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A Versatile Model for Packet Loss Visibility and its Application to Packet Prioritization

机译:包丢失可见性的通用模型及其在包优先级划分中的应用

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In this paper, we propose a generalized linear model for video packet loss visibility that is applicable to different group-of-picture structures. We develop the model using three subjective experiment data sets that span various encoding standards (H.264 and MPEG-2), group-of-picture structures, and decoder error concealment choices. We consider factors not only within a packet, but also in its vicinity, to account for possible temporal and spatial masking effects. We discover that the factors of scene cuts, camera motion, and reference distance are highly significant to the packet loss visibility. We apply our visibility model to packet prioritization for a video stream; when the network gets congested at an intermediate router, the router is able to decide which packets to drop such that visual quality of the video is minimally impacted. To show the effectiveness of our visibility model and its corresponding packet prioritization method, experiments are done to compare our perceptual-quality-based packet prioritization approach with existing Drop-Tail and Hint-Track-inspired cumulative-MSE-based prioritization methods. The result shows that our prioritization method produces videos of higher perceptual quality for different network conditions and group-of-picture structures. Our model was developed using data from high encoding-rate videos, and designed for high-quality video transported over a mostly reliable network; however, the experiments show the model is applicable to different encoding rates.
机译:在本文中,我们提出了一种适用于视频丢包可见性的通用线性模型,适用于不同的图片组结构。我们使用三个主观实验数据集来开发该模型,这些数据集涵盖了各种编码标准(H.264和MPEG-2),图片组结构以及解码器错误隐藏选项。我们不仅考虑数据包内的因素,还考虑其附近的因素,以考虑可能的时间和空间掩蔽效应。我们发现场景切换,摄像机运动和参考距离等因素对于丢包可见性具有非常重要的意义。我们将可见性模型应用于视频流的数据包优先级划分;当网络在中间路由器处拥塞时,路由器能够决定丢弃哪些数据包,从而将视频的视觉质量受到的影响最小。为了显示我们的可见性模型及其相应的数据包优先级排序方法的有效性,我们进行了实验,以将我们基于感知质量的数据包优先级排序方法与现有的基于Drop-Tail和启发式启发式累积MSE的优先级排序方法进行比较。结果表明,针对不同的网络条件和图片组结构,我们的优先级排序方法可产生更高感知质量的视频。我们的模型是使用来自高编码率视频的数据开发的,旨在通过最可靠的网络传输高质量的视频;然而,实验表明该模型适用于不同的编码率。

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