Visual quality assessment of reconstructed background images

Aditee Shrotre, Lina Karam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

A clean background image is of great importance in multiple applications such as video surveillance, object tracking and context-based video encoding, but acquiring a clean background image in public areas is seldom possible. Many algorithms have been developed to initialize the background from videos and images. This paper presents a database consisting of 13 different scenes that can be used for benchmarking the performance of background initialization algorithms. We also conducted a subjective study on the perceptual quality of background images that are reconstructed using existing background initialization algorithms. The obtained subjective scores are used to evaluate existing image quality metrics and their capability in predicting the perceived quality of reconstructed background images.

Original languageEnglish (US)
Title of host publication2016 8th International Conference on Quality of Multimedia Experience, QoMEX 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509003549
DOIs
StatePublished - Jun 23 2016
Event8th International Conference on Quality of Multimedia Experience, QoMEX 2016 - Lisbon, Portugal
Duration: Jun 6 2016Jun 8 2016

Other

Other8th International Conference on Quality of Multimedia Experience, QoMEX 2016
CountryPortugal
CityLisbon
Period6/6/166/8/16

Keywords

  • Background Reconstruction
  • Image Database
  • Image Quality Assessment
  • Perceptual Quality
  • Subjective Evaluation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Media Technology
  • Communication

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  • Cite this

    Shrotre, A., & Karam, L. (2016). Visual quality assessment of reconstructed background images. In 2016 8th International Conference on Quality of Multimedia Experience, QoMEX 2016 [7498954] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/QoMEX.2016.7498954