143 research outputs found

    The perceptual and attentive impact of delay and jitter in multimedia delivery

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    In this paper we present the results of a study that examines the user’s perception—understood as both information assimilation and subjective satisfaction—of multimedia quality, when impacted by varying network-level parameters (delay and jitter). In addition, we integrate eye-tracking assessment to provide a more complete understanding of user perception of multimedia quality. Results show that delay and jitter significantly affect user satisfaction; variation in video eye path when either no single/obvious point of focus exists or when the point of attention changes dramatically. Lastly, results showed that content variation significantly affected user satisfaction, as well as user information assimilation

    Defining user perception of distributed multimedia quality

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    This article presents the results of a study that explored the human side of the multimedia experience. We propose a model that assesses quality variation from three distinct levels: the network, the media and the content levels; and from two views: the technical and the user perspective. By facilitating parameter variation at each of the quality levels and from each of the perspectives, we were able to examine their impact on user quality perception. Results show that a significant reduction in frame rate does not proportionally reduce the user's understanding of the presentation independent of technical parameters, that multimedia content type significantly impacts user information assimilation, user level of enjoyment, and user perception of quality, and that the device display type impacts user information assimilation and user perception of quality. Finally, to ensure the transfer of information, low-level abstraction (network-level) parameters, such as delay and jitter, should be adapted; to maintain the user's level of enjoyment, high-level abstraction quality parameters (content-level), such as the appropriate use of display screens, should be adapted

    Objective assessment of region of interest-aware adaptive multimedia streaming quality

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    Adaptive multimedia streaming relies on controlled adjustment of content bitrate and consequent video quality variation in order to meet the bandwidth constraints of the communication link used for content delivery to the end-user. The values of the easy to measure network-related Quality of Service metrics have no direct relationship with the way moving images are perceived by the human viewer. Consequently variations in the video stream bitrate are not clearly linked to similar variation in the user perceived quality. This is especially true if some human visual system-based adaptation techniques are employed. As research has shown, there are certain image regions in each frame of a video sequence on which the users are more interested than in the others. This paper presents the Region of Interest-based Adaptive Scheme (ROIAS) which adjusts differently the regions within each frame of the streamed multimedia content based on the user interest in them. ROIAS is presented and discussed in terms of the adjustment algorithms employed and their impact on the human perceived video quality. Comparisons with existing approaches, including a constant quality adaptation scheme across the whole frame area, are performed employing two objective metrics which estimate user perceived video quality

    Region of interest-based adaptive multimedia streaming scheme

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    Adaptive multimedia streaming aims at adjusting the transmitted content based on the available bandwidth such as losses that often severely affect the end-user perceived quality are minimized and consequently the transmission quality increases. Current solutions affect equally the whole viewing area of the multimedia frames, despite research showing that there are regions on which the viewers are more interested in than on others. This paper presents a novel region of interest-based adaptive scheme (ROIAS) for multimedia streaming that when performing transmission-related quality adjustments, selectively affects the quality of those regions of the image the viewers are the least interested in. As the quality of the regions the viewers are the most interested in will not change (or will involve little change),the proposed scheme provides higher overall end-user perceived quality than any of the existing adaptive solutions

    Relationship between IoT Service User Quality and Network QoS Factors

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    The Internet of Things (IoT) is a complete network of networked computer devices, digital and mechanical equipment, and the capacity to send data over the Internet based on machine to machine interaction. It is also known as the Internet of Everything (IoE). The Internet is a packet-switched network, which means that the Quality of Service (QoS) elements (such as packet loss, latency, jitter, and so on) have an influence on the Quality of Experience (QoE) for the Internet of Things services. This research used a subjective evaluation method in order to evaluate the relationship between the quality of service (QoS) measures such as packet loss, latency, and jitter and the quality of experience (QoE) for Internet of Things services. In addition to that, a mapping model from quality of service to quality of experience was suggested. According to the results of this research, there is a close connection between the subjective opinion score and the quality of service (QoS) variables such as packet loss, latency, and jitter. The results of this investigation open up possibilities for additional research into the quality of experience of Internet of Things services

    No-reference bitstream-based visual quality impairment detection for high definition H.264/AVC encoded video sequences

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    Ensuring and maintaining adequate Quality of Experience towards end-users are key objectives for video service providers, not only for increasing customer satisfaction but also as service differentiator. However, in the case of High Definition video streaming over IP-based networks, network impairments such as packet loss can severely degrade the perceived visual quality. Several standard organizations have established a minimum set of performance objectives which should be achieved for obtaining satisfactory quality. Therefore, video service providers should continuously monitor the network and the quality of the received video streams in order to detect visual degradations. Objective video quality metrics enable automatic measurement of perceived quality. Unfortunately, the most reliable metrics require access to both the original and the received video streams which makes them inappropriate for real-time monitoring. In this article, we present a novel no-reference bitstream-based visual quality impairment detector which enables real-time detection of visual degradations caused by network impairments. By only incorporating information extracted from the encoded bitstream, network impairments are classified as visible or invisible to the end-user. Our results show that impairment visibility can be classified with a high accuracy which enables real-time validation of the existing performance objectives
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