Towards still image experience predictions in augmented vision settings

Brian Bauman, Patrick Seeling

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

With the emergence of Augmented Reality (AR) services in a broad range of application scenarios, the interplay of (compressed) content as delivered and quality as experienced by the user becomes increasingly important. While significant research efforts have uncovered interplays for traditional (opaque) media consumption scenarios, applications in augmented vision scenarios pose significant challenges. This paper focuses on the Quality of Experience (QoE) for a grounded truth reference still image set. We corroborate previous findings which indicate that there is only limited QoE benefit obtainable from very high image quality levels. The main contribution is the first evaluation of several popular objective image quality metrics and their relationships to the QoE in opaque and vision augmenting presentation formats. For the first time, we provide an assessment of the possibility to predict the QoE based on these metrics. We find that linear regression of a small degree is able to accurately capture the coefficients and fairly accurate prediction can be performed even with a non-referential image quality metric as parameter. We note, however, that prediction accuracy still fluctuates with the image content and subjects removed from the data set. Our overall findings can be employed by AR service providers to perform an optimized delivery of still image content.

Original languageEnglish
Title of host publication2017 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1038-1043
Number of pages6
ISBN (Electronic)9781509061969
DOIs
StatePublished - Jul 17 2017
Event14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017 - Las Vegas, United States
Duration: Jan 8 2017Jan 11 2017

Publication series

Name2017 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017

Conference

Conference14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017
Country/TerritoryUnited States
CityLas Vegas
Period01/8/1701/11/17

Keywords

  • Augmented reality
  • Image quality
  • Multimedia systems
  • Quality of experience
  • Quality of service

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