Adaptive video transmission with subjective quality constraints

Chen, Chao; Zhu, Xiaoqing; Veciana, Gustavo de; Bovik, Alan C.; Heath, Robert W.

doi:10.1109/icip.2014.7025501

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…Recently, some studies [23]- [31] investigate the impact of bit rate changes and video resolution variations on the users' QoE. However, these studies do not consider the pauses and the temporal location of the degradation events in their proposed models.…”

Section: B Test Methodologies For Video Quality Assessmentmentioning

confidence: 97%

“…Some studies [3]- [9] have proposed models that incorporate the impact of temporal interruptions or pauses; however, they do not consider the video resolution changes. Some recent studies treat the impact of video quality switching [23]- [31], most of them focusing on qualitative and not on quantitative models to measure the impairments. These cited models do not consider the pauses and their temporal locations.…”

Section: Introductionmentioning

confidence: 99%

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Video Quality Metric for Streaming Service Using DASH Standard

Rodríguez

Rosa

Alfaia

et al. 2016

IEEE Trans. on Broadcast.

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Currently, dynamic adaptive streaming over HTTP (DASH) standard is used to change the video resolution according to the network capacity of the end user's. However, if the video resolution changes frequently, the attention of the users can be affected decreasing their quality-of-experience (QoE). In this context, this paper proposes a no-reference video quality metric that takes three impairment factors into account, initial buffering delay, temporal interruptions or pauses, and video resolution changes during a video transmission. Also, the temporal locations of pauses and resolution changes are considered. In order to perform this task, extensive subjective tests were conducted. Experimental results show that the evaluators' QoE is highly correlated with the frequency of pauses, video resolution changes and initial delay. Based on these results a video streaming quality metric for DASH services (VsQM DASH ) is formulated and implemented on PC and mobile hand-held devices. Finally, the performance of VsQM DASH is validated with additional subjective tests reaching a person correlation coefficient of 0.99. Furthermore, experimental results demonstrated that the proposed metric has low complexity because it is based on application level parameters and its energy consumption is negligible in relation to the energy consumed by the video player; therefore, can be easily implemented in consumer electronic devices.

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Section: B Test Methodologies For Video Quality Assessmentmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Video Quality Metric for Streaming Service Using DASH Standard

Rodríguez

Rosa

Alfaia

et al. 2016

IEEE Trans. on Broadcast.

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show abstract

“…In these scenarios, other approaches such as Reduced Reference (RR) or No-Reference (NR) metrics based on scene characteristics and network characteristics could be used [93] [107]. For instance, the method described in [108] tries to predict quality based on the bitrate. Recently proposed ITU-T Rec.…”

Section: A Ctvq Modelling Challengesmentioning

confidence: 99%

Measuring, Modeling and Integrating Time-Varying Video Quality in End-to-End Multimedia Service Delivery: A Review and Open Challenges

et al. 2022

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The multimedia delivery chain consists of multiple stages such as content preparation, content delivery via Over-The-Top delivery network and Internet Service Providers network. Within the multimedia service chain, each stage influences the Quality of Experience (QoE) of the end user. The objective of this work is to provide a comprehensive literature survey with future research challenges and opportunities in the field of time-varying video quality in multimedia service delivery. The contribution of this work is two fold: 1) Survey -we provide a review of state-of-the-art works for video quality models to quantify multiple artifacts into a single QoE metric, pooling strategies for global quality measurements, and Continuous Time-Varying Quality (CTVQ) models; 2) Future Challenges and Directions -we investigate ten major research challenges and future directions based on the state-of-the-art for QoE modelling, QoE-aware encoding/decoding and QoE monitoring/management of multimedia streaming in next-generation networks.

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“…Even though many studies deal with the video quality assessment using subjective methods, the demand exceeds the supply; there is still a lack of video quality datasets, as well as recorded subjective tests, conducted on these datasets. Very popular and extensively used datasets, such as References [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ], come from the University of Texas and were developed by the Laboratory for Image and Video Engineering. Another very popular option is the VQEG-HDTV database [ 28 ], which is a result of international project of VQEG (Video Quality Experts Group) consortium.…”

Section: Introductionmentioning

confidence: 99%

Impact of Scene Content on High Resolution Video Quality

Uhrina

Holesova

Bienik

et al. 2021

Sensors

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This paper deals with the impact of content on the perceived video quality evaluated using the subjective Absolute Category Rating (ACR) method. The assessment was conducted on eight types of video sequences with diverse content obtained from the SJTU dataset. The sequences were encoded at 5 different constant bitrates in two widely video compression standards H.264/AVC and H.265/HEVC at Full HD and Ultra HD resolutions, which means 160 annotated video sequences were created. The length of Group of Pictures (GOP) was set to half the framerate value, as is typical for video intended for transmission over a noisy communication channel. The evaluation was performed in two laboratories: one situated at the University of Zilina, and the second at the VSB—Technical University in Ostrava. The results acquired in both laboratories reached/showed a high correlation. Notwithstanding the fact that the sequences with low Spatial Information (SI) and Temporal Information (TI) values reached better Mean Opinion Score (MOS) score than the sequences with higher SI and TI values, these two parameters are not sufficient for scene description, and this domain should be the subject of further research. The evaluation results led us to the conclusion that it is unnecessary to use the H.265/HEVC codec for compression of Full HD sequences and the compression efficiency of the H.265 codec by the Ultra HD resolution reaches the compression efficiency of both codecs by the Full HD resolution. This paper also includes the recommendations for minimum bitrate thresholds at which the video sequences at both resolutions retain good and fair subjectively perceived quality.

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Adaptive video transmission with subjective quality constraints

Cited by 7 publications

References 18 publications

Video Quality Metric for Streaming Service Using DASH Standard

Video Quality Metric for Streaming Service Using DASH Standard

Measuring, Modeling and Integrating Time-Varying Video Quality in End-to-End Multimedia Service Delivery: A Review and Open Challenges

Impact of Scene Content on High Resolution Video Quality

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