Deep learning for objective quality assessment of 3D images

Decebal Constantin Mocanu, Georgios Exarchakos, Antonio Liotta

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

28 Citations (Scopus)

Abstract

Improving the users' Quality of Experience (QoE) in modern 3D Multimedia Systems is a challenging proposition, mainly due to our limited knowledge of 3D image Quality Assessment algorithms. While subjective QoE methods would better reflect the nature of human perception, these are not suitable in real-time automation cases. In this paper we tackle this issue from a new angle, using deep learning to make predictions on the user's QoE rather than trying to measure it through deterministic algorithms. We benchmark our method, dubbed Quality of Experience for 3D images through Factored Third Order Restricted Boltzmann Machine (Q3D-RBM), with subjective QoE methods, to determine its accuracy for different types of 3D images. The outcome is a Reduced Reference QoE assessment process for automatic image assessment and has significant potential to be extended to work on 3D video assessment.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages758-762
Number of pages5
ISBN (Electronic)978-1-4799-5751-4
DOIs
Publication statusPublished - 28 Jan 2014
Externally publishedYes

Publication series

Name2014 IEEE International Conference on Image Processing, ICIP 2014
PublisherIEEE
Volume2014
ISSN (Print)1522-4880
ISSN (Electronic)2381-8549

Keywords

  • Deep learning
  • Quality of experience
  • Reduced reference 3D image quality assessment
  • Third order restricted boltzmann machine
  • Unsupervised learning

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