Blind PSNR Estimation of Compressed Video Sequences Supported by Machine Learning

Takahiro Kumekawa; Masahiro Wakabayashi; Jiro Katto; Naofumi Wada

doi:10.3169/mta.2.353

Blind PSNR Estimation of Compressed Video Sequences Supported by Machine Learning

Takahiro Kumekawa, Masahiro Wakabayashi, Jiro Katto, Naofumi Wada

School of Fundamental Science and Engineering

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

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 language	English
Pages (from-to)	353-361
Number of pages	9
Journal	ITE Transactions on Media Technology and Applications
Volume	2
Issue number	4
DOIs	https://doi.org/10.3169/mta.2.353
Publication status	Published - 2014

Keywords

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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title = "Blind PSNR Estimation of Compressed Video Sequences Supported by Machine Learning",

abstract = "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.",

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author = "Takahiro Kumekawa and Masahiro Wakabayashi and Jiro Katto and Naofumi Wada",

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AU - Wakabayashi, Masahiro

AU - Katto, Jiro

AU - Wada, Naofumi

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - AC Power

KW - Blind PSNR Estimation

KW - SVM

KW - Saliency Map

KW - Video Quality Assessment

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ER -

Blind PSNR Estimation of Compressed Video Sequences Supported by Machine Learning

Abstract

Keywords

ASJC Scopus subject areas

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