Modeling and subjective assessment of cell discard in ATM video

Hughes, C.J.; Ghanbari, M.; Pearson, D.E.; Seferidis, V.; Xiong, Jiu

doi:10.1109/83.217224

Cited by 40 publications

(22 citation statements)

References 11 publications

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“…As in previous studies of video quality, the main benefit of bandwidth appears to be good quality when the content includes motion or the concatenation of motion and detail (see Hughes et al, 1993;ITU, 1998;Morton, 1994;VQEG, 2003). For the Polycom system operating at 128, 384, and 768 kbits/s, waving to the camera had very clear differential effects.…”

Section: Bandwidth and Motionmentioning

confidence: 90%

“…The parameters most often discussed as characterizing packet network performance are latency, packet loss, packet delay variation or "jitter", and bandwidth (e.g., Kostas et al, 1998). Packet network emulators can be inserted into functioning networks, so that realtime network performance can be manipulated (for an early version, see Hughes et al, 1993). In previous experiments on VoIP (Cermak, 2001) we manipulated latency, packet loss, and jitter.…”

Section: Network Quality Parameters Manipulated Whenmentioning

confidence: 99%

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Multimedia Quality as a Function of Bandwidth, Packet Loss, and Latency

Cermak

2005

Int J Speech Technol

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Two experiments examined the judged quality of videoconferencing as a function of three measures of IP network performance: bandwidth, latency, and packet loss. The experiments were realized in a laboratory using a network emulator and a commercial videoconferencing system. Experiment 1 used a fractional factorial design and all three parameters:• Bandwidth: 128 kbits/s, 384 kbits/s, 768 kbits/s • Latency: 0, 150, 300 ms one-way • Bursty packet loss: 0, 2%, 4% (using the "Gilbert-Eliot" method).Experiment 2 was designed (a) to use random packet loss rather than bursty packet loss, and (b) so that a statistical interaction between bandwidth and packet loss could be detected, if present. Bandwidth levels were the same as in Experiment 1, but packet loss was set to 0, 1 and 2%. The experimental design was full factorial.In both experiments, pairs of non-expert judges held five-minute videoconferences for each combination of parameters, then rated the quality of system performance immediately after each videoconference. In both experiments statistical analysis showed that packet loss was the most important network performance parameter in predicting subjective quality of videoconferencing. These results agree with results on VoIP quality. Bandwidth and latency also affected the judges' ratings, but to a smaller extent. In Experiment 2, an interaction between packet loss and bandwidth was detected: At lower bandwidths and greater packet loss, the subjective ratings were not as low as would be expected, contrary to the idea that quality would degrade catastrophically when both bandwidth and packet loss were simultaneously unfavorable.

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Section: Bandwidth and Motionmentioning

confidence: 90%

Section: Network Quality Parameters Manipulated Whenmentioning

confidence: 99%

Multimedia Quality as a Function of Bandwidth, Packet Loss, and Latency

Cermak

2005

Int J Speech Technol

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“…Using different objective and subjective performance metrics importance of the autocorrelation in the packet loss process have been reported in other studies as well (see e.g. [100,26,48]). …”

Section: Network-related Metricsmentioning

confidence: 99%

Service quality in P2P streaming systems

Moltchanov

2011

Computer Science Review

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“…Prior research to understand the relationship between packet losses and visual quality degradation includes [1,2,3,4], where average packet loss rate (PLR) was used to model average video quality. In [3], a random neural network model was used to assess quality given different bandwidth, framerate, packet loss rate, and I-block refresh rate.…”

Section: Introductionmentioning

confidence: 99%

Perceptual quality based packet dropping for generalized video GOP structures

Lin¹,

Zhi²,

Kanumuri³

et al. 2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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Our work builds a general visibility model of video packets which is applicable to various types of GOP (Group of Pictures). The data used for analysis and building the model come from three subjective experiment sets with different encoding and decoding parameters on H.264 and MPEG-2 videos. We consider factors not only within a packet but also across its vicinity to account for possible temporal and spatial masking effects. This model can be useful for an intermediate router in a congested network to drop less visible packets to maintain overall video quality. Experiments are done to compare our perceptual-quality-based packet dropping approach with existing Drop-Tail and Hint-Track-inspired cumulative-MSE-based dropping methods. The result shows that our dropping method produces videos of higher perceptual quality for different network conditions and GOP structures.

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Modeling and subjective assessment of cell discard in ATM video

Cited by 40 publications

References 11 publications

Multimedia Quality as a Function of Bandwidth, Packet Loss, and Latency

Multimedia Quality as a Function of Bandwidth, Packet Loss, and Latency

Service quality in P2P streaming systems

Perceptual quality based packet dropping for generalized video GOP structures

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