Modeling packet-loss visibility in MPEG-2 video

Kanumuri, Sandeep; Cosman, Pamela C.; Reibman, Amy R.; Vaishampayan, Vinay A.

doi:10.1109/tmm.2005.864343

Cited by 133 publications

(110 citation statements)

References 18 publications

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“…The location of the packet is of significant importance, because different types of frames carry information of different nature. A similar approach for MPEG2 video is presented in [15]. This method uses different machine learning (ML) algorithms to predict the visibility of the lost packet on the presented video.…”

Section: Reduced and No Reference Methodsmentioning

confidence: 99%

Computational Inference and Control of Quality in Multimedia Services

Menkovski

2015

Springer Theses

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Please check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:www.tue.nl/taverne Take down policyIf you believe that this document breaches copyright please contact us at:openaccess@tue.nl providing details and we will investigate your claim. Copyright c 2013 by Vlado Menkovski All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means without the prior written consent of the author. Typeset using L A T E X, printed in The NetherlandsTo my parents, Angel who dedicated his life to his sons and Snežana whose strength inspires me deeply. SummaryQuality is the degree of excellence we expect of a service or a product. It is also one of the key factors that determine its value. For multimedia services, understanding the experienced quality means understanding how the delivered fidelity, precision and reliability correspond to the users' expectations. Yet the quality of multimedia services is inextricably linked to the underlying technology. It is developments in video recording, compression and transport as well as display technologies that enables high quality multimedia services to become ubiquitous. The constant evolution of these technologies delivers a steady increase in performance, but also a growing level of complexity. As new technologies stack on top of each other the interactions between them and their components become more intricate and obscure. In this environment optimizing the delivered quality of multimedia services becomes increasingly challenging. The factors that affect the experienced quality, or Quality of Experience (QoE), tend to have complex non-linear relat...

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Section: Reduced and No Reference Methodsmentioning

confidence: 99%

Computational Inference and Control of Quality in Multimedia Services

Menkovski

2015

Springer Theses

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“…A more recent piece of work presented mathematical foundations and quality predictors for decoders to conceal losses by copying or freezing [19]. Other recent work aims at predicting the visibility of a packet loss to the HVS [20]- [22], where the important issue of accurately evaluating the impact of packet loss on the quality of each frame has not been fully addressed. In [23], an FR metric for evaluating the quality degradation due to packet loss was presented utilizing the "PSNR drops" of erroneous frames.…”

Section: Impact Of Packet Loss On Perceivedmentioning

confidence: 99%

No-Reference Quality Assessment for Networked Video via Primary Analysis of Bit Stream

Yang

Wan

Xie

et al. 2010

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-A no-reference (NR) quality measure for networked video is introduced using information extracted from the compressed bit stream without resorting to complete video decoding. This NR video quality assessment measure accounts for three key factors which affect the overall perceived picture quality of networked video, namely, picture distortion caused by quantization, quality degradation due to packet loss and error propagation, and temporal effects of the human visual system. First, the picture quality in the spatial domain is measured, for each frame, relative to quantization under an error-free transmission condition. Second, picture quality is evaluated with respect to packet loss and the subsequent error propagation. The video frame quality in the spatial domain is, therefore, jointly determined by coding distortion and packet loss. Third, a pooling scheme is devised as the last step of the proposed quality measure to capture the perceived quality degradation in the temporal domain. The results obtained by performance evaluations using MPEG-4 coded video streams have demonstrated the effectiveness of the proposed NR video quality metric.Index Terms-Coding distortion, networked video, noreference video quality assessment, packet loss, temporal pooling.

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“…Although the proposed approach does not require availability of the video sequence at the receiving end, no general network performance metric that can be directly mapped to the perceived quality was identified. In a number of studies [53,93,52] the authors considered the effect of errors visibility in various video sequences. Particularly, in [53,52] they introduced artificial losses to the received stream of packets and tried to detect whether they are visible to a viewer.…”

Section: Network-related Metricsmentioning

confidence: 99%

Service quality in P2P streaming systems

Moltchanov

2011

Computer Science Review

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Modeling packet-loss visibility in MPEG-2 video

Cited by 133 publications

References 18 publications

Computational Inference and Control of Quality in Multimedia Services

Computational Inference and Control of Quality in Multimedia Services

No-Reference Quality Assessment for Networked Video via Primary Analysis of Bit Stream

Service quality in P2P streaming systems

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