Real-time video quality monitoring
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RESEARCH
Open Access
Real-time video quality monitoring Tao Liu*, Niranjan Narvekar, Beibei Wang, Ran Ding, Dekun Zou, Glenn Cash, Sitaram Bhagavathy and Jeffrey Bloom
Abstract The ITU-T Recommendation G.1070 is a standardized opinion model for video telephony applications that uses video bitrate, frame rate, and packet-loss rate to measure the video quality. However, this model was original designed as an offline quality planning tool. It cannot be directly used for quality monitoring since the above three input parameters are not readily available within a network or at the decoder. And there is a great room for the performance improvement of this quality metric. In this article, we present a real-time video quality monitoring solution based on this Recommendation. We first propose a scheme to efficiently estimate the three parameters from video bitstreams, so that it can be used as a real-time video quality monitoring tool. Furthermore, an enhanced algorithm based on the G.1070 model that provides more accurate quality prediction is proposed. Finally, to use this metric in real-world applications, we present an example emerging application of real-time quality measurement to the management of transmitted videos, especially those delivered to mobile devices. Keywords: G.1070, video quality monitoring, bitrate estimation, frame rate estimation, packet-loss rate estimation
1 Introduction With the increase in the volume of video content processed and transmitted over communication networks, the variety of video applications and services has also been steadily growing. These include more mature services such as broadcast television, pay-per-view, and video on demand, as well as newer models for delivery of video over the internet to computers and over telephone systems to mobile devices such as smart phones. Niche markets for very high quality video for telepresence are emerging as are more moderate quality channels for video conferencing. Hence, an accurate, and in many cases real-time, assessment of the video quality is becoming increasingly important. The most commonly used methods for assessing visual quality are designed to predict subjective quality ratings on a set of training data [1]. Many of these methods rely on access to an original undistorted version of the video under test. There has been significant progress in the development of such tools. However, they are not directly useful for many of the new video applications and services in which the quality of a target video must be assessed without access to a reference. For these cases, no-reference (NR) models are more * Correspondence: [email protected] Dialogic Inc., 12 Christopher Way, Suite 104, Eatontown, NJ 07724, USA
appropriate. Development of NR visual quality metrics is a challenging research problem partially due to the fact that the artifacts introduced by different transmission components can have dramatically different visual impacts and the perceived quality can largely depend on the underlying video content. Therefore, a “divide-an
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