Perceptual difference evaluation of video alternatives in adaptive streaming

Thang, Truong Cong; Nguyen, Hoc X.; Pham, Anh T.; Ngoc, Nam Pham

doi:10.1109/cce.2012.6315921

Cited by 9 publications

(8 citation statements)

References 10 publications

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“…In [31] it was studied how spatial and temporal quality switching had different impact on the QoE and which features of the switching that were most relevant in relation to the QoE. The results reported in [42] investigated the optimum number of coding quality levels that could be used in an adaptive video system by studying the just noticeable difference levels that exist in the quality range of video content. The incorporation of the effects of frame rate and resolution adaptations on the user perception to obtain the encoding configuration that maximizes the QoE for a certain type of content has been investigated in [7].…”

Section: Related Workmentioning

confidence: 99%

“…The ClosetoEdge video (43,24) is from a documentary and is mostly shot with a handheld camera and features varying characteristics. The Rollingstone video (45,42) is from a concert recording, where the lead singer moves around a lot and the video has some sudden scene changes. The News (49,23) video is from a TV news broadcast and has static scenes with one/two standing/sitting people, some scenes with moving background and some scenes without reporters with more motion and panning.…”

Section: Subjective Experiments Of Video Qualitymentioning

confidence: 99%

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On subjective quality assessment of adaptive video streaming via crowdsourcing and laboratory based experiments

Sogaard

Shahid

Pokhrel

et al. 2016

Multimed Tools Appl

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Video streaming services are offered over the Internet and since the service providers do not have full control over the network conditions all the way to the end user, streaming technologies have been developed to maintain the quality of service in these varying network conditions i.e. so called adaptive video streaming. In order to cater for users' Quality of Experience (QoE) requirements, HTTP based adaptive streaming solutions of video services have become popular. However, the keys to ensure the users a good QoE with this technology is still not completely understood. User QoE feedback is therefore instrumental in improving this understanding. Controlled laboratory based perceptual quality experiments that involve a panel of human viewers are considered to be the most valid method of the assessment of QoE. Besides laboratory based subjective experiments, crowdsourcing based subjective assessment of video quality is gaining popularity as an alternative

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Section: Related Workmentioning

confidence: 99%

Section: Subjective Experiments Of Video Qualitymentioning

confidence: 99%

On subjective quality assessment of adaptive video streaming via crowdsourcing and laboratory based experiments

Sogaard

Shahid

Pokhrel

et al. 2016

Multimed Tools Appl

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“…In this study, we propose a novel trellis-based method that can find the best appropriate bitrates for near future segments. Especially, JND (Just Noticeable Difference [8] [9]) is used as a subjective quality metric to help the client in making the decision. To the best of our knowledge, this paper presents for the first time a way to use a subjective quality metric for adaptivity in HTTP streaming.…”

Section: Introductionmentioning

confidence: 99%

Quality-driven bitrate adaptation method for HTTP live-streaming

Lê

Nguyen

Ngoc

et al. 2015

2015 IEEE International Conference on Communication Workshop (ICCW)

Self Cite

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In this paper, we focus on the bitrate adaptation issue of HTTP live video streaming. We first present a systematic approach that enables the client to choose the best video quality when deciding video bitrate for next several segments. After that, based on the concept of JND (Just Noticeable Difference), an adaptation method is proposed to meet the tradeoff between the requirements of buffer stability and smooth video quality. The experiment results show that our proposed method can provide smooth videos in terms of perceptual quality even under a small buffer size (i.e. 10s). To the best of our knowledge, this paper presents for the first time the way to use a perceptual quality metric for adaptivity in HTTP streaming.

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“…Their study outperforms the other available algorithms in terms of switching frequency, and usage of the available resources; however it does not take the amplitude of quality switches into account. Some subjective studies have already been published, for example, Thang et al studied the optimum number of coding quality levels that could be used in an adaptive video system by investigating the Just Noticeable Difference (JND) levels existing in the quality range of mobile video content taking into account the perceptual factors of the human visual system [9]. Their experiment was done by side-by-side comparison of different quality levels looking for noticeable differences.…”

Section: Dash Quality Metrics and Related Workmentioning

confidence: 99%

Subjective quality assessment of an adaptive video streaming model

et al. 2014

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With the recent increased popularity and high usage of HTTP Adaptive Streaming (HAS) techniques, various studies have been carried out in this area which generally focused on the technical enhancement of HAS technology and applications. However, a lack of common HAS standard led to multiple proprietary approaches which have been developed by major Internet companies. In the emerging MPEG-DASH standard the packagings of the video content and HTTP syntax have been standardized; but all the details of the adaptation behavior are left to the client implementation. Nevertheless, to design an adaptation algorithm which optimizes the viewing experience of the enduser, the multimedia service providers need to know about the Quality of Experience (QoE) of different adaptation schemes. Taking this into account, the objective of this experiment was to study the QoE of a HAS-based video broadcast model. The experiment has been carried out through a subjective study of the end user response to various possible clients' behavior for changing the video quality taking different QoE-influence factors into account. The experimental conclusions have made a good insight into the QoE of different adaptation schemes which can be exploited by HAS clients for designing the adaptation algorithms.

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Perceptual difference evaluation of video alternatives in adaptive streaming

Cited by 9 publications

References 10 publications

On subjective quality assessment of adaptive video streaming via crowdsourcing and laboratory based experiments

On subjective quality assessment of adaptive video streaming via crowdsourcing and laboratory based experiments

Quality-driven bitrate adaptation method for HTTP live-streaming

Subjective quality assessment of an adaptive video streaming model

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