About: Estimating H.264/AVC Video PSNR Without Reference Using the Artificial Neural Network Approach     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
Description
  • This paper presents a method capable of estimating peak signal-to-noise ratios (PSNR) of digital video sequences compressed using the H.264/AVC algorithm. The idea is in replacing a full reference metric - the PSNR (for whose evaluation we need the original as well as the processed video data) - with a no reference metric, operating on the encoded bit stream only. As we are working just with the encoded bit stream, we can spare a significant amount of computations needed to decode the video pixel values. In this paper, we describe the network inputs and network configurations, suitable to estimate PSNR in intra and inter predicted pictures. Finally, we make a simple evaluation of the proposed algorithm, having the correlation coefficient of the real and estimated PSNRs as the measure of optimality.
  • This paper presents a method capable of estimating peak signal-to-noise ratios (PSNR) of digital video sequences compressed using the H.264/AVC algorithm. The idea is in replacing a full reference metric - the PSNR (for whose evaluation we need the original as well as the processed video data) - with a no reference metric, operating on the encoded bit stream only. As we are working just with the encoded bit stream, we can spare a significant amount of computations needed to decode the video pixel values. In this paper, we describe the network inputs and network configurations, suitable to estimate PSNR in intra and inter predicted pictures. Finally, we make a simple evaluation of the proposed algorithm, having the correlation coefficient of the real and estimated PSNRs as the measure of optimality. (en)
Title
  • Estimating H.264/AVC Video PSNR Without Reference Using the Artificial Neural Network Approach
  • Estimating H.264/AVC Video PSNR Without Reference Using the Artificial Neural Network Approach (en)
skos:prefLabel
  • Estimating H.264/AVC Video PSNR Without Reference Using the Artificial Neural Network Approach
  • Estimating H.264/AVC Video PSNR Without Reference Using the Artificial Neural Network Approach (en)
skos:notation
  • RIV/00216305:26220/08:PU75052!RIV10-MSM-26220___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GD102/08/H027), Z(MSM0021630513)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 366523
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26220/08:PU75052
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • H.264/AVC, video quality, no reference assessment, PSNR, artificial neural network. (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [CF4001EBB7FE]
http://linked.open...v/mistoKonaniAkce
  • Porto
http://linked.open...i/riv/mistoVydani
  • Porto
http://linked.open...i/riv/nazevZdroje
  • Sigmap 2008 International Conference on Signal Processing and Multimedia Applications Proceedings
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Slanina, Martin
  • Říčný, Václav
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • INSTICC
https://schema.org/isbn
  • 978-989-8111-60-9
http://localhost/t...ganizacniJednotka
  • 26220
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 48 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software