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Qos Assessment of Video Over IP

transmission quality network level

Definition: At the network level, the QoS offered to a video transmission can be evaluated by monitoring network performance parameters, such as delay, jitter, and packet loss rate.

The capacity of measuring the Quality of Service (QoS) of a video communication becomes increasingly important because of the growing interest in video delivery over unreliable channels, such as wireless networks and the Internet. A key point for the success of new multimedia applications and video applications in particular, is the network QoS provided to video and audio streams. At the network level, the QoS offered to a video transmission can be evaluated by monitoring network performance parameters, such as delay, jitter, and packet loss rate. Nevertheless, it is challenging to establish how these network-level parameters and the consequent delivered QoS to video streams effectively map onto application QoS, i.e. the delivered QoS as perceived by a (human) end user.

At the application level, the quality of a video transmission may be evaluated by subjective and objective metrics . An example of a subjective metric is the Mean Opinion Score (MOS). MOS provides a measure of the quality perceived by a user of a multimedia transmission. The scheme uses subjective controlled tests that are averaged after applied to a number of individuals to obtain a quantitative indicator of the system performance in transmitting a multimedia stream. In the case of video transmission assessment, these individuals rate the perceived quality of test videos after they are sent over simulated or experimental networks. Each video is given a rating as follows: (1) bad; (2) poor; (3) fair; (4) good; (5) excellent. The MOS rate is the mean of all individual scores and can then range from 1 (worst) and 5 (best). This is useful to establish reference scores on video quality transmission, as it is indeed perceived by end users. However, the subjective assessment provided by MOS is expensive and time-consuming, thereby preventing it to be conducted in real-time. As a consequence, it is also hard to embed the MOS metric into a practical video processing system because it cannot be implemented automatically.

Objective techniques offer an alternative to subjective techniques by avoiding human intervention to rate the quality of the video transmission and allowing a means to automatically estimate the quality of a video transmission. Indeed, objective metrics for assessing video performance are useful for the practical specification of performance requirements, comparison of service offerings, automatic network QoS monitoring, and better utilization of limited network resources, such as transmission bandwidth. An example of objective metric for video transmission assessment is the Peak Signal-to-Noise Ratio (PSNR). PSNR measures the error between a reconstructed image and the original one. Prior to transmission, one may then compute a reference PSNR value sequence on the reconstruction of the encoded video as compared to the original raw video. After transmission, the PSNR is computed at the receiver for the reconstructed video of the possibly corrupted video sequence received. The individual PSNR values at the source or receiver do not mean much, but the difference between the quality of the encoded video at the source and the received one can be used as an objective QoS metric to assess the transmission impact on video quality at the application level.

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