Deep Blind Video Quality Assessment Based on Temporal Human Perception

Sewoong Ahn, Sanghoon Lee

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

12 Citations (Scopus)

Abstract

The high performance video quality assessment (VQA) algorithm is a necessary skill to provide high quality video to viewers. However, since the nonlinear perception function between the distortion level of the video and the subjective quality score is not precisely defined, there are many limitations in accurately predicting the quality of the video. In this paper, we propose a deep learning scheme named Deep Blind Video Quality Assessment (DeepBVQA) to achieve a more accurate and reliable video quality predictor by considering various spatial and temporal cues which have not been considered before. We used CNN to extract the spatial cues of each video in VQA and proposed new hand-crafted features for temporal cues. Performance experiments show that performance is better than other state-of-the-art no-reference (NR) VQA models and the introduction of hand-crafted temporal features is very efficient in VQA.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PublisherIEEE Computer Society
Pages619-623
Number of pages5
ISBN (Electronic)9781479970612
DOIs
Publication statusPublished - 2018 Aug 29
Event25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
Duration: 2018 Oct 72018 Oct 10

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference25th IEEE International Conference on Image Processing, ICIP 2018
CountryGreece
CityAthens
Period18/10/718/10/10

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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