Blind PSNR Estimation of Compressed Video Sequences Supported by Machine Learning

Takahiro Kumekawa, Masahiro Wakabayashi, Jiro Katto, Naofumi Wada

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


The peak signal-to-noise ratio (PSNR) used as an index of image quality usually requires original images, but this is difficult for consumer generated content such as videos on YouTube. Therefore, we developed two blind PSNR estimation methods without bit-stream analysis in which multiple support vector machines are prepared to learn differently encoded images in PSNR; using an entire frame and dividing the frame into two areas. We confirmed that higher estimation accuracy is possible for the latter method against that using the entire frame.

Original languageEnglish
Pages (from-to)353-361
Number of pages9
JournalITE Transactions on Media Technology and Applications
Issue number4
Publication statusPublished - 2014


  • AC Power
  • Blind PSNR Estimation
  • SVM
  • Saliency Map
  • Video Quality Assessment

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Graphics and Computer-Aided Design


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