3,082 research outputs found
A genetic approach to Markovian characterisation of H.264 scalable video
We propose an algorithm for multivariate Markovian characterisation of H.264/SVC scalable video traces at the sub-GoP (Group of Pictures) level. A genetic algorithm yields Markov models with limited state space that accurately capture temporal and inter-layer correlation. Key to our approach is the covariance-based fitness function. In comparison with the classical Expectation Maximisation algorithm, ours is capable of matching the second order statistics more accurately at the cost of less accuracy in matching the histograms of the trace. Moreover, a simulation study shows that our approach outperforms Expectation Maximisation in predicting performance of video streaming in various networking scenarios
Markovian Characterisation of H.264/SVC scalable video
In this paper, a multivariate Markovian traffic: model is proposed to characterise H.264/SVC scalable video traces. Parametrisation by a genetic algorithm results in models with a limited state space which accurately capture. both the temporal and the inter-layer correlation of the traces. A simulation study further shows that the model is capable of predicting performance of video streaming in various networking scenarios
Influence of distortions of key frames on video transfer in wireless networks
In this paper it is shown that for substantial increase of video quality in
wireless network it is necessary to execute two obligatory points on
modernization of the communication scheme. The player on the received part
should throw back automatically duplicated RTP packets, server of streaming
video should duplicate the packets containing the information of key frames.
Coefficients of the mathematical model describing video quality in wireless
network have been found for WiFi and 3G standards and codecs MPEG-2 and MPEG-4
(DivX). The special experimental technique which has allowed collecting and
processing the data has been developed for calculation of values of factors.Comment: 6 pages, 4 figures, 2 Table
Semantic multimedia remote display for mobile thin clients
Current remote display technologies for mobile thin clients convert practically all types of graphical content into sequences of images rendered by the client. Consequently, important information concerning the content semantics is lost. The present paper goes beyond this bottleneck by developing a semantic multimedia remote display. The principle consists of representing the graphical content as a real-time interactive multimedia scene graph. The underlying architecture features novel components for scene-graph creation and management, as well as for user interactivity handling. The experimental setup considers the Linux X windows system and BiFS/LASeR multimedia scene technologies on the server and client sides, respectively. The implemented solution was benchmarked against currently deployed solutions (VNC and Microsoft-RDP), by considering text editing and WWW browsing applications. The quantitative assessments demonstrate: (1) visual quality expressed by seven objective metrics, e.g., PSNR values between 30 and 42 dB or SSIM values larger than 0.9999; (2) downlink bandwidth gain factors ranging from 2 to 60; (3) real-time user event management expressed by network round-trip time reduction by factors of 4-6 and by uplink bandwidth gain factors from 3 to 10; (4) feasible CPU activity, larger than in the RDP case but reduced by a factor of 1.5 with respect to the VNC-HEXTILE
Transport of video over partial order connections
A Partial Order and partial reliable Connection (POC) is an end-to-end transport connection authorized to deliver objects in an order that can differ from the transmitted one. Such a connection is also authorized to lose some objects. The POC concept is motivated by the fact that heterogeneous best-effort networks such as Internet are plagued by unordered delivery of packets and losses, which tax the performances of current applications and protocols. It has been shown, in several research works, that out of order delivery is able to alleviate (with respect to CO service) the use of end systems’ communication resources. In this paper, the efficiency of out-of-sequence delivery on MPEG video streams processing is studied. Firstly, the transport constraints (in terms of order and reliability) that can be relaxed by MPEG video decoders, for improving video transport, are detailed. Then, we analyze the performance gain induced by this approach in terms of blocking times and recovered errors. We demonstrate that POC connections fill not only the conceptual gap between TCP and UDP but also provide real performance improvements for the transport of multimedia streams such MPEG video
Multi-Frame Quality Enhancement for Compressed Video
The past few years have witnessed great success in applying deep learning to
enhance the quality of compressed image/video. The existing approaches mainly
focus on enhancing the quality of a single frame, ignoring the similarity
between consecutive frames. In this paper, we investigate that heavy quality
fluctuation exists across compressed video frames, and thus low quality frames
can be enhanced using the neighboring high quality frames, seen as Multi-Frame
Quality Enhancement (MFQE). Accordingly, this paper proposes an MFQE approach
for compressed video, as a first attempt in this direction. In our approach, we
firstly develop a Support Vector Machine (SVM) based detector to locate Peak
Quality Frames (PQFs) in compressed video. Then, a novel Multi-Frame
Convolutional Neural Network (MF-CNN) is designed to enhance the quality of
compressed video, in which the non-PQF and its nearest two PQFs are as the
input. The MF-CNN compensates motion between the non-PQF and PQFs through the
Motion Compensation subnet (MC-subnet). Subsequently, the Quality Enhancement
subnet (QE-subnet) reduces compression artifacts of the non-PQF with the help
of its nearest PQFs. Finally, the experiments validate the effectiveness and
generality of our MFQE approach in advancing the state-of-the-art quality
enhancement of compressed video. The code of our MFQE approach is available at
https://github.com/ryangBUAA/MFQE.gitComment: to appear in CVPR 201
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