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Subjective assessment and instrumental prediction of mobile online gaming on the basis of perceptual dimensions


The assessment of the perceived quality of the user (Quality of Experience) of pure audio and video material differs in many ways from the quality of computer games. The later possess a variety of factors due to their interactive nature. Not only factors of complex and innovative game systems have an impact on the QoE, but also the players themselves. A quality judgment, that results by comparing the expected and perceived composition of an entity, depends highly on the preferences, expectations and abilities of the player.

In this still young area of research standard methods for determining the QoE are not directly applicable. This is apparent since in a task oriented human-computer interaction the goal should be achieved with minimal effort. However in a game  the player exerts an effort in order to influence the outcome and thereby feels emotionally attached. Thus new concepts such as immersion and flow, a state of happiness while being in an equilibrium between competence and challenge, appear.

An analysis of the gaming market shows, that the proportion of mobile games has risen sharply in recent years. Mobile games are special in a sense that mobile devices such as smartphones or tablets were originally not designed for games and are therefore not optimally adapted to them. This also applies to the new concept of cloud gaming, where the entire game is executed on a server and only the video and audio material is transferred to the end user.


Aim of the project

The aim of this research project is to develop methods to assess the QoE of mobile games. In addition, based on a database containing subjective quality judgments, a model similar to the well known E-model should be constructed to predict the QoE. The following concrete steps are planned for this purpose:

  • Set up and modification of a testbed for conducting experiments including a cloud gaming system for mobile games
  • Development of a classification of games to choose representative games and identify system and user factors
  • Building a questionnaire covering a large space of relevant quality dimensions
  • Identification of quality-relevant perceptual dimensions and analysis of their impact on the overall quality
  • Analyzing the performance of current objective metrics which were proposed for different contents and services in mobile gaming
  • Building a QoE model based on game, system and network characteristics as well as user and context factors
Time Frame: 
01/2016 - 06/2019
T-labs Team Members:
Steven Schmidt
Funding by:
Deutsche Forschungsgemeinschaft (DFG)
Project Number:
MO 1038/21-1

List of Publications

An objective and subjective quality assessment study of passive gaming video streaming
Citation key barman2018d
Author Barman, Nabajeet and Zadtootaghaj, Saman and Schmidt, Steven and Martini, Maria G. and Möller, Sebastian
Pages e2054
Year 2018
DOI 10.1002/nem.2054
Address Piscataway, NJ
Journal International Journal of Network Management
Month may
Note electronic
Publisher Hoboken, New Jersey
How Published full
Abstract Summary Passive gaming video-streaming applications have recently gained much attention as evident with the rising popularity of many Over The Top (OTT) providers such as Twitch.tv and YouTube Gaming. For the continued success of such services, it is imperative that the user Quality of Experience (QoE) remains high, which is usually assessed using subjective and objective video quality assessment methods. Recent years have seen tremendous advancement in the field of objective video quality assessment (VQA) metrics, with the development of models that can predict the quality of the videos streamed over the Internet. A study on the performance of objective VQA on gaming videos, which are artificial and synthetic and have different streaming requirements than traditionally streamed videos, is still missing. Towards this end, we present in this paper an objective and subjective quality assessment study on gaming videos considering passive streaming applications. Subjective ratings are obtained for 90 stimuli generated by encoding six different video games in multiple resolution-bitrate pairs. Objective quality performance evaluation considering eight widely used VQA metrics is performed using the subjective test results and on a data set of 24 reference videos and 576 compressed sequences obtained by encoding them in 24 resolution-bitrate pairs. Our results indicate that Video Multimethod Assessment Fusion (VMAF) predicts subjective video quality ratings the best, while Naturalness Image Quality Evaluator (NIQE) turns out to be a promising alternative as a no-reference metric in some scenarios.
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