MPEG-2 basic training, part 3
Nov 16, 2011 2:45 PM, By Ned Soseman
Broadcast engineering requires a unique set of skills and talents. Some audio engineers claim the ability to hear the difference between tiny nuisances such as different kinds of speaker wire. They are known as those with golden ears. Their video engineering counterparts can spot and obsess over a single deviate pixel during a Super Bowl touchdown pass or a “Leave it to Beaver” rerun in real time. They are known as eagle eyes or video experts.
Not all audio and video engineers are blessed with super-senses. Nor do we all have the talent to focus our brain’s undivided processing power to discover and discern vague, cryptic and sometimes immeasurable sound or image anomalies with our bare eyes or ears on the fly, me included. Sometimes, the message can overpower the media. Fortunately for us and thanks to the Internet and digital video, more objective quality and measurement standards and tools have developed.
One of those standards is Perceptual Evaluation of Video Quality (PEVQ). It is an end-to-end (E2E) measurement algorithm standard that grades picture quality of a video presentation by a five-point mean opinion score (MOS), one being bad and five being excellent.
PEVQ can be used to analyze visible artifacts caused by digital video encoding/decoding or transcoding processes, RF- or IP-based transmission systems and viewer devices like set-top boxes. PEVQ is suited for next-generation networking and mobile services and include SD and HD IPTV, streaming video, mobile TV, video conferencing and video messaging.
The development for PEVQ began with still images. Evaluation models were later expanded to include motion video. PEVQ can be used to assess degradations of a decoded video stream from the network, such as that received by a TV set-top box, in comparison to the original reference picture as broadcast from the studio. This evaluation model is referred to as end-to-end (E2E) quality testing.
E2E exactly replicates how so-called average viewers would evaluate the video quality based on subjective comparison, so it addresses Quality-of-Experience (QoE) testing. PEVQ is based on modeling human visual behaviors. It is a full-reference algorithm that analyzes the picture pixel-by-pixel after a temporal alignment of corresponding frames of reference and test signal.
Besides an overall quality Mean Opinion Score figure of merit, abnormalities in the video signal are quantified by several key performance indicators (KPI), such as peak signal-to-noise ratios (PSNR), distortion indicators and lip-sync delay.
PVEQ references
Depending on the data made available to the algorithm, video quality test algorithms can be divided into three categories based on available reference data.
A Full Reference (FR) algorithm has access to and makes use of the original reference sequence for a comparative difference analysis. It compares each pixel of the reference sequence to each corresponding pixel of the received sequence. FR measurements deliver the highest accuracy and repeatability but are processing intensive.
A Reduced Reference (RR) algorithm uses a reduced bandwidth side channel between the sender and the receiver, which is not capable of transmitting the full reference signal. Instead, parameters are extracted at the sending side, which help predict the quality at the receiving end. RR measurements are less accurate than FR and represent a working compromise if bandwidth for the reference signal is limited.
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