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Abstract: Abstract Fuelled by the increase in popularity of virtual and augmented reality applications, point clouds have emerged as a popular 3D format for acquisition and rendering of digital humans, thanks to their versatility and real-time capabilities. Due to technological constraints and real-time rendering limitations, however, the visual quality of dynamic point cloud contents is seldom evaluated using virtual and augmented reality devices, instead relying on prerecorded videos displayed on conventional 2D screens. In this study, we evaluate how the visual quality of point clouds representing digital humans is affected by compression distortions. In particular, we compare three different viewing conditions based on the degrees of freedom that are granted to the viewer: passive viewing (2DTV), head rotation (3DoF), and rotation and translation (6DoF), to understand how interacting in the virtual space affects the perception of quality. We provide both quantitative and qualitative results of our evaluation involving 78 participants, and we make the data publicly available. To the best of our knowledge, this is the first study evaluating the quality of dynamic point clouds in virtual reality, and comparing it to traditional viewing settings. Results highlight the dependency of visual quality on the content under test, and limitations in the way current data sets are used to evaluate compression solutions. Moreover, influencing factors in quality evaluation in VR, and shortcomings in how point cloud encoding solutions handle visually-lossless compression, are discussed. PubDate: 2022-05-07
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Abstract: Abstract The current pandemic situation has led to an extraordinary increase in remote working activities all over the world. In this paper, we conducted a research study with the aim to investigate the Quality of Remote Working Experience (QRWE) of workers when conducting remote working activities and to analyse its correlation with implicit emotion responses estimated from the speech of video-calls or discussions with people in the same room. We implemented a system that captures the audio when the worker is talking and extracts and stores several speech features. A subjective assessment has been conducted, using this tool, which involved 12 people that were asked to provide feedback on the QRWE and assess their sentiment polarity during their daily remote working hours. ANOVA results suggest that speech features may be potentially observed to infer the QRWE and the sentiment polarity of the speaker. Indeed, we have also found that the perceived QRWE and polarity are strongly related. PubDate: 2022-03-30
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Abstract: Abstract Within the worldwide diving community, underwater photography is becoming increasingly popular. However, the marine environment presents certain challenges for image capture, with resulting imagery often suffering from colour distortions, low contrast and blurring. As a result, image enhancement software is used not only to enhance the imagery aesthetically, but also to address these degradations. Although feature-rich image enhancement software products are available, little is known about the user experience of underwater photographers when interacting with such tools. To address this gap, we conducted an online questionnaire to better understand what software tools are being used, and face-to-face interviews to investigate the characteristics of the image enhancement user experience for underwater photographers. We analysed the interview transcripts using the pragmatic and hedonic categories from the frameworks of Hassenzahl (Funology, Kluwer Academic Publishers, Dordrecht, pp 31–42, 2003; Funology 2, Springer, pp 301–313, 2018) for positive and negative user experience. Our results reveal a moderately negative experience overall for both pragmatic and hedonic categories. We draw some insights from the findings and make recommendations for improving the user experience for underwater photographers using image enhancement tools. PubDate: 2021-12-22 DOI: 10.1007/s41233-021-00048-3
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Abstract: Abstract Human experiences have been studied in multiple disciplines, Human–Computer Interaction (HCI) being one of the largest research fields with its user experience (UX) research. Currently, there is little interaction between experience researchers from different disciplines, although cross-disciplinary knowledge sharing has the potential to accelerate the development of UX and other experience research fields to the next level. This article reports a research profiling study of almost 52,000 experience publications over 125 years, showing the breadth of experience research across disciplines. The data analysis reveals the disciplines that study experiences, the prominent authors, institutions and countries in experience research, the most cited works by experience researchers across disciplines, and how UX research is situated on the map of experience research. This descriptive research profiling study is a necessary first step on the journey of mapping the landscape of experience research, guiding researchers towards understanding experience as a multidisciplinary concept, and establishing a more coherent experience research field. PubDate: 2021-09-17 DOI: 10.1007/s41233-021-00047-4
