Analysis of the impact of temporal, spatial, and quantization variations on perceptual video quality

Rossholm, Andreas; Shahid, Muhammad; Lövström, Benny

doi:10.1109/noms.2014.6838397

Cited by 7 publications

(2 citation statements)

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“…• The task of finding the optimal trade-off between temporal and spatial resolution, and the level of quantization for its impacts on the perceptual quality in different scenarios of application, is challenging [201]. This issue should be taken into consideration for the future development of NR methods.…”

Section: Discussionmentioning

confidence: 99%

No-reference image and video quality assessment: a classification and review of recent approaches

et al. 2014

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The field of perceptual quality assessment has gone through a wide range of developments and it is still growing. In particular, the area of no-reference (NR) image and video quality assessment has progressed rapidly during the last decade. In this article, we present a classification and review of latest published research work in the area of NR image and video quality assessment. The NR methods of visual quality assessment considered for review are structured into categories and subcategories based on the types of methodologies used for the underlying processing employed for quality estimation. Overall, the classification has been done into three categories, namely, pixel-based methods, bitstream-based methods, and hybrid methods of the aforementioned two categories. We believe that the review presented in this article will be helpful for practitioners as well as for researchers to keep abreast of the recent developments in the area of NR image and video quality assessment. This article can be used for various purposes such as gaining a structured overview of the field and to carry out performance comparisons for the state-of-the-art methods. Keywords:No-reference; Image quality assessment; Video quality assessment; Perceptual quality 1 Review IntroductionThere has been a tremendous progress recently in the usage of digital images and videos for an increasing number of applications. Multimedia services that have gained wide interest include digital television broadcasts, video streaming applications, and real-time audio and video services over the Internet. The global mobile data traffic grew by 81% in 2013, and during 2014, the number of mobileconnected devices will exceed the number of people on earth, according to predictions made by Cisco. The video portion of the mobile data traffic was 53% in 2013 and is expected to exceed 67% by 2018 [1]. With this huge increase in the exposure of image and video to the human eye, the interest in delivering quality of experience (QoE) may increase naturally. The quality of visual media can get degraded during capturing, compression, transmission, reproduction, and displaying due to the distortions that might occur at any of these stages. The legitimate judges of visual quality are humans as end users, the opinions of whom can be obtained by subjective experiments. Subjective experiments involve a panel of participants which are usually non-experts, also referred to as test subjects, to assess the perceptual quality of given test material such as a sequence of images or videos. Subjective experiments are typically conducted in a controlled laboratory environment. Careful planning and several factors including assessment method, selection of test material, viewing conditions, grading scale, and timing of presentation have to be considered prior to a subjective experiment. For example, Recommendation (ITU-R) BT.500 [2] provides detailed guidelines for conducting subjective experiments for the assessment of quality of television pictures. The outcomes of a subjectiv...

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Section: Discussionmentioning

confidence: 99%

No-reference image and video quality assessment: a classification and review of recent approaches

et al. 2014

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“…Several studies have demonstrated that this assumption is not entirely valid, and that the preference for frame rate versus quality is dependent on bandwidth constraints applied to the video stream, with a high frame rate preferred over high frame quality when bandwidth is limited, and the preference transitioning to frame quality over frame rate when more bandwidth is available, even for fast-motion video [7], [8].…”

Section: Related Workmentioning

confidence: 99%

A Study of User Perception of the Quality of Video Content Rendered Inside a 3-D Virtual Environment

Pourashraf

Safaei

Franklin

2017

IEEE J. Sel. Top. Signal Process.

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Abstract-This paper reports on the results of a user study which assessed the perceptual impact of relevant video parameters, such as resolution and frame rate, based on the perspectives of participants in the virtual environment. A mathematical model for video rate is presented that expresses the total rate as the product of separate functions of spatial and temporal resolutions. Results from the user study are combined with the model to predict the rate parameters which will result in perceptually acceptable quality using the 3D features of the virtual environment. The results show that by exploiting the insensitivity of users to controlled quality degradation, the downstream network load for the client can be significantly reduced with little or no perceptual impact on the clients.Index Terms-Video quality differentiation, subjective video quality assessments, video conferencing, 3D immersive environments, rate model, adaptive video content

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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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Analysis of the impact of temporal, spatial, and quantization variations on perceptual video quality

Cited by 7 publications

References 9 publications

No-reference image and video quality assessment: a classification and review of recent approaches

No-reference image and video quality assessment: a classification and review of recent approaches

A Study of User Perception of the Quality of Video Content Rendered Inside a 3-D Virtual Environment

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

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