Reduced reference image quality assessment via Boltzmann Machines

Decebal Constantin Mocanu, Georgios Exarchakos, Haitham Bou Ammar, Antonio Liotta

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

17 Citations (Scopus)

Abstract

Monitoring and controlling the user's perceived quality, in modern video services is a challenging proposition, mainly due to the limitations of current Image Quality Assessment (IQA) algorithms. Subjective Quality of Experience (QoE) is widely used to get a right impression, but unfortunately this can not be used in real world scenarios. In general, objective QoE algorithms represent a good substitution for the subjective ones, and they are split in three main directions: Full Reference (FR), Reduced Reference (RR), and No Reference (NR). From these three, the RR IQA approach offers a practical solution to assess the quality of an impaired image due to the fact that just a small amount of information is needed from the original image. At the same time, keeping in mind that we need automated QoE algorithms which are context independent, in this paper we introduce a novel stochastic RR IQA metric to assess the quality of an image based on Deep Learning, namely Restricted Boltzmann Machine Similarity Measure (RBMSim). RBMSim was evaluated on two benchmarked image databases with subjective studies, against objective IQA algorithms. The results show that its performance is comparable, or even better in some cases, with widely known FR IQA methods.

Original languageEnglish
Title of host publication2015 IFIP/IEEE International Symposium on Integrated Network Management, IM 2015
EditorsFilip De Turck, Remi Badonnel, Carlos Raniery P. dos Santos, Jin Xiao, Shingo Ata, Voicu Groza
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1278-1281
Number of pages4
ISBN (Electronic)9783901882760
DOIs
Publication statusPublished - 29 Jun 2015
Externally publishedYes
Event14th IFIP/IEEE International Symposium on Integrated Network Management, IM 2015: Integrated Management in the Age of Big Data - Shaw Centre, Ottawa, Canada
Duration: 11 May 201515 May 2015
Conference number: 14
http://im2015.ieee-im.org/

Publication series

NameProceedings of the IFIP/IEEE International Symposium on Integrated Network Management (IM)
PublisherIEEE
Volume2015
ISSN (Print)1573-0077

Conference

Conference14th IFIP/IEEE International Symposium on Integrated Network Management, IM 2015
Abbreviated titleIM 2015
CountryCanada
CityOttawa
Period11/05/1515/05/15
Internet address

Keywords

  • Deep learning
  • Quality of experience
  • Reduced reference image quality assessment
  • Restricted Boltzmann machines
  • Similarity measure

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