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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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Proceedings Article
01 Jan 2013
TL;DR: The evaluations show that the proposed client can outperform standard HAS in the evaluated networking environments, and dynamically learns the optimal behavior corresponding to the current network environment.
Abstract: Over the past decades, the importance of multimedia services such as video streaming has increased considerably. HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for adaptive video streaming services. In HAS, a video is split into multiple segments and encoded at multiple quality levels. State-of-the-art HAS clients employ deterministic heuristics to dynamically adapt the requested quality level based on the perceived network and device conditions. Current HAS client heuristics are however hardwired to fit specific network configurations, making them less flexible to fit a vast range of settings. In this article, an adaptive Q-Learning-based HAS client is proposed. In contrast to existing heuristics, the proposed HAS client dynamically learns the optimal behavior corresponding to the current network environment. Considering multiple aspects of video quality, a tunable reward function has been constructed, giving the opportunity to focus on different aspects of the Quality of Experience, the quality as perceived by the end-user. The proposed HAS client has been thoroughly evaluated using a network-based simulator, investigating multiple reward configurations and Reinforcement Learning specific settings. The evaluations show that the proposed client can outperform standard HAS in the evaluated networking environments.

63 citations

Proceedings ArticleDOI
14 Apr 2013
TL;DR: This paper designs and implements a framework of adaptive mobile video streaming and sharing in the NDN architecture (AMVS-NDN), considering that most of mobile stations have multiple wireless interfaces (e.g., 3G and WiFi).
Abstract: Recently, mobile traffic (especially video traffic) explosion becomes a serious concern for mobile network operators. While video streaming services become crucial for mobile users, their traffic may often exceed the bandwidth capacity of cellular networks. To address the video traffic problem, we consider a future Internet architecture: Named Data Networking (NDN). In this paper, we design and implement a framework of adaptive mobile video streaming and sharing in the NDN architecture (AMVS-NDN) considering that most of mobile stations have multiple wireless interfaces (e.g., 3G and WiFi). To demonstrate the benefit of NDN, AMVS-NDN has two key functionalities: (1) a mobile station (MS) seeks to use either 3G/4G or WiFi links opportunistically, and (2) MSs can share content directly by exploiting local WiFi connectivities. We implement AMVS-NDN over CCNx, and perform tests in a real testbed consisting of a WiMAX base station and Android phones. Testing with time-varying link conditions in mobile environments reveals that AMVS-NDN achieves the higher video quality and less cellular traffic than other solutions.

63 citations

Proceedings ArticleDOI
15 Oct 2018
TL;DR: Wang et al. as discussed by the authors proposed QARC (video Quality Aware Rate Control), a rate control algorithm that aims to obtain a higher perceptual video quality with possible lower sending rate and transmission latency.
Abstract: Real-time video streaming is now one of the main applications in all network environments. Due to the fluctuation of throughput under various network conditions, how to choose a proper bitrate adaptively has become an upcoming and interesting issue. To tackle this problem, most proposed rate control methods work for providing high video bitrates instead of video qualities. Nevertheless, we notice that there exists a trade-off between sending bitrate and video quality, which motivates us to focus on how to reach a balance between them. In this paper, we propose QARC (video Quality Aware Rate Control), a rate control algorithm that aims to obtain a higher perceptual video quality with possible lower sending rate and transmission latency. Starting from scratch, QARC uses deep reinforcement learning(DRL) algorithm to train a neural network to select future bitrates based on previously observed network status and past video frames. To overcome the "state explosion problem'', we design a neural network to predict future perceptual video quality as a vector for taking the place of the raw picture in the DRL's inputs. We evaluate QARC via trace-driven simulation, outperforming existing approach with improvements in average video quality of 18% - 25% and decreasing in average latency with 23% -45%. Meanwhile, comparing QARC with offline optimal high bitrate method on various network conditions, we find that QARC also yields a solid result.

63 citations

Proceedings ArticleDOI
18 Apr 2006
TL;DR: The effect of burst packet losses on the video delivered quality is less than distributed packet losses in the same packet loss rate, and the smaller size of the play-out buffer leads to more packet drops and worse video quality.
Abstract: The purpose of this paper is to study the packet loss effect on MPEG video transmission quality in wireless networks. First, we consider the distribution of packet losses in wireless network, including distributed and burst packet losses. Besides, we also discuss the additional packet drops by the play-out buffer at the received end, and the effect of the transmission packet size on the video delivered quality. From the results, we find that the effect of burst packet losses on the video delivered quality is less than distributed packet losses in the same packet loss rate. Moreover, the smaller size of the play-out buffer leads to more packet drops and worse video quality. Finally, if there is no video recovery for video transmission, the video delivered quality of the larger packet size will be better than smaller packet size.

62 citations

Proceedings ArticleDOI
16 Jun 2003
TL;DR: This paper presents a subjective method and an objective method for combining multiple subjective data sets and demonstrates that the objective method can be used as an effective substitute for the expensive and time consuming subjective meta-test.
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. In addition to displaying the video sequences in different ways, subjective tests also have different rating scales, different words associated with these scales, and many other test variables that change from one laboratory to another (e.g., viewer expertise and criticality, cultural differences, physical test environments). Thus, it is very difficult to directly compare or combine results from two or more subjective experiments. The ability to compare and combine results from multiple subjective experiments would greatly benefit developers and users of video technology since standardized subjective data bases could be expanded upon to include new source material and past measurement results could be related to newer measurement results. This paper presents a subjective method and an objective method for combining multiple subjective data sets. The subjective method utilizes a large meta-test with selected video clips from each subjective data set. The objective method utilizes the functional relationships between objective video quality metrics (extracted from the video sequences) and corresponding subjective mean opinion scores (MOSs). The objective mapping algorithm, called the iterated nested least-squares algorithm (INLSA), relates two or more independent data sets that are themselves correlated with some common intermediate variables (i.e, the objective video quality metrics). We demonstrate that the objective method can be used as an effective substitute for the expensive and time consuming subjective meta-test.

62 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023139
2022336
2021399
2020535
2019609
2018673