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Abstract: Abstract The uptake of chatbots for customer service depends on the user experience. For such chatbots, user experience in particular concerns whether the user is provided relevant answers to their queries and the chatbot interaction brings them closer to resolving their problem. Dialogue data from interactions between users and chatbots represents a potentially valuable source of insight into user experience. However, there is a need for knowledge of how to make use of these data. Motivated by this, we present a framework for qualitative analysis of chatbot dialogues in the customer service domain. The framework has been developed across several studies involving two chatbots for customer service, in collaboration with the chatbot hosts. We present the framework and illustrate its application with insights from three case examples. Through the case findings, we show how the framework may provide insight into key drivers of user experience, including response relevance and dialogue helpfulness (Case 1), insight to drive chatbot improvement in practice (Case 2), and insight of theoretical and practical relevance for understanding chatbot user types and interaction patterns (Case 3). On the basis of the findings, we discuss the strengths and limitations of the framework, its theoretical and practical implications, and directions for future work. PubDate: 2021-08-20 DOI: 10.1007/s41233-021-00046-5
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Abstract: Abstract The development of immersive technologies has brought with it the need to redefine the concept of quality of experience (QoE). Studies have explored QoE in virtual reality (VR) by adopting a top-down approach—these are solely based on existing frameworks and theory, and complemented with novel technical considerations. It can be argued that any QoE framework derived in this manner is limited, as its scope is fixed even prior to any data gathering process. To this end, the current study proposes a bottom-up approach, involving the user in the formulation of a broader QoE model. The repertory grid technique (RGT) was used to analyse and group 360 attributes, listed by participants as criteria they used in judging the quality of a VR experience. The advantage of RGT is that it has a holistic approach towards the interpretation of the user’s experience combined with the precision of quantitative analysis. The study resulted in a QoE model that consists of three main groups of attributes (i.e., user, content, and system). Furthermore, the analysis showed that participants listed attributes related to their experience and appraisal of VR, and to the content that they viewed. In contrast, very few system-related attributes were mentioned. Finally, the current study discussed the RGT methodology—and user-driven approaches in general—as a complementary research approach to create a comprehensive and practical QoE model. PubDate: 2021-04-03 DOI: 10.1007/s41233-021-00045-6
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Abstract: Abstract Driving stress can impact the driving performance that has an impact on the overall driving experiences. It is a vital area to focus on when the traffic scenario is challenging in terms of having traffic congestion, unruly drivers, and a lack of law enforcement. In Bangladesh, these issues are frequent on the roads. That is why we looked at self-reported stress scores of professional drivers, their personality analysis and conducted mixed-method (quantitative and qualitative) user studies that provided us a clear indication of driving stress. Then the findings motivated us to design and develop a low-cost real-time stress measurement wearable through human-centered computing, users’ feedback, and experiences. This wearable unit can understand bodily stress from physiological factors using Heart Rate Variability along with road conditions. This technology can help in supporting drivers in increasing self-awareness regarding driving stress, which will have a positive impact on drivers’ wellbeing and overall driving performance. PubDate: 2021-01-06 DOI: 10.1007/s41233-020-00043-0
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Abstract: Abstract 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. PubDate: 2020-12-26 DOI: 10.1007/s41233-020-00044-z
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Abstract: Abstract Subjective speech quality assessment has traditionally been carried out in laboratory environments under controlled conditions. With the advent of crowdsourcing platforms tasks, which need human intelligence, can be resolved by crowd workers over the Internet. Crowdsourcing also offers a new paradigm for speech quality assessment, promising higher ecological validity of the quality judgments at the expense of potentially lower reliability. This paper compares laboratory-based and crowdsourcing-based speech quality assessments in terms of comparability of results and efficiency. For this purpose, three pairs of listening-only tests have been carried out using three different crowdsourcing platforms and following the ITU-T Recommendation P.808. In each test, listeners judge the overall quality of the speech sample following the Absolute Category Rating procedure. We compare the results of the crowdsourcing approach with the results of standard laboratory tests performed according to the ITU-T Recommendation P.800. Results show that in most cases, both paradigms lead to comparable results. Notable differences are discussed with respect to their sources, and conclusions are drawn that establish practical guidelines for crowdsourcing-based speech quality assessment. PubDate: 2020-11-22 DOI: 10.1007/s41233-020-00042-1
