Quality of experience (QoE) is the perceptual quality of service (QoS) from the users' perspective. For video service, the relationship between QoE and QoS (such as coding parameters and network statistics) is complicated because users' perceptual video quality is subjective and diversified in different environments. Traditionally, QoE is obtained from subjective test, where human viewers evaluate the quality of tested videos under a laboratory environment. To avoid high cost and offline nature of such tests, objective quality models are developed to predict QoE based on objective QoS parameters, but it is still an indirect way to estimate QoE. With the rising popularity of video streaming over the Internet, data-driven QoE analysis models have newly emerged due to availability of large-scale data. In this paper, we give a comprehensive survey of the evolution of video quality assessment methods, analyzing their characteristics, advantages, and drawbacks. We also introduce QoE-based video applications and, finally, identify the future research directions of QoE.

From QoS to QoE: A Tutorial on Video Quality Assessment

Perceptual quality assessmentplays a vital role in the visual communication systems owing to theexistence of quality degradations introduced in various stages of visual signalacquisition, compression, transmission and display.Quality assessment for visual signals can be performed subjectively andobjectively, and objective quality assessment is usually preferred owing to itshigh efficiency and easy deployment. A large number of subjective andobjective visual quality assessment studies have been conducted during recent years.In this survey, we give an up-to-date and comprehensivereview of these studies.Specifically, the frequently used subjective image quality assessment databases are firstreviewed, as they serve as the validation set for the objective measures.Second, the objective image quality assessment measures are classified and reviewed according to the applications and the methodologies utilized in the quality measures.Third, the performances of the state-of-the-artquality measures for visual signals are compared with an introduction of theevaluation protocols.This survey provides a general overview of classical algorithms andrecent progresses in the field of perceptual image quality assessment.

Perceptual image quality assessment: a survey

This paper explores gaze prediction in dynamic 360° immersive videos, i.e., based on the history scan path and VR contents, we predict where a viewer will look at an upcoming time. To tackle this problem, we first present the large-scale eye-tracking in dynamic VR scene dataset. Our dataset contains 208 360° videos captured in dynamic scenes, and each video is viewed by at least 31 subjects. Our analysis shows that gaze prediction depends on its history scan path and image contents. In terms of the image contents, those salient objects easily attract viewers' attention. On the one hand, the saliency is related to both appearance and motion of the objects. Considering that the saliency measured at different scales is different, we propose to compute saliency maps at different spatial scales: the sub-image patch centered at current gaze point, the sub-image corresponding to the Field of View (FoV), and the panorama image. Then we feed both the saliency maps and the corresponding images into a Convolutional Neural Network (CNN) for feature extraction. Meanwhile, we also use a Long-Short-Term-Memory (LSTM) to encode the history scan path. Then we combine the CNN features and LSTM features for gaze displacement prediction between gaze point at a current time and gaze point at an upcoming time. Extensive experiments validate the effectiveness of our method for gaze prediction in dynamic VR scenes.

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Gaze Prediction in Dynamic 360° Immersive Videos

Nowadays, 360° video/image has been increasingly popular and drawn great attention. The spherical viewing range of 360° video/image accounts for huge data, which pose the challenges to 360° video/image processing in solving the bottleneck of storage, transmission, etc. Accordingly, the recent years have witnessed the explosive emergence of works on 360° video/image processing. In this article, we review the state-of-the-art works on 360° video/image processing from the aspects of perception, assessment and compression. First, this article reviews both datasets and visual attention modelling approaches for 360° video/image. Second, we survey the related works on both subjective and objective visual quality assessment (VQA) of 360° video/image. Third, we overview the compression approaches for 360° video/image, which either utilize the spherical characteristics or visual attention models. Finally, we summarize this overview article and outlook the future research trends on 360° video/image processing.

State-of-the-Art in 360° Video/Image Processing: Perception, Assessment and Compression

In this chapter different factors that may influence Quality of Experience (QoE) in the context of media consumption, networked services, and other electronic communication services and applications, are discussed. QoE can be subject to a range of complex and strongly interrelated factors, falling into three categories: human, system and context influence factors (IFs). With respect to Human IFs, we discuss variant and stable factors that may potentially bear an influence on QoE, either for low-level (bottom-up) or higher-level (top-down) cognitive processing. System IFs are classified into four distinct categories, namely content-, media-, network- and device-related IFs. Finally, the broad category of possible Context IFs is decomposed into factors linked to the physical, temporal, social, economic, task and technical information context. The overview given here illustrates the complexity of QoE and the broad range of aspects that potentially have a major influence on it.

