Deep Video Quality Assessor: From Spatio-Temporal Visual Sensitivity to a Convolutional Neural Aggregation Network

Woojae Kim, Jongyoo Kim, Sewoong Ahn, Jinwoo Kim, Sanghoon Lee

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

1 Citation (Scopus)

Abstract

Incorporating spatio-temporal human visual perception into video quality assessment (VQA) remains a formidable issue. Previous statistical or computational models of spatio-temporal perception have limitations to be applied to the general VQA algorithms. In this paper, we propose a novel full-reference (FR) VQA framework named Deep Video Quality Assessor (DeepVQA) to quantify the spatio-temporal visual perception via a convolutional neural network (CNN) and a convolutional neural aggregation network (CNAN). Our framework enables to figure out the spatio-temporal sensitivity behavior through learning in accordance with the subjective score. In addition, to manipulate the temporal variation of distortions, we propose a novel temporal pooling method using an attention model. In the experiment, we show DeepVQA remarkably achieves the state-of-the-art prediction accuracy of more than 0.9 correlation, which is \sim 5% higher than those of conventional methods on the LIVE and CSIQ video databases.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
EditorsMartial Hebert, Vittorio Ferrari, Cristian Sminchisescu, Yair Weiss
PublisherSpringer Verlag
Pages224-241
Number of pages18
ISBN (Print)9783030012458
DOIs
Publication statusPublished - 2018 Jan 1
Event15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
Duration: 2018 Sep 82018 Sep 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11205 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th European Conference on Computer Vision, ECCV 2018
CountryGermany
CityMunich
Period18/9/818/9/14

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Kim, W., Kim, J., Ahn, S., Kim, J., & Lee, S. (2018). Deep Video Quality Assessor: From Spatio-Temporal Visual Sensitivity to a Convolutional Neural Aggregation Network. In M. Hebert, V. Ferrari, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings (pp. 224-241). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11205 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-01246-5_14