Blind video quality assessment based on Spatio-Temporal Feature Resolver

Xiaodong Bi, Xiaohai He*, Shuhua Xiong, Zeming Zhao, Honggang Chen, Raymond Edward Sheriff

*Corresponding author for this work

Research output: Contribution to journalArticle (journal)peer-review

Abstract

As a method to measure video quality, blind video quality assessment (BVQA) plays an important role in video related applications. Feature extraction, as a vital component of BVQA, significantly impacts the performance and speed of the method. In the
process of designing BVQA method, we make the feature extraction process independent of the whole quality evaluation method.
First, a Spatio-Temporal Feature Resolver (STFR) is obtained by training. Then, the STFR is employed to directly extract the
spatio-temporal features of the video sequences. Finally, the extracted features are mapped to quality scores using Support Vector
Regression (SVR). STFR only needs to be trained in an undistorted video sequence, and can be directly applied to video sequences
of various scenes, which has a universality. To evaluate the effectiveness of the proposed method, the experimental results on
four published video quality databases show that the proposed BVQA method not only achieves more accurately than the existing
BVQA methods, but also exhibits significant competitiveness in computational speed.
Original languageEnglish
Article number127249
Pages (from-to)1-12
JournalNeurocomputing
Volume574
Early online date11 Jan 2024
DOIs
Publication statusPublished - 14 Mar 2024

Keywords

  • Blind Video Quality Assessment
  • Spatio-Temporal Feature Resolver
  • Support Vector Regression
  • Universality
  • Blind video quality assessment

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