QoE as a Function of Frame Rate and Resolution Changes

Janowski, Lucjan; Romaniak, Piotr

doi:10.1007/978-3-642-13789-1_4

Cited by 31 publications

(17 citation statements)

References 3 publications

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“…This dependency can be based on the psychovisual factors such as objective characteristics of the content (e.g. spatial and temporal complexities) as the rate distortion performance of encoded video depends largely on these factors [17], in addition to psychological factors like user expectations and desirability of the video [18] which could be because of the content genera. To investigate the impact of these factors on perception of adaptation, the switching scenarios were applied on various video sequences in different objective characterizations and genera which are usually watched by the actual viewers in the real-world condition.…”

Section: Study Factorsmentioning

confidence: 99%

Quality of Experience of adaptive video streaming: Investigation in service parameters and subjective quality assessment methodology

Tavakoli

Brunnström

Gutiérrez

et al. 2015

Signal Processing: Image Communication

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The usage of HTTP adaptive streaming (HAS) has become widely spread in multimedia services. Because it allows the service providers to improve the network resource utilization and user's Quality of Experience (QoE). Using this technology, the video playback interruption is reduced since the network and server status in addition to capability of user device, all are taken into account by HAS client to adapt the quality to the current condition. Adaptation can be done using different strategies. In order to provide optimal QoE, the perceptual impact of adaptation strategies from point of view of the user should be studied. However, the time-varying video quality due to the adaptation which usually takes place in a long interval introduces a new type of impairment making the subjective evaluation of adaptive streaming system challenging. The contribution of this paper is two-fold: first, it investigates the testing methodology to evaluate HAS QoE by comparing the subjective experimental outcomes obtained from ACR standardized method and a semi-continuous method developed to evaluate the long sequences. In addition, influence of using audiovisual stimuli to evaluate the video-related impairment is inquired. Second, impact of some of the adaptation technical factors including the quality switching amplitude and chunk size in combination with high range of commercial content type is investigated. The results of this study provide a good insight toward achieving appropriate testing method to evaluate HAS QoE, in addition to designing switching strategies with optimal visual quality.

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Section: Study Factorsmentioning

confidence: 99%

Quality of Experience of adaptive video streaming: Investigation in service parameters and subjective quality assessment methodology

Tavakoli

Brunnström

Gutiérrez

et al. 2015

Signal Processing: Image Communication

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“…Selected QP values were adjusted for different video processing paths in order to provide a wide range of plate number recognition ability thresholds. Frame rates have been kept intact as, due to inter-frame coding, their deterioration does not necessarily result in bit-rates savings [16]. Furthermore, network streaming artefacts have been not considered as the authors believe in numerous cases they are related to excessive bit-streams, which had been already addressed by different QPs.…”

Section: Processed Video Sequencesmentioning

confidence: 99%

Quality assessment for a visual and automatic license plate recognition

Janowski

Kozłowski

Baran

et al. 2012

Multimed Tools Appl

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Video transmission and analysis is often utilized in applications outside of the entertainment sector, and generally speaking this class of video is used to perform specific tasks. Examples of these applications include security and public safety. The Quality of Experience (QoE) concept for video content used for entertainment differs significantly from the QoE of surveillance video used for recognition tasks. This is because, in the latter case, the subjective satisfaction of the user depends on achieving a given functionality. Recognizing the growing importance of video in delivering a range of public safety services, we focused on developing critical quality thresholds in license plate recognition tasks based on videos streamed in constrained networking conditions. Since the number of surveillance cameras is still growing it is obvious that automatic systems will be used to do the tasks. Therefore, the presented research includes also analysis of automatic recognition algorithms.

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“…Issues of quality measurements for task-based video are partially addressed in the ITU-T P.912 Recommendation "Subjective video quality assessment methods for recognition tasks" (ITU-T, 2008). This Recommendation introduces basic definitions, methods of testing and ways of conducting psycho-physical experiments (e.g.…”

Section: How It Influences the Subjective Experiment?mentioning

confidence: 99%

“…Anyway, the QoP metric does not entirely fit video surveillance needs. It targets mainly video deterioration caused by frame rate (fps), whereas fps not necessarily affects the quality of CCTV and the required bandwidth (Janowski & Romaniak, 2010). The metric has been established for rather low, legacy resolutions, and tested on rather small groups of subjects (10 instead of standardized 24 valid, correlating subjects).…”

mentioning

confidence: 99%

“…The metric has been established for rather low, legacy resolutions, and tested on rather small groups of subjects (10 instead of standardized 24 valid, correlating subjects). Furthermore, a video recognition quality metric for a clear objective of video surveillance context requires tests in fully controlled environment (ITU-T, 2000), with standardized discrimination levels (avoiding ambiguous questions) and with minimized impact of subliminal cues (ITU-T, 2008).…”

mentioning

confidence: 99%

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