Topic
Video quality
About: Video quality is a research topic. Over the lifetime, 13143 publications have been published within this topic receiving 178307 citations.
Papers published on a yearly basis
Papers
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TL;DR: Improved video quality assessment algorithms are obtained by incorporating a recent model of human visual speed perception and incorporating the model as spatiotemporal weighting factors, where the weight increases with the information content and decreases with the perceptual uncertainty in video signals.
Abstract: Motion is one of the most important types of information contained in natural video, but direct use of motion information in the design of video quality assessment algorithms has not been deeply investigated. Here we propose to incorporate a recent model of human visual speed perception [Nat. Neurosci. 9, 578 (2006)] and model visual perception in an information communication framework. This allows us to estimate both the motion information content and the perceptual uncertainty in video signals. Improved video quality assessment algorithms are obtained by incorporating the model as spatiotemporal weighting factors, where the weight increases with the information content and decreases with the perceptual uncertainty. Consistent improvement over existing video quality assessment algorithms is observed in our validation with the video quality experts group Phase I test data set.
224 citations
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30 Jun 2017TL;DR: KoNViD-1k is reported on, a subjectively annotated VQA database consisting of 1,200 public-domain video sequences, fairly sampled from a large public video dataset, YFCC100m, aimed at ‘in the wild’ authentic distortions.
Abstract: Subjective video quality assessment (VQA) strongly depends on semantics, context, and the types of visual distortions. Currently, all existing VQA databases include only a small number of video sequences with artificial distortions. The development and evaluation of objective quality assessment methods would benefit from having larger datasets of real-world video sequences with corresponding subjective mean opinion scores (MOS), in particular for deep learning purposes. In addition, the training and validation of any VQA method intended to be ‘general purpose’ requires a large dataset of video sequences that are representative of the whole spectrum of available video content and all types of distortions. We report our work on KoNViD-1k, a subjectively annotated VQA database consisting of 1,200 public-domain video sequences, fairly sampled from a large public video dataset, YFCC100m. We present the challenges and choices we have made in creating such a database aimed at ‘in the wild’ authentic distortions, depicting a wide variety of content.
217 citations
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01 Sep 1998TL;DR: It is argued that ITU-recommended methods for subjective quality assessment of speech and video are not suitable for assessing the quality of many newer services and applications.
Abstract: There is currently much discussion of Quality of Service (QoS) measurements at the network level of real-time multimedia services, but it is the subjective qualify perceived by the user that will determine whether these applications are adopted This paper argues that ITU-recommended methods for subjective quality assessment of speech and video are not suitable for assessing the quality of many newer services and applications. We present an outline of what we believe to be a more suitable testing methodology, which acknowledges the multi-dimensional nature of perceived audio and video quality.
217 citations
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16 Jun 2003TL;DR: A method for post-processing the secondary SSCQE data to produce quality scores that are highly correlated to the original DSCQS and DSCS data is given.
Abstract: International recommendations for subjective video quality assessment (e.g., ITU-R BT.500-11) include specifications for how to perform many different types of subjective tests. Some of these test methods are double stimulus where viewers rate the quality or change in quality between two video streams (reference and impaired). Others are single stimulus where viewers rate the quality of just one video stream (the impaired). Two examples of the former are the double stimulus continuous quality scale (DSCQS) and double stimulus comparison scale (DSCS). An example of the latter is single stimulus continuous quality evaluation (SSCQE). Each subjective test methodology has claimed advantages. For instance, the DSCQS method is claimed to be less sensitive to context (i.e., subjective ratings are less influenced by the severity and ordering of the impairments within the test session). The SSCQE method is claimed to yield more representative quality estimates for quality monitoring applications. This paper considers data from six different subjective video quality experiments, originally performed with SSCQE, DSCQS and DSCS methodologies. A subset of video clips from each of these six experiments were combined and rated in a secondary SSCQE subjective video quality test. We give a method for postprocessing the secondary SSCQE data to produce quality scores that are highly correlated to the original DSCQS and DSCS data. We also provide evidence that human memory effects for time-varying quality estimation seem to be limited to about 15 seconds.
217 citations
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TL;DR: This survey paper looks at emerging research into the application of client-side, server- side, and in-network rate adaptation techniques to support DASH-based content delivery and provides context and motivation for the application.
Abstract: With companies such as Netflix and YouTube accounting for more than 50% of the peak download traffic on North American fixed networks in 2015, video streaming represents a significant source of Internet traffic. Multimedia delivery over the Internet has evolved rapidly over the past few years. The last decade has seen video streaming transitioning from User Datagram Protocol to Transmission Control Protocol-based technologies. Dynamic adaptive streaming over HTTP (DASH) has recently emerged as a standard for Internet video streaming. A range of rate adaptation mechanisms are proposed for DASH systems in order to deliver video quality that matches the throughput of dynamic network conditions for a richer user experience. This survey paper looks at emerging research into the application of client-side, server-side, and in-network rate adaptation techniques to support DASH-based content delivery. We provide context and motivation for the application of these techniques and review significant works in the literature from the past decade. These works are categorized according to the feedback signals used and the end-node that performs or assists with the adaptation. We also provide a review of several notable video traffic measurement and characterization studies and outline open research questions in the field.
216 citations