Topic
Video quality
About: Video quality is a research topic. Over the lifetime, 13143 publications have been published within this topic receiving 178307 citations.
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01 Jun 2012
TL;DR: As it will show, the video clip encoding rate plays a relevant role in determining the traffic generation rate, and therefore, a cumulative density function for the most viewed video clips will be presented.
Abstract: YouTube currently accounts for a significant percentage of the Internet's global traffic. Hence, understanding the characteristics of the YouTube traffic generation pattern can provide a significant advantage in predicting user video quality and in enhancing network design. In this paper, we present a characterisation of the traffic generated by YouTube when accessed from a regular PC. On the basis of this characterisation, a YouTube server traffic generation model is proposed, which, for example, can be easily implemented in simulation tools. The derived characterisation and model are based on experimental evaluations of traffic generated by the application layer of YouTube servers. A YouTube server commences the download with an initial burst and later throttles down the generation rate. If the available bandwidth is reduced (e.g. in the presence of network congestion), the server behaves as if the data excess that cannot be transmitted because of the reduced bandwidth were accumulated at a server's buffer, which is later drained if the bandwidth availability is recovered. As we will show, the video clip encoding rate plays a relevant role in determining the traffic generation rate, and therefore, a cumulative density function for the most viewed video clips will be presented. The proposed traffic generation model was implemented in a YouTube emulation server, and the generated synthetic traffic traces were compared with downloads from the original YouTube server. The results show that the relative error between downloads from the emulation server and the original server does not exceed 6% for the 90% of the considered videos. Copyright © 2012 John Wiley & Sons, Ltd.
127 citations
TL;DR: An automatic key frame extraction method dedicated to summarizing consumer video clips acquired from digital cameras and demonstrates the effectiveness of the method by comparing the results with two alternative methods against the ground truth agreed by multiple judges.
Abstract: Extracting key frames from video is of great interest in many applications, such as video summary, video organization, video compression, and prints from video. Key frame extraction is not a new problem but existing literature has focused primarily on sports or news video. In the personal or consumer video space, the biggest challenges for key frame selection are the unconstrained content and lack of any pre-imposed structures. First, in a psychovisual study, we conduct ground truth collection of key frames from video clips taken by digital cameras (as opposed to camcorders) using both first- and third-party judges. The goals of this study are to: 1) create a reference database of video clips reasonably representative of the consumer video space; 2) identify consensus key frames by which automated algorithms can be compared and judged for effectiveness, i.e., ground truth; and 3) uncover the criteria used by both first- and third-party human judges so these criteria can influence algorithm design. Next, we develop an automatic key frame extraction method dedicated to summarizing consumer video clips acquired from digital cameras. Analysis of spatio-temporal changes over time provides semantically meaningful information about the scene and the camera operator's general intents. In particular, camera and object motion are estimated and used to derive motion descriptors. A video clip is segmented into homogeneous parts based on major types of camera motion (e.g., pan, zoom, pause, steady). Dedicated rules are used to extract candidate key frames from each segment. In addition, confidence measures are computed for the candidates to enable ranking in semantic relevance. This method is scalable so that one can produce any desired number of key frames from the candidates. Finally, we demonstrate the effectiveness of our method by comparing the results with two alternative methods against the ground truth agreed by multiple judges.
127 citations
TL;DR: The results indicate that there is no general superiority of UGVs over AGVs, and videos generated by users are rated more highly than agency-generated videos under both low and high technical qualities, but the advantage is significantly lower under high technical quality.
126 citations
23 Feb 2011
TL;DR: The benefits of using the Scalable Video Coding (SVC) for such a DASH environment is shown, which helps video clients dynamically adapt the requested video quality for ongoing video flows, to match their current download rate as good as possible.
Abstract: HTTP-based delivery for Video on Demand (VoD) has been gaining popularity within recent years. Progressive Download over HTTP, typically used in VoD, takes advantage of the widely deployed network caches to relieve video servers from sending the same content to a high number of users in the same access network. However, due to a sharp increase in the requests at peak hours or due to cross-traffic within the network, congestion may arise in the cache feeder link or access link respectively. Since the connection characteristics may vary over the time, with Dynamic Adaptive Streaming over HTTP (DASH), a technique that has been recently proposed, video clients may dynamically adapt the requested video quality for ongoing video flows, to match their current download rate as good as possible. In this work we show the benefits of using the Scalable Video Coding (SVC) for such a DASH environment.
126 citations
02 Nov 2003
TL;DR: An integrated power management approach that unifies low level architectural optimizations, OS power-saving mechanisms, and adaptive middleware techniques that supports tight coupling of inter-level parameters can enhance user experience on a handheld substantially is proposed.
Abstract: Optimizing user experience for streaming video applications on handheld devices is a significant research challenge In this paper, we propose an integrated power management approach that unifies low level architectural optimizations (CPU, memory, register), OS power-saving mechanisms (Dynamic Voltage Scaling) and adaptive middleware techniques (admission control, optimal transcoding, network traffic regulation) Specifically, we identify interaction parameters between the different levels and optimize them to significantly reduce power consumption With knowledge of device configurations, dynamic device parameters and changing system conditions, the middleware layer selects an appropriate video quality and fine tunes the architecture for optimized delivery of video Our performance results indicate that architectural optimizations that are cognizant of user level parameters(eg transcoded video quality) can provide energy gains as high as 575% for the CPU and memory Middleware adaptations to changing network noise levels can save as much as 70% of energy consumed by the wireless network interface Furthermore, we demonstrate how such an integrated framework, that supports tight coupling of inter-level parameters can enhance user experience on a handheld substantially
126 citations