9,792 research outputs found
QoE Modelling, Measurement and Prediction: A Review
In mobile computing systems, users can access network services anywhere and
anytime using mobile devices such as tablets and smart phones. These devices
connect to the Internet via network or telecommunications operators. Users
usually have some expectations about the services provided to them by different
operators. Users' expectations along with additional factors such as cognitive
and behavioural states, cost, and network quality of service (QoS) may
determine their quality of experience (QoE). If users are not satisfied with
their QoE, they may switch to different providers or may stop using a
particular application or service. Thus, QoE measurement and prediction
techniques may benefit users in availing personalized services from service
providers. On the other hand, it can help service providers to achieve lower
user-operator switchover. This paper presents a review of the state-the-art
research in the area of QoE modelling, measurement and prediction. In
particular, we investigate and discuss the strengths and shortcomings of
existing techniques. Finally, we present future research directions for
developing novel QoE measurement and prediction technique
No-reference bitstream-based visual quality impairment detection for high definition H.264/AVC encoded video sequences
Ensuring and maintaining adequate Quality of Experience towards end-users are key objectives for video service providers, not only for increasing customer satisfaction but also as service differentiator. However, in the case of High Definition video streaming over IP-based networks, network impairments such as packet loss can severely degrade the perceived visual quality. Several standard organizations have established a minimum set of performance objectives which should be achieved for obtaining satisfactory quality. Therefore, video service providers should continuously monitor the network and the quality of the received video streams in order to detect visual degradations. Objective video quality metrics enable automatic measurement of perceived quality. Unfortunately, the most reliable metrics require access to both the original and the received video streams which makes them inappropriate for real-time monitoring. In this article, we present a novel no-reference bitstream-based visual quality impairment detector which enables real-time detection of visual degradations caused by network impairments. By only incorporating information extracted from the encoded bitstream, network impairments are classified as visible or invisible to the end-user. Our results show that impairment visibility can be classified with a high accuracy which enables real-time validation of the existing performance objectives
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
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