To bring this trade-off to its optimum, it is necessary to understand better how people perceive video quality.
No matter how good the phone's video quality is, it's still prone to shakiness as its director moves around.
Better yet, it never once hiccupped (although video quality will depend on how compressed the file is).
The PSNR Y diff metric, illustrated in (6), indicates how the luma (Y) samples impact the video quality after embedding the physiological samples.
It is not clear how this business model works if, when the video quality over the Internet gets up to broadcast qualities, it is based on MPEG standards.
However, as the original source video or information on the video quality is not always available, how to obtain the best video from these multiple compressed copies becomes an interesting problem.
In this article, we showcase how this technology can be used in a real-world video quality monitoring application.
Previous studies focus on how to estimate network quality of services (QoS) and achieve good video quality with limited network resources.
Video quality is pretty good, although that'll depend on how the video you're watching was encoded too.
That is, it can be seen very easy how many users are experiencing a video with no errors, with a few errors, with some errors, or experiencing a video quality which is not acceptable.
