Evaluation of packet loss impairment on streaming video

Rui, Huaxia; Li, Chongrong; Qiu, Shengke

doi:10.1631/jzus.2006.as0131

Cited by 15 publications

(13 citation statements)

References 5 publications

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“…If we set the weights for EC effectiveness to one in Equation 4 and ignore the second item of propagated artifacts by setting it to zero in Equation 3, then the MLoVA regresses to PLR, where the data losses are regarded as equally important to perceptual quality. As described in Section 3, the EC strategy employed at decoder can hide the visible artifacts caused by packet loss to a degree that depends on the spatiotemporal complexity of the lost content.…”

Section: Resultsmentioning

confidence: 99%

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A packet-layer video quality assessment model with spatiotemporal complexity estimation

Liao¹,

Chen²

2011

J Image Video Proc.

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A packet-layer video quality assessment (VQA) model is a lightweight model that predicts the video quality impacted by network conditions and coding configuration for application scenarios such as video system planning and in-service video quality monitoring. It is under standardization in ITU-T Study Group (SG) 12. In this article, we first differentiate the requirements for VQA model from the two application scenarios, and state the argument that the dataset for evaluating the quality monitoring model should be more challenging than that for system planning model. Correspondingly, different criteria and approaches are used for constructing the test datasets, for system planning (dataset-1) and for video quality monitoring (dataset-2), respectively. Further, we propose a novel video quality monitoring model by estimating the spatiotemporal complexity of video content. The model takes into account the interactions among content features, the error concealment effectiveness, and error propagation effects. Experiment results demonstrate that the proposed model achieves robust performance improvement compared with the existing peer VQA metrics on both dataset-1 and dataset-2. It is noted that on the more challenging dataset-2 for video quality monitoring, we obtain a large increase in Pearson correlation from 0.75 to 0.92 and a decrease in the modified RMSE from 0.41 to 0.19.

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Section: Resultsmentioning

confidence: 99%

“…Thus, it can easily obtain content-dependent features that influence video quality, such as texture-masking effects and motion-masking effects. However, a media-layer model usually needs special solutions (e.g., [4]) for locating the impaired parts in the distorted video because of the lack of information on packet loss.…”

Section: Introductionmentioning

confidence: 99%

A packet-layer video quality assessment model with spatiotemporal complexity estimation

Liao¹,

Chen²

2011

J Image Video Proc.

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show abstract

“…It is generally observed that, as the bit rate decreases, fewer macroblocks are coded (more macroblocks are skipped). Therefore, the percentage of macroblocks that are coded can be used to represent the bit rate in Equation 15. Thus, we can compute the normalized bit rate as follows:…”

Section: Bitrate Normalization Using Frame Complexitymentioning

confidence: 99%

“…Among various forms of artifacts, the most commonly studied are spatial coding artifacts, e.g. blurriness [3][4][5] and blockiness [6][7][8][9], temporally induced artifacts [10][11][12], and packet-loss-related artifacts [13][14][15][16][17][18]. In addition to the models developed for specific distortions, there are investigations into generic quality measurement which can predict the quality of video affected by multiple distortions [19].…”

Section: Introductionmentioning

confidence: 99%

Real-time video quality monitoring

Liu

Narvekar

Wang

et al. 2011

EURASIP J. Adv. Signal Process.

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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.

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“…Spatially, Babu et al counted the number of pixels along the edges where gradient differences are high [17] . Slice Boundary Mismatch (SBM), proposed by Rui et al, considered the strength of continuities, together with its length, as an indication of packet loss [18] . Reibman and Poole estimated the quality of packet loss impaired video from the perspective of the visibility of packet loss.…”

Section: Packet Lossmentioning

confidence: 99%

Real-time video quality monitoring for mobile devices

Liu

Cash

Chen

et al. 2010

2010 44th Annual Conference on Information Sciences and Systems (CISS)

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Due to the explosion of various video applications and services it is becoming increasingly important to accurately measure video quality in real-time. Since reference information is absent in many practical situations, video quality assessment with No-Reference (NR) measures is attracting increasing attention from both industry and academia. However, there are very few commercial quality assessment systems for mobile devices available in today's market.After describing a set of applications, we briefly survey the state-of-the-art in NR video quality assessment. Finally, we present a real-time quality monitoring system for video transmitted to mobile devices and describe a proof-ofconcept implementation. In this system, the quality of video at different nodes and terminals in a transmission channel can be monitored and tracked through a remote software interface. These video quality measurements can also provide valuable information for network diagnosis and quality-scalable service planning.

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Evaluation of packet loss impairment on streaming video

Cited by 15 publications

References 5 publications

A packet-layer video quality assessment model with spatiotemporal complexity estimation

A packet-layer video quality assessment model with spatiotemporal complexity estimation

Real-time video quality monitoring

Real-time video quality monitoring for mobile devices

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