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Abstract: Abstract Evaluating the Quality of Experience (QoE) of video streaming and its influence factors has become paramount for streaming providers, as they want to maintain high satisfaction for their customers. In this context, crowdsourced user studies became a valuable tool to evaluate different factors which can affect the perceived user experience on a large scale. In general, most of these crowdsourcing studies either use, what we refer to, as an in vivo or an in vitro interface design. In vivo design means that the study participant has to rate the QoE of a video that is embedded in an application similar to a real streaming service, e.g., YouTube or Netflix. In vitro design refers to a setting, in which the video stream is separated from a specific service and thus, the video plays on a plain background. Although these interface designs vary widely, the results are often compared and generalized. In this work, we use a crowdsourcing study to investigate the influence of three interface design alternatives, an in vitro and two in vivo designs with different levels of interactiveness, on the perceived video QoE. Contrary to our expectations, the results indicate that there is no significant influence of the study’s interface design in general on the video experience. Furthermore, we found that the in vivo design does not reduce the test takers’ attentiveness. However, we observed that participants who interacted with the test interface reported a higher video QoE than other groups. PubDate: 2020-11-02 DOI: 10.1007/s41233-020-00041-2
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Abstract: Abstract Output-based instrumental speech quality assessment relies only on the received (processed) signal to predict quality. Such methods are called non-intrusive and are crucial in speech applications where reference clean signals are not accessible. In this paper, we propose a new non-intrusive instrumental quality measure based on the similarity between two i-vectors. As the reference clean signal is not available, the reference i-vector representation cannot be extracted directly from it. Therefore, we propose the use of a clean speech Gaussian mixture model to estimate the clean speech spectra from its degraded speech spectrum counterpart. Next, the two respective i-vector representations are extracted and either the cosine or Eucledian similarity metrics are computed as a correlate of speech quality. Here, the clean speech model is trained using RASTA-filtered mel-frequency cepstral coefficients extracted from a pool of clean speech files, thus allowing us to attain a model of clean spectrum characteristics. The proposed method is evaluated on noisy, reverberant, and enhanced speech conditions. Experimental results show the proposed system providing higher correlations with perceptual speech quality than several benchmark non-intrusive measures, especially for noisy and enhanced speech. PubDate: 2020-10-06 DOI: 10.1007/s41233-020-00040-3
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Abstract: Abstract Progressively, smartphones have become the pocket Swiss army knife for everyone. They support their users needs to accomplish tasks in numerous contexts. However, the applications executing those tasks are regularly not performing as they should, and the user-perceived experience is altered. In this paper, we present our approach to model and predict the Quality of Experience (QoE) of mobile applications used over WiFi or cellular network. We aimed to create predictive QoE models and to derive recommendations for mobile application developers to create QoE aware applications. Previous works on smartphone applications’ QoE prediction only focus on qualitative or quantitative data. We collected both qualitative and quantitative data “in the wild“ through our living lab. We ran a 4-week-long study with 38 Android phone users. We focused on frequently used and highly interactive applications. The participants rated their mobile applications’ expectation and QoE and in various contexts resulting in a total of 6086 ratings. Simultaneously, our smartphone logger (mQoL-Log) collected background information such as network information, user physical activity, battery statistics, and more. We apply various data aggregation approaches and features selection processes to train multiple predictive QoE models. We obtain better model performances using ratings acquired within 14.85 minutes after the application usage. Additionally, we boost our models’ performance with the users expectation as a new feature. We create an on-device prediction model with on-smartphone only features. We compare its performance metrics against the previous model. The on-device model performs below the full features models. Surprisingly, among the following top three features: the intended task to accomplish with the app, application’s name (e.g., WhatsApp, Spotify), and network Quality of Service (QoS), the user physical activity is the most important feature (e.g., if walking). Finally, we share our recommendations with the application developers, and we discuss the implications of QoE and expectations in mobile application design. PubDate: 2020-10-04 DOI: 10.1007/s41233-020-00039-w
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Abstract: Abstract Digitalising patient-centric services to address society’s challenges with an ageing population and healthcare provision is by many seen as important. Studying the effects of the digitalisation on the work engagement of the users of the new systems is vital in this context, especially since previous research has established that the work engagement at work in healthcare is problematic. Work engagement is defined as a positive, fulfilling, affective-motivational state of work related well being, as is closely connected to the experience of resources and demands in the work context. These resources can be for example digital support, experienced demands or empowerment whereas exhaustion is connected to work demand in a workplace. This study contributes to knowledge about the effects of digitalisation on work engagement and exhaustion in the context of patient-centred services and eHealth. Contextual interviews were conducted on site for 5 h with nurses using a new chat function and using telephone for medical advice to patients. Additionally, semi-structured interviews were conducted with all the nurses participating in this digitalisation project to gather more insights into their work engagement in the two work situations. Results were analysed in different themes of areas affected by the digitalisation in the two overarching themes: job demands and job resources. The results show that the change to a chat function when communicating with advice seekers had connection to work engagement in several ways. The nurses experienced less time pressure and emotional pressure, but also a loss of job control and feedback from colleagues working from home. PubDate: 2020-09-19 DOI: 10.1007/s41233-020-00038-x
