[Paper] Blind PSNR Estimation of Compressed Video Sequences Supported by Machine Learning

• Kumekawa Takahiro

  Graduate School of Fundamental Science and Engineering, Waseda University

• Wakabayashi Masahiro

  Graduate School of Fundamental Science and Engineering, Waseda University

• Katto Jiro

  Graduate School of Fundamental Science and Engineering, Waseda University

• Wada Naofumi

  Samsung R&D Institute Japan (SRJ) - Sapporo

抄録

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.

収録刊行物

• 映像情報メディア学会英語論文誌

  映像情報メディア学会英語論文誌 2 (4), 353-361, 2014

  一般社団法人 映像情報メディア学会

キーワード

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

詳細情報

• CRID: 1390282680399990016
• NII論文ID: 130004698815
• DOI: 10.3169/mta.2.353
• ISSN: 21867364
• Web Site: https://www.jstage.jst.go.jp/article/mta/2/4/2_353/_pdf
• 本文言語コード: en
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