International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 63 - Issue 16 |
Published: February 2013 |
Authors: Marcio L. Graciano, Alexandre R. S. Romariz, Jose Camargo Da Costa |
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Marcio L. Graciano, Alexandre R. S. Romariz, Jose Camargo Da Costa . Methodology for Objective Evaluation of Video Broadcasting Quality using a Video Camera at the User’s Home. International Journal of Computer Applications. 63, 16 (February 2013), 37-42. DOI=10.5120/10554-5750
@article{ 10.5120/10554-5750, author = { Marcio L. Graciano,Alexandre R. S. Romariz,Jose Camargo Da Costa }, title = { Methodology for Objective Evaluation of Video Broadcasting Quality using a Video Camera at the User’s Home }, journal = { International Journal of Computer Applications }, year = { 2013 }, volume = { 63 }, number = { 16 }, pages = { 37-42 }, doi = { 10.5120/10554-5750 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2013 %A Marcio L. Graciano %A Alexandre R. S. Romariz %A Jose Camargo Da Costa %T Methodology for Objective Evaluation of Video Broadcasting Quality using a Video Camera at the User’s Home%T %J International Journal of Computer Applications %V 63 %N 16 %P 37-42 %R 10.5120/10554-5750 %I Foundation of Computer Science (FCS), NY, USA
In this work, a methodology for objective evaluation of the quality of video programs, without reference, recording these programs in the users' residence using a video camera is presented. Themethodology is based on the use of a digital watermark embedded in the original program. The watermark is invisible to the user, but capturable by the video camera. The recorded video is handled by specific software that evaluates the watermark degradation. The measure of degradation of this watermark is used to estimate the quality of the video broadcasting system. A case study is presented to validate the methodology. The results of video quality metrics using this methodology were compared to a standardized full reference metrics and the linear correlation between these metrics was superior to 93%, which indicates a high convergence. The result of video quality metrics were also compared to a pixel based difference metrics, PSNR (Peak Signal to Noise Ratio) and the linear correlation was superior to 99%.