Modeling And Research On Distortion For Video Transmission Over IP Data Optical Networks

The development of video communication is along with and partly based on the advancement of its underlying network technologies. Generally, video services could be carried through various networks, including the early PSTN, ISDN, and later ATM, as well as the IP networks which are widely deployed currently. Today, a new concept of underlying networks, IP data optical network, has been proposed, to present a relatively broad category of networks, which are based on optical techniques and enable the IP services. It could be denoted as IP/.../optics for clarity. In such a case, researchers have turned into the new subject of video over IP data optical networks.The QoS (Quality of Service) in video transmission over IP data optical networks is most important for both service provider and end user. The key factor degrading the video quality in video communication systems is the packet loss. It is thus important to deeply understand the relation between the end-to-end packet losses and the user-perceptive video quality. That is why people in both institutes and industry are interested in this subject for a long time. For video over IP data optical networks, we also focus on the same problem, i.e. the impact of IP data optical network end-to-end packet losses in user-perceptive video quality. To understand the effect of packet loss on video quality, it is desired to model the end-to-end distortion caused by packet loss in decoded video. Based on the distortion model, one can estimate the packet-loss-induced distortion at the encoder for video transmission over lossy channels. It is thus critical for most joint source-channel rate-distortion optimized schemes and channel error control techniques such as inter/intra mode switching and forward error correction.When modeling the packet-loss-induced distortion for IP data optical networks, the network loss characteristics should be prior known. A simple assumption is to regard the packet losses as an independent and identically-distributed random process, characterized by a Bernoulli loss model. However, for IP data optical networks, the end-to-end packet losses often exhibit time dependences. It will lead to burst packet loss, a characteristic cannot be found in a Bernoulli loss model, but can be described by a Markov loss model. Many distortion models for video over lossy networks have been proposed. However, all existing models are based on the Bernoulli loss assumption. Modeling the video decoded distortion for Markov-model losses is more complicated than that for Bernoulli losses. This paper focuses on the distortion modeling problems and aims to made one step effort on understanding the impact of IP data optical network losses on user-perceptive video quality. Part of the results proposed in this paper will be published in the IEEE Transaction on Circuits and Systems for Video Technology.The main contributions of this paper are summarized as follows.(1) To formulate the problem in a mathematic way, we first propose a framework of video decoded distortion modeling, where the packet losses in IP data optical networks are modeled as an (m+1)-state Markov chain.(2) To establish the distortion model for video transmission over IP data optical nerworks modeled by Markov loss process, the decoded distortion for arbitrary packet loss pattern should be prior modeled. For video communications using motion composition techniques, we propose a distortion model, denoted as the Distortion Infection, to estimate the decoded distortion caused by arbitrary packet loss pattern.(3) Based on the detailed analysis of both the error propagation and the loss burstiness, the Distortion Trellis model is established, enabling us to estimate the expected MSE distortion for two-state Markov losses, or Gilbert losses, at both frame level and sequence level at the encoder. The model is designed to be applicable to most block-based motion compensated encoders. The model also allows for any temporal error concealment at the decoder.(4) A siding window algorithm is developed to calculate the MSE estimation with low complexity. Using the sliding window algorithm, in most cases more than 90% computation burden can be saved compared with the original Distortion Trellis model without degrading of accuracy.(5) Finally, we extend the proposed model to a more general form, enabling us to calculate the distortion caused by (m+1)-state Markov losses, and thus to estimate the decoded distortion for video transmission over IP data optical networks. Based on the proposed model, we analysis in detail the impact of IP data optical networks losses on video quality and established some new findings that have never been proposed before. The last section mentions some probable practical applications of the proposed models.