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Abstract: Abstract Current benchmarks for optical flow algorithms evaluate the estimation either directly by comparing the predicted flow fields with the ground truth or indirectly by using the predicted flow fields for frame interpolation and then comparing the interpolated frames with the actual frames. In the latter case, objective quality measures such as the mean squared error are typically employed. However, it is well known that for image quality assessment, the actual quality experienced by the user cannot be fully deduced from such simple measures. Hence, we conducted a subjective quality assessment crowdscouring study for the interpolated frames provided by one of the optical flow benchmarks, the Middlebury benchmark. It contains interpolated frames from 155 methods applied to each of 8 contents. For this purpose, we collected forced-choice paired comparisons between interpolated images and corresponding ground truth. To increase the sensitivity of observers when judging minute difference in paired comparisons we introduced a new method to the field of full-reference quality assessment, called artefact amplification. From the crowdsourcing data (3720 comparisons of 20 votes each) we reconstructed absolute quality scale values according to Thurstone’s model. As a result, we obtained a re-ranking of the 155 participating algorithms w.r.t. the visual quality of the interpolated frames. This re-ranking not only shows the necessity of visual quality assessment as another evaluation metric for optical flow and frame interpolation benchmarks, the results also provide the ground truth for designing novel image quality assessment (IQA) methods dedicated to perceptual quality of interpolated images. As a first step, we proposed such a new full-reference method, called WAE-IQA, which weights the local differences between an interpolated image and its ground truth. PubDate: 2020-09-05 DOI: 10.1007/s41233-020-00037-y
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Abstract: Abstract With Quality of Experience (QoE) research having made significant advances over the years, service and network providers aim at user-centric evaluation of the services provided in their system. The question arises how to derive QoE in systems. In the context of subjective user studies conducted to derive relationships between influence factors and QoE, user diversity leads to varying distributions of user rating scores for different test conditions. Such models are commonly exploited by providers to derive various QoE metrics in their system, such as expected QoE, or the percentage of users rating above a certain threshold. The question then becomes how to combine (a) user rating distributions obtained from subjective studies, and (b) system parameter distributions, so as to obtain the actual observed QoE distribution in the system' Moreover, how can various QoE metrics of interest in the system be derived' We prove fundamental relationships for the derivation of QoE in systems, thus providing an important link between the QoE community and the systems community. In our numerical examples, we focus mainly on QoE metrics. We furthermore provide a more generalized view on quantifying the quality of systems by defining a QoE-based Service-level Quality Index. This index exploits the fact that quality can be seen as a proxy measure for utility. Following the assumption that not all user sessions should be weighted equally, we aim to provide a generic framework that can be utilized to quantify the overall utility of a service delivered by a system. PubDate: 2020-06-23 DOI: 10.1007/s41233-020-00035-0
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Abstract: Abstract The i-vector framework has been widely used to summarize speaker-dependent information present in a speech signal. Considered the state-of-the-art in speaker verification for many years, its potential to estimate speech recording distortion/quality has been overlooked. This paper is an attempt to fill this gap. We conduct a detailed analysis of how distortions are captured in the total variability space. We then propose a full-reference speech quality model based on i-vector similarities and three no-reference approaches. The first no-reference approach makes use of a single reference i-vector based on the average of i-vectors extracted from clean signals. A second approach relies on a vector quantizer codebook of representative clean speech i-vectors. Lastly, i-vectors and subjective ratings were used to train a no-reference deep neural network model for speech quality assessment. Four experiments have shown that the proposed methods, based on the i-vector speech representation, are well-suited for assessing speech quality. Results show correlations with subjective quality judgments similar to those achieved with standardized instrumental algorithms, particularly for degradations caused by noise and reverberation.ϖ PubDate: 2020-06-20 DOI: 10.1007/s41233-020-00036-z
