Study of Temporal Effects on Subjective Video Quality of Experience

Bampis, Christos G.; Li, Zhi; Moorthy, Anush K.; Katsavounidis, Ioannis; Aaron, Anne; Bovik, Alan C.

doi:10.1109/tip.2017.2729891

Cited by 160 publications

(113 citation statements)

References 36 publications

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“…In this study, we used two adaptation sets, each consisted of 9 versions with different QP values and/or resolutions. In particular, the 9 versions in the first adaptation set have the same resolution of 1280×720 and 9 different QP values of 52, 48, 44,40,36,32,28,24, and 20. The first adaptation set was used to generate the streaming sessions of Video #1, Video #2, and Video #3.…”

Section: Subjective Test For Cumulative Qualitymentioning

confidence: 99%

A Cumulative Quality Model for HTTP Adaptive Streaming

Tran

Ngoc

Hoβfeld

et al. 2018

2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX)

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Thanks to the abundance of Web platforms and broadband connections, HTTP Adaptive Streaming has become the de facto choice for multimedia delivery nowadays. However, the visual quality of adaptive video streaming may fluctuate strongly during a session due to bandwidth fluctuations. So, it is important to evaluate the quality of a streaming session over time. In this paper, we propose a model to estimate the cumulative quality for HTTP Adaptive Streaming. In the model, a sliding window of video segments is employed as the basic building block. Through statistical analysis using a subjective dataset, we identify three important components of the cumulative quality model, namely the minimum window quality, the last window quality, and the average window quality. Experiment results show that the proposed model achieves high prediction performance and outperforms related quality models. In addition, another advantage of the proposed model is its simplicity and effectiveness for deployment in real-time estimation. The source code of the proposed model has been made available to the public at https

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Section: Subjective Test For Cumulative Qualitymentioning

confidence: 99%

A Cumulative Quality Model for HTTP Adaptive Streaming

Tran

Ngoc

Hoβfeld

et al. 2018

2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX)

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“…For majority of the models, the video sequences that are selected from the VQEG database are very short in duration (roughly 10 s only) and hence their ability to portray a real life-streaming scenario is questionable. In addition, the effect of using videos lesser or greater than 10 seconds on the subjective quality assessment has not been accounted for [59,60].…”

Section: Current Limitations and Challengesmentioning

confidence: 99%

A Survey of Standardized Approaches towards the Quality of Experience Evaluation for Video Services: An ITU Perspective

Pal

Triyason

2018

International Journal of Digital Multimedia Broadcasting

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Over the past few years there has been an exponential increase in the amount of multimedia data being streamed over the Internet. At the same time, we are also witnessing a change in the way quality of any particular service is interpreted, with more emphasis being given to the end-users. Thus, silently there has been a paradigm shift from the traditional Quality of Service approach (QoS) towards a Quality of Experience (QoE) model while evaluating the service quality. A lot of work that tries to evaluate the quality of audio, video, and multimedia services over the Internet has been done. At the same time, research is also going on trying to map the two different domains of quality metrics, i.e., the QoS and QoE domain. Apart from the work done by individual researchers, the International Telecommunications Union (ITU) has been quite active in this area of quality assessment. This is obvious from the large number of ITU standards that are available for different application types. The sheer variety of techniques being employed by ITU as well as other researchers sometimes tends to be too complex and diversified. Although there are survey papers that try to present the current state of the art methodologies for video quality evaluation, none has focused on the ITU perspective. In this work, we try to fill up this void by presenting up-to-date information on the different measurement methods that are currently being employed by ITU for a video streaming scenario. We highlight the outline of each method with sufficient detail and try to analyze the challenges being faced along with the direction of future research.

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“…Human judgments of visual image/video quality depend on content, which is well known in many subjective experiments [1,6,26,41,43,46,53]. For images, Siahaan et al show that scene and object categories influence human judgments of visual quality for JPEG compressed and blurred images [41].…”

Section: Introductionmentioning

confidence: 98%

Quality Assessment of In-the-Wild Videos

Jiang

2019

Proceedings of the 27th ACM International Conference on Multimedia

261

245

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Quality assessment of in-the-wild videos is a challenging problem because of the absence of reference videos and shooting distortions. Knowledge of the human visual system can help establish methods for objective quality assessment of in-the-wild videos. In this work, we show two eminent effects of the human visual system, namely, content-dependency and temporal-memory effects, could be used for this purpose. We propose an objective no-reference video quality assessment method by integrating both effects into a deep neural network. For content-dependency, we extract features from a pre-trained image classification neural network for its inherent content-aware property. For temporal-memory effects, long-term dependencies, especially the temporal hysteresis, are integrated into the network with a gated recurrent unit and a subjectivelyinspired temporal pooling layer. To validate the performance of our method, experiments are conducted on three publicly available inthe-wild video quality assessment databases: KoNViD-1k, CVD2014, and LIVE-Qualcomm, respectively. Experimental results demonstrate that our proposed method outperforms five state-of-the-art methods by a large margin, specifically, 12.39%, 15.71%, 15.45%, and 18.09% overall performance improvements over the second-best method VBLIINDS, in terms of SROCC, KROCC, PLCC and RMSE, respectively. Moreover, the ablation study verifies the crucial role of both the content-aware features and the modeling of temporalmemory effects. The PyTorch implementation of our method is released at https://github.com/lidq92/VSFA. KEYWORDSvideo quality assessment; human visual system; content dependency; temporal-memory effects; in-the-wild videos ACM Reference Format:

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Study of Temporal Effects on Subjective Video Quality of Experience

Cited by 160 publications

References 36 publications

A Cumulative Quality Model for HTTP Adaptive Streaming

A Cumulative Quality Model for HTTP Adaptive Streaming

A Survey of Standardized Approaches towards the Quality of Experience Evaluation for Video Services: An ITU Perspective

Quality Assessment of In-the-Wild Videos

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