No-reference video quality assessment via pretrained CNN and LSTM networks

Domonkos Varga, Tamás Szirányi

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

A general-purpose no-reference video quality assessment algorithm based on a long short-term memory (LSTM) network and a pretrained convolutional neural network (CNN) is introduced. Considering video sequences as a time series of deep features extracted with the help of a CNN, an LSTM network is trained to predict subjective quality scores. In contrast to previous methods, the resulting algorithm was trained on the recently published Konstanz Natural Video Quality Database (KoNViD-1k), which is the only publicly available database that contains sequences with authentic distortions. The results of experiments on KoNViD-1k demonstrate that the proposed method outperforms other state-of-the-art algorithms. Furthermore, these results are also confirmed using tests on the LIVE Video Quality Assessment Database, which consists of artificially distorted videos.

Original languageEnglish
Pages (from-to)1569-1576
Number of pages8
JournalSignal, Image and Video Processing
Volume13
Issue number8
DOIs
Publication statusPublished - Nov 1 2019

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Keywords

  • Convolutional neural network
  • Long short-term memory
  • No-reference video quality assessment

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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