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Abstract: Abstract The satisfied user ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the complementary cumulative distribution function of the just noticeable difference (JND), the smallest distortion level that can be perceived by a subject when a reference image is compared to a distorted one. A sequence of JNDs can be defined with a suitable successive choice of reference images. We propose the first deep learning approach to predict SUR curves. We show how to apply maximum likelihood estimation and the Anderson–Darling test to select a suitable parametric model for the distribution function. We then use deep feature learning to predict samples of the SUR curve and apply the method of least squares to fit the parametric model to the predicted samples. Our deep learning approach relies on a siamese convolutional neural network, transfer learning, and deep feature learning, using pairs consisting of a reference image and a compressed image for training. Experiments on the MCL-JCI dataset showed state-of-the-art performance. For example, the mean Bhattacharyya distances between the predicted and ground truth first, second, and third JND distributions were 0.0810, 0.0702, and 0.0522, respectively, and the corresponding average absolute differences of the peak signal-to-noise ratio at a median of the first JND distribution were 0.58, 0.69, and 0.58 dB. Further experiments on the JND-Pano dataset showed that the method transfers well to high resolution panoramic images viewed on head-mounted displays. PubDate: 2020-05-04 DOI: 10.1007/s41233-020-00034-1
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Abstract: Abstract Omnidirectional video (ODV) enables viewers to look at every direction from a fixed point and provides a much more immersive experience than traditional 2D video. Assessing the video quality is important for delivering ODV to the end-user with the best possible quality. For this goal, two aspects of ODV should be considered. The first is the spherical nature of ODV and the related projection distortions when the ODV is stored in a planar format. The second is the interactive look-around consumption nature of ODV. Related to this aspect, visual attention, that identifies the regions that attract the viewer’s attention, is important for ODV quality assessment. Considering these aspects, in this paper, we study in particular objective full-reference quality assessment for ODV. To this end, we propose a quality assessment framework based on the spherical Voronoi diagram and visual attention. In this framework, a given ODV is subdivided into multiple planar patches with low projection distortions using the spherical Voronoi diagram. Afterwards, each planar patch is analyzed separately by a quality metric for traditional 2D video, obtaining a quality score for each patch. Then, the patch scores are combined based on visual attention into a final quality score. To validate the proposed framework, we create a dataset of ODVs with scaling and compression distortions, and conduct subjective experiments in order to gather the subjective quality scores and the visual attention data for our ODV dataset. The evaluation of the proposed framework based on our dataset shows that both the use of the spherical Voronoi diagram and visual attention are crucial for achieving state-of-the-art performance. PubDate: 2020-04-27 DOI: 10.1007/s41233-020-00032-3
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Abstract: Abstract For chatbots to be broadly adopted by users, it is critical that they are experienced as useful and pleasurable. While there is an emerging body of research concerning user uptake and use of chatbots, there is a lack of theoretically grounded studies detailing what constitutes good or poor chatbot user experiences. In this paper, we present findings from a questionnaire study involving more than 200 chatbot users who reported on episodes of chatbot use that they found particularly satisfactory or frustrating. The user reports were analysed with basis in theory on user experience, with particular concern for pragmatic and hedonic attributes. We found that pragmatic attributes such as efficient assistance (positive) and problems with interpretation (negative) were important elements in user reports of satisfactory and frustrating episodes. Hedonic attributes such as entertainment value (positive) and strange and rude responses (negative) were also frequently mentioned. Older participants tended to report on pragmatic attributes more often, whereas younger participants tended to report on hedonic attributes more often. Drawing on the findings, we propose four high-level lessons learnt that may benefit chatbot service providers, and we suggest relevant future research. PubDate: 2020-04-11 DOI: 10.1007/s41233-020-00033-2
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Abstract: Abstract Virtual and augmented reality is increasingly prevalent in industrial applications, such as remote control of industrial machinery, due to recent advances in head-mounted display technologies and low-latency communications via 5G. However, the influence of augmentations and camera placement-based viewing positions on operator performance in telepresence systems remains unknown. In this paper, we investigate the joint effects of depth-aiding augmentations and viewing positions on the quality of experience for operators in augmented telepresence systems. A study was conducted with 27 non-expert participants using a real-time augmented telepresence system to perform a remote-controlled navigation and positioning task, with varied depth-aiding augmentations and viewing positions. The resulting quality of experience was analyzed via Likert opinion scales, task performance measurements, and simulator sickness evaluation. Results suggest that reducing the reliance on stereoscopic depth perception via camera placement has a significant benefit to operator performance and quality of experience. Conversely, the depth-aiding augmentations can partly mitigate the negative effects of inferior viewing positions. However the viewing-position based monoscopic and stereoscopic depth cues tend to dominate over cues based on augmentations. There is also a discrepancy between the participants’ subjective opinions on augmentation helpfulness, and its observed effects on positioning task performance. PubDate: 2020-02-10 DOI: 10.1007/s41233-020-0031-7
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