Temporal hysteresis model of time varying subjective video quality

Seshadrinathan, Kalpana; Bovik, Alan C.

doi:10.1109/icassp.2011.5946613

Cited by 125 publications

(118 citation statements)

References 8 publications

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“…The analysis below is simplistic, but much work remains on developing good behavioral models of temporal quality judgements of dynamically changing video distortions. Our first attempt at understanding this new problem is detailed in [32].…”

Section: F Evaluation Of Temporal Quality Scoresmentioning

confidence: 99%

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Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies

Moorthy

Choi

Bovik

et al. 2012

IEEE J. Sel. Top. Signal Process.

268

168

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Abstract-We introduce a new video quality database that models video distortions in heavily-trafficked wireless networks and that contains measurements of human subjective impressions of the quality of videos. The new LIVE Mobile Video Quality Assessment (VQA) database consists of 200 distorted videos created from 10 RAW HD reference videos, obtained using a RED ONE digital cinematographic camera. While the LIVE Mobile VQA database includes distortions that have been previously studied such as compression and wireless packet-loss, it also incorporates dynamically varying distortions that change as a function of time, such as frame-freezes and temporally varying compression rates. In this article, we describe the construction of the database and detail the human study that was performed on mobile phones and tablets in order to gauge the human perception of quality on mobile devices. The subjective study portion of the database includes both the differential mean opinion scores (DMOS) computed from the ratings that the subjects provided at the end of each video clip, as well as the continuous temporal scores that the subjects recorded as they viewed the video. The study involved over 50 subjects and resulted in 5,300 summary subjective scores and time-sampled subjective traces of quality. In the behavioral portion of the article we analyze human opinion using statistical techniques, and also study a variety of models of temporal pooling that may reflect strategies that the subjects used to make the final decision on video quality. Further, we compare the quality ratings obtained from the tablet and the mobile phone studies in order to study the impact of these different display modes on quality. We also evaluate several objective image and video quality assessment (IQA/VQA) algorithms with regards to their efficacy in predicting visual quality. A detailed correlation analysis and statistical hypothesis testing is carried out. Our general conclusion is that existing VQA algorithms are not well-equipped to handle distortions that vary over time. The LIVE Mobile VQA database, along with the subject DMOS and the continuous temporal scores is being made available to researchers in the field of VQA at no cost in order to further research in the area of video quality assessment. Index Terms-Mobile video quality, objective algorithm evaluations, subjective quality, video quality assessment, video quality database.

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Section: F Evaluation Of Temporal Quality Scoresmentioning

confidence: 99%

“…Thus a total of 450 temporal scores were collected for each 15 second video. The temporal scores so obtained were then processed as in [32], in order to produce a temporal MOS (z-score) for each video. Specifically, let be the score assigned to the video by subject in session , where each video is of length .…”

Section: F Evaluation Of Temporal Quality Scoresmentioning

confidence: 99%

Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies

Moorthy

Choi

Bovik

et al. 2012

IEEE J. Sel. Top. Signal Process.

268

168

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“…Throughput variations in the network lead to higher video quality variations which are regarded as leading to lower end user QoE [5][6] [7]. This was reconfirmed in [8] where it was shown that the average quality of the complete video session is often not the correct metric of user's QoE due to hysteresis effect in which the current QoE of a user is negatively influenced by drastic changes in video quality in the recent past.…”

mentioning

confidence: 73%

“…An objective full reference quality metric multi-scale structural similarity index (MS-SSIM) [15] is used for the frame level quality assessment of the decoded distorted video stream. In order to predict the overall objective quality score, the frame-level quality scores are temporally pooled accounting for the fact that users respond strongly to drops in video quality [8]. Unlike temporal averaging, this pooling strategy takes into account the effect of bad quality experienced in the recent past into the objective estimate of quality at the current instant.…”

Section: A Real-time Video Usersmentioning

confidence: 99%

“…The increase in video QoE due to shaping the interference is evaluated using the hysteresis based temporal pooling strategy for frame based objective quality scores proposed in [8]. The objective full reference multiscale structural similarity (MS-SSIM) index [15] is used for benchmarking the improvement 1 Throughput and capacity are used interchangeably in the paper.…”

Section: B Contributionsmentioning

confidence: 99%

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Interference Shaping for Improved Quality of Experience for Real-Time Video Streaming

Singh

Andrews

Veciana

2012

IEEE J. Select. Areas Commun.

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Abstract-The unpredictability of the wireless medium poses a major challenge to delivering a high quality of experience (QoE) for real-time video services. Bursty co-channel interference is a prominent cause of wireless throughput variability, which leads to video QoE degradation, even for a fixed average channel quality. In this paper, we propose and analyze a network-level resource management algorithm termed interference shaping to smooth out the throughput variations (and hence improve the QoE) of video users by decreasing the peak rate of co-channel best effort users. Wireless link capacity variations are mapped to the realtime video packet loss rate, and the interference shaping QoE gain for video users is quantified by benchmarking against a modified multi-scale structural similarity (H-MS-SSIM) index. H-MS-SSIM is an accurate perceptual video quality metric that incorporates the important hysteresis effect whereby the current QoE (which is subjective) may strongly depend on the recent past. The proposed technique increases mean QoE and reduces the QoE variability over time, with a net perceptual increase of about 2-3x in illustrative settings while incurring insignificant decrease in the QoE for co-channel best effort users. Interference shaping can be implemented in both unicast and multicast real-time video streaming with much higher potential gains for multicast.Index Terms-Quality of experience; resource allocation and interference management; cross-layer design; bursty interference, heterogeneous networks.

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