Deep Neural Networks for No-Reference Video Quality Assessment

Junyong You*, Jari Korhonen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

69 Citations (Scopus)

Abstract

Video quality assessment (VQA) is a challenging task due to the complexity of modeling perceived quality characteristics in both spatial and temporal domains. A novel no-reference (NR) video quality metric (VQM) is proposed in this paper based on two deep neural networks (NN), namely 3D convolution network (3D-CNN) and a recurrent NN composed of long short-term memory (LSTM) units. 3D-CNNs are utilized to extract local spatiotemporal features from small cubic clips in video, and the features are then fed into the LSTM networks to predict the perceived video quality. Such design can elaborately tackle the issue of insufficient training data whilst also efficiently capture perceptive quality features in both spatial and temporal domains. Experimental results with respect to two publicly available video quality datasets have demonstrate that the proposed quality metric outperforms the other compared NR quality metrics.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing (ICIP)
PublisherIEEE Explore
Pages2349-2353
Number of pages5
ISBN (Electronic)978-1-5386-6249-6
ISBN (Print)978-1-5386-6250-2
DOIs
Publication statusPublished - 2019
Event26th IEEE International Conference on Image Processing (ICIP) - Taipei, TAIWAN
Duration: 22 Sept 201925 Sept 2019

Conference

Conference26th IEEE International Conference on Image Processing (ICIP)
Country/TerritoryTAIWAN
CityTaipei
Period22/09/1925/09/19

Keywords

  • 3D-CNN
  • deep learning
  • LSTM
  • video quality assessment
  • PREDICTION

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