Factors Influencing Quality of Experience

Understanding how observers watch visual stimuli like Images and Videos has helped the multimedia encoding, transmission, quality assessment and rendering communities immensely, to learn the regions important to an observer and provide to him/her an optimum quality of experience. The problem is even more paramount in case of 360 degree stimuli considering that most/a part of the content might not be seen by the observers at all, while other regions maybe extraordinarily important. Attention studies in this area has however been missing, mainly due to the lack of a dataset and guidelines to evaluate and compare visual attention/saliency in such scenarios. In this work, we present a dataset of sixty different 360 degree images, each watched by at-least 40 observers. Additionally, we also provide guidelines and tools to the community regarding the procedure to evaluate and compare saliency in omni-directional images. Some basic image/ observer agnostic viewing characteristics, like variation of exploration strategies with time and expertise, and also the effect of eye-movement within the view-port are explored. The dataset and tools are made available for free use by the community and is expected to promote Reproducible Research for all future work on computational modeling of attention in 360 scenarios.

A Dataset of Head and Eye Movements for 360 Degree Images

Research on visual attention in 360° content is crucial to understand how people perceive and interact with this immersive type of content and to develop efficient techniques for processing, encoding, delivering and rendering. And also to offer a high quality of experience to end users. The availability of public datasets is essential to support and facilitate research activities of the community. Recently, some studies have been presented analyzing exploration behaviors of people watching 360° videos, and a few datasets have been published. However, the majority of these works only consider head movements as proxy for gaze data, despite the importance of eye movements in the exploration of omnidirectional content. Thus, this paper presents a novel dataset of 360° videos with associated eye and head movement data, which is a follow-up to our previous dataset for still images [14]. Head and eye tracking data was obtained from 57 participants during a free-viewing experiment with 19 videos. In addition, guidelines on how to obtain saliency maps and scanpaths from raw data are provided. Also, some statistics related to exploration behaviors are presented, such as the impact of the longitudinal starting position when watching omnidirectional videos was investigated in this test. This dataset and its associated code are made publicly available to support research on visual attention for 360° content.

https://hal.archives-ouvertes.fr/hal-01994923/document

A dataset of head and eye movements for 360° videos

Research in stereoscopic 3D coding, transmission and subjective assessment methodology depends largely on the availability of source content that can be used in cross-lab evaluations. While several studies have already been presented using proprietary content, comparisons between the studies are difficult since discrepant contents are used. Therefore in this paper, a freely available dataset of high quality Full-HD stereoscopic sequences shot with a semiprofessional 3D camera is introduced in detail. The content was designed to be suited for usage in a wide variety of applications, including high quality studies. A set of depth maps was calculated from the stereoscopic pair. As an application example, a subjective assessment has been performed using coding and spatial degradations. The Absolute Category Rating with Hidden Reference method was used. The observers were instructed to vote on video quality only. Results of this experiment are also freely available and will be presented in this paper as a first step towards objective video quality measurement for 3DTV.

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NAMA3DS1-COSPAD1: Subjective video quality assessment database on coding conditions introducing freely available high quality 3D stereoscopic sequences

Research on understanding how people watch/explore Virtual Reality (VR) and 360° content is crucial to develop appropriate technologies for processing, encoding, delivering and rendering media content in order to provide high-quality immersive experiences to users. In this sense, the work described in this paper aims at supporting the research on visual attention modeling for omnidirectional content, by presenting a dataset and toolbox that were created for the Grand Challenge “Salient360!” organized at ICME’17. In particular, the dataset includes 360° images and their corresponding saliency maps and scanpaths, obtained from a subjective experiment in which eye gazes and head movements of observers were recorded during a free-viewing task. In addition, guidelines to obtain saliency maps and scanpaths from eye and head tracking data for omnidirectional images are provided. Finally, together with the dataset, a toolbox has been made publicly available, containing scripts for comparing saliency maps and scanpaths to benchmark developed models.

Toolbox and dataset for the development of saliency and scanpath models for omnidirectional/360° still images

Social difficulties are the hallmark features of Autism Spectrum Disorder (ASD) and can lead to atypical visual attention towards stimuli. Eye movements encode rich information about attention and psychological factors of an individual, which could help to characterize the traits of ASD. Learning atypical eye movements of the individuals with ASD towards various stimuli is important and has many application scenarios. However, due to the lack of open datasets, research in this sense is still limited. In this work, we present an open dataset of eye movements of children with Autism Spectrum Disorder. It consists of 300 natural scene images and the corresponding eye movement data collected from 14 children with ASD and 14 healthy controls. In particular, fixation maps and scanpaths are available in the dataset. Based on this dataset, researchers could analyze the visual traits of children with ASD and design specialized visual attention models to promote research in related fields, as well as design specialized models to identify the individuals with ASD. The dataset can be accessed in http://doi.org/10.5281/zenodo.2647418

Jesus Gutierrez

Papers

A Dataset of Head and Eye Movements for 360 Degree Images

A dataset of head and eye movements for 360° videos

NAMA3DS1-COSPAD1: Subjective video quality assessment database on coding conditions introducing freely available high quality 3D stereoscopic sequences

Toolbox and dataset for the development of saliency and scanpath models for omnidirectional/360° still images

A dataset of eye movements for the children with autism spectrum disorder