear and concise feedback can enhance the quality of rehabilitation and recovery. A critical requirement emerges to consider the quality of feedback from the user perspective i.e. how they process, understand and react to the feedback. In this context, this paper reports the results of a Quality of Experience (QoE) evaluation of two feedback modalities: Augmented Reality (AR) and Haptic, employed as part of an overall gait analysis system. The aim of the feedback is to reduce varus/valgus misalignments, which can cause serious orthopedics problems. The QoE analysis considers objective (improvement in knee alignment) and subjective (questionnaire responses) user metrics in 26 participants, as part of a within subject design. Participants answered 12 questions on QoE aspects such as utility, usability, interaction and immersion of the feedback modalities via post-test reporting. In addition, objective metrics of participant performance (angles and alignment) were also considered as indicators of the utility of each feedback modality. The findings show statistically significant higher QoE ratings for AR feedback. Also, the number of knee misalignments was reduced after users experienced AR feedback (35% improvement with AR feedback relative to baseline when compared to haptic). Gender analysis showed significant differences in performance for number of misalignments and time to correct valgus misalignment (for males when they experienced AR feedback). The female group self-reported higher utility and QoE ratings for AR when compared to male group.

Quality of Experience Assessment of 360-degree video

The research domain on the Quality of Experience (QoE) of 2D video streaming has been well established. However, a new video format is emerging and gaining popularity and availability: VR 360-degree video. The processing and transmission of 360-degree videos brings along new challenges such as large bandwidth requirements and the occurrence of different distortions. The viewing experience is also substantially different from 2D video, it offers more interactive freedom on the viewing angle but can also be more demanding and cause cybersickness. Further research on the QoE of 360-videos specifically is thus required. The goal of this study is to complement earlier research by (Tran, Ngoc, Pham, Jung, and Thank, 2017) testing the effects of quality degradation, freezing, and content on the QoE of 360-videos. Data will be gathered through subjective tests where participants watch degraded versions of 360-videos through an HMD. After each video they will answer questions regarding their quality perception, experience, perceptual load, and cybersickness. Results of the first part show overall rather low QoE ratings and it decreases even more as quality is degraded and freezing events are added. Cyber sickness was found not to be an issue.

The effect of requests for user feedback on Quality of Experience

Companies are interested in knowing how users experience and perceive their products. Quality of Experience (QoE) is a measurement that is used to assess the degree of delight or annoyance in experiencing a software product. To assess QoE, we have used a feedback tool integrated into a software product to ask users about their QoE ratings and to obtain information about their rationales for good or bad QoEs. It is known that requests for feedback may disturb users; however, little is known about the subjective reasoning behind this disturbance or about whether this disturbance negatively affects the QoE of the software product for which the feedback is sought. In this paper, we present a mixed qualitative-quantitative study with 35 subjects that explore the relationship between feedback requests and QoE. The subjects experienced a requirement-modeling mobile product, which was integrated with a feedback tool. During and at the end of the experience, we collected the users’ perceptions of the product and the feedback requests. Based on the users’ rational for being disturbed by the feedback requests, such as “early feedback,” “interruptive requests,” “frequent requests,” and “apparently inappropriate content,” we modeled feedback requests. The model defines feedback requests using a set of five-tuple variables: “task,” “timing” of the task for issuing the feedback requests, user’s “expertise-phase” with the product, the “frequency” of feedback requests about the task, and the “content” of the feedback request. Configuration of these parameters might drive the participants’ perceived disturbances. We also found that the disturbances generated by triggering user feedback requests have negligible impacts on the QoE of software products. These results imply that software product vendors may trust users’ feedback even when the feedback requests disturb the users.

Crowdsourcing vs. laboratory experiments – QoE evaluation of binaural playback in a teleconference scenario

Experiments for the subjective evaluation of multimedia presentations and content are traditionally conducted in a laboratory environment. In this respect common procedures for the evaluation of teleconference systems are no different. The strictly controlled laboratory environment, however, often gives a rather poor representation of the actual use case. Therefore in this study we crowdsourced the evaluation of a teleconference system to perform the evaluation in a real-life environment. Moreover, we used the unique possibilities of crowdsourcing to employ two different demographics by hiring workers from Germany on the one hand and the US and Great Britain on the other hand. The goal of this experiment was to assess the perceived Quality of Experience (QoE) during a listening test and compare the results to results from a similar listening test conducted in the controlled laboratory environment. In doing so, we observed not only intriguing differences in the collected QoE ratings between the results of laboratory and crowdsourcing experiments, but also between the different worker demographics in terms of reliability, availability and efficiency.