Publication detail

Estimating PSNR in High Definition H.264/AVC Video Sequences Using Artificial Neural Networks

SLANINA, M. ŘÍČNÝ, V.

Original Title

Estimating PSNR in High Definition H.264/AVC Video Sequences Using Artificial Neural Networks

English Title

Estimating PSNR in High Definition H.264/AVC Video Sequences Using Artificial Neural Networks

Type

journal article - other

Language

en

Original Abstract

The paper presents a video quality metric designed for the H.264/AVC codec. The metric operates directly on the encoded H.264/AVC bit stream, parses the encoding parameters and processes them using an artificial neural network. The network is designed to estimate peak signal-to-noise ratios of the video sequence frames, thus enabling computation of full reference objective quality metric values without having the undistorted video material prior to encoding for comparison. We present the metric framework and test its performance for LDTV (low definition television) as well as HDTV (high definition television) video material.

English abstract

The paper presents a video quality metric designed for the H.264/AVC codec. The metric operates directly on the encoded H.264/AVC bit stream, parses the encoding parameters and processes them using an artificial neural network. The network is designed to estimate peak signal-to-noise ratios of the video sequence frames, thus enabling computation of full reference objective quality metric values without having the undistorted video material prior to encoding for comparison. We present the metric framework and test its performance for LDTV (low definition television) as well as HDTV (high definition television) video material.

Keywords

H.264/AVC, video quality, objective quality metric, HDTV, artificial neural network.

RIV year

2008

Released

30.09.2008

Pages from

103

Pages to

108

Pages count

6

BibTex


@article{BUT47911,
  author="Martin {Slanina} and Václav {Říčný}",
  title="Estimating PSNR in High Definition H.264/AVC Video Sequences Using Artificial Neural Networks",
  annote="The paper presents a video quality metric designed for the H.264/AVC codec. The metric operates directly on the encoded H.264/AVC bit stream, parses the encoding parameters and processes them using an artificial neural network. The network is designed to estimate peak signal-to-noise ratios of the video sequence frames, thus enabling computation of full reference objective quality metric values without having the undistorted video material prior to encoding for comparison. We present the metric framework and test its performance for LDTV (low definition television) as well as HDTV (high definition television) video material.",
  chapter="47911",
  journal="Radioengineering",
  number="3",
  volume="17",
  year="2008",
  month="september",
  pages="103--108",
  type="journal article - other"
}