Study of Subjective and Objective Quality Assessment of Video

Seshadrinathan, Kalpana; Soundararajan, Rajiv; Bovik, Alan C.; Cormack, Lawrence K.

doi:10.1109/tip.2010.2042111

Cited by 1,007 publications

(572 citation statements)

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“…The raw video samples we used for this assessment are part of the Live Video Quality Database [34,68]. The description of each video is given in to 2Mb/s.…”

Section: Experimental Analysis Of Objective Qoementioning

confidence: 99%

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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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“…The raw video samples we used for this assessment are part of the Live Video Quality Database [34,68]. The description of each video is given in to 2Mb/s.…”

Section: Experimental Analysis Of Objective Qoementioning

confidence: 99%

“…The Live video database [68] contains a set of test sequences impaired with two types of compression methods and two types of transports errors. In this study the subjective test was implemented using the DMOS [79] scale.…”

Section: Rating the Qualitymentioning

confidence: 99%

Computational Inference and Control of Quality in Multimedia Services

Menkovski

2015

Springer Theses

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“…The source video "Pedestrian Area