In the past 20 years the "Quality of Experience" (QoE) has increasingly become a necessary
aspect to be taken into account by the multimedia industry. In fact, the QoE has allowed
to extend the concept of image quality by considering other aspects to evaluate "the
delight or annoyance of a customer's experiences with a service" [1]. The concept
of QoE is extremely important since it has a big impact on several fields, ranging
from the acquisition to the rendering of multimedia contents.

Many works can be found in literature that address the problem of evaluating the
QoE for 2D and 3D contents but nowadays we are facing a new challenging task: to study
the QoE for immersive media. When talking about immersive media we are referring to
different types of multimedia that allow the users to explore the content in their
personal way thus introducing a new and important variable to be considered while
evaluating the QoE.

In particular, recent years have witnessed the spread of Virtual and Augmented Reality
that allow the users to be involved in a more realistic and deep way in the artificial
multimedia content or to interact with virtual objects [2]. In this new scenario we
are moving to the concept of "personal" QoE that encompasses many open questions that
are still unanswered such as: which is the impact of the rendering system on the QoE,
how important are the viewing conditions, how can we account for the immersive media
content way of exploration, can the study of multimedia saliency help in understanding
the QoE [3]?

In this talk we will address these open questions to explore what are the current
research findings and trends and I will give you an insight of what will come next.

In recent times, video content encoded at High-Definition (HD) and Ultra-High-Definition
(UHD) resolution dominates internet traffic. The significantly increased data rate
and growing expectations of video quality from users create great challenges in video
compression and quality assessment, especially for higher-resolution, higher-quality
content. The development of robust video quality assessment metrics relies on the
collection of subjective ground truths. As high-quality video content is more ambiguous
and difficult for a human observer to rate, a more distinguishable subjective protocol/methodology
should be considered. In this study, towards better quality assessment of high-quality
videos, a subjective study was conducted focusing on high-quality HD and UHD content
with the Degradation Category Rating (DCR) protocol. Commonly used video quality metrics
were benchmarked in two quality ranges.

Containing full panoramic content in a single frame and providing immersive experience
for users, 360° video has attracted great attention in industry and academia. Viewport-driven
tiling schemes have been introduced in 360° video processing to provide high-quality
video streaming. However, treating viewport as traditional streaming screen results
in frequently rebuffer or quality distortion, leading to poor Quality of Experience
(QoE) of schemes. In this paper, we propose Viewpoint Movement Perception 360° Video
Streaming (VMP360), an adaptive 360° video streaming system that utilizes unique factors
of 360° video perception quality of users to improve the overall QoE. By studying
the relative moving speed and depth difference between the viewpoint and other content,
the system evaluates the perceived quality distortion based on optical flow estimation.
Taking QoE into account, a novel 360° video quality evaluation metric is defined as
Optical-flow-based Peak Signal-to-Noise Ratio (OPSNR). Appling OPSNR to tiling process,
VMP360 proposes a versatile-size tiling scheme, and further Reinforcement Learning
(RL) is used to realize the Adaptive Bit Rate (ABR) selection of tiles. VMP360 is
evaluated through the client-server streaming system with two prior schemes Pano and
Plato. Statistics show that the proposed scheme can improve the quality of 360° video
by 10.1% while maintaining same rebuffer ratio compared with the Pano and Plato, which
confirms that VMP360 can provide a promising high QoE for 360° video streaming. The
code of a prototype can be found in https://github.com/buptexplorers/OFB-VR.

Crowdsourced testing is an increasingly popular way to study the quality of experience
(QoE) of applications, such as video streaming and web. The diverse nature of the
crowd provides a more realistic assessment environment than laboratory-based assessments
allow. Because of the short life-span of crowdsourcing tasks, each subject spends
a significant fraction of the experiment time just learning how it works. We propose
a novel experiment design to conduct a longitudinal crowdsourcing study aimed at improving
the efficiency of crowdsourced QoE assessments. On Amazon Mechanical Turk, we found
that our design was 20% more cost-effective than crowdsourcing multiple one-off short
experiments. Our results showed that subjects had a high level of revisit intent and
continuously participated in our experiments. We replicated the video streaming QoE
assessments in a traditional laboratory setting. Our study showed similar trends in
the relationship between video bitrate and QoE, which confirm findings in prior research.

Objective video quality metrics are an essential part of modern video processing pipelines,
guiding video encoding decisions and encoding recipes, helping adaptive bitrate streaming
algorithms make smart decisions and providing system-level monitoring capabilities.
We will offer a breakdown of an end-to-end such pipeline, highlighting which types
of video quality metrics are deployed in each system component and then focus on the
single aspect that makes social videos so much different - and one can argue more
difficult - to process: their wildly varying and typically inferior source quality.
We will then discuss how no-reference video quality metrics have been typically used
to measure user-generated video content quality with limited success and make a case
for how the video industry can unite and solve this problem at its root.

This paper provides insights on how to perceptually characterize colored 3D Graphical
Contents (3DGC). In this study, pre-defined viewpoints were considered to render static
graphical objects. For perceptual characterization, we used visual attention complexity
(VAC) measures. Considering a view-based approach to exploit the perceived information,
an eye-tracking experiment was conducted using colored graphical objects.

Based on the collected gaze data, we revised the VAC measure, suggested in 2D imaging
context, and adapted it to 3DGC. We also provided an objective predictor that highly
mimics the experimental attentional complexity information. This predictor can be
useful in Quality of Experience (QoE) studies: to balance content selection when benchmarking
3DGC processing techniques (e.g., rendering, coding, streaming, etc.) for human panel
studies or ad hoc key performance indicator, and also to optimize the user's QoE when
rendering such contents.

As cloud gaming and Virtual Reality (VR) games become popular in the game industry,
game developers engage in these fields to boost their sales. Because cloud gaming
possesses the merit of lifting computation loads from client devices to servers, it
solves the high resource consumption issue of VR games on regular clients. However,
it is important to know where is the bottleneck of the cloud VR gaming platform and
how can it be improved in the future. In this paper, we conduct extensive experiments
on the state-of-the-art cloud VR gaming platform--Air Light VR (ALVR). In particular,
we analyze the performance of ALVR using both Quality-of-Service and Quality-of-Experience
metrics. Our experiments reveal that latency (up to 90 ms RTT) has less influence
on user experience compared to bandwidth limitation (as small as 35 Mbps) and packet
loss rate (as high as 8%) . Moreover, we find that VR gamers can hardly notice the
difference between the gaming experience with different latency values (between 0
and 90 ms RTT). Such findings shed some lights on how to further improve the cloud
VR gaming platform, e.g., a budget of up to 90 ms RTT may be used to absorb network
dynamics when bandwidth is insufficient.

Nowadays, mobile gaming has become one of the most rapidly developing fields boosted
by fast-evolving techniques, which also gradually becomes one of the biggest parts
of modern digital entertainment. With the exponential growth of users, published mobile
games, and higher expectations for gaming experiences, multi-dimensional aesthetic
assessment is essential in providing guidance for graphic/game developers, quality
control of the overall gaming system, and achieving a better trade-off between gaming
image quality and the rendering complexity (limited by the device performance). So
far, most of the relative researches have been limited to only one dimension evaluation
e.g., quality assessment considering streaming artifacts, which neglects other important
aesthetic-related perspectives. In this paper, a comprehensive subjective study is
presented considering multi-dimensional aesthetic factors (i.e., the fineness, color
harmony, colorfulness, and overall quality) of mobile gaming images. Throughout extensive
conducted experiments on the collected large-scale dataset, we discuss the relationships
between different dimensions, and benchmark different image metrics designed for various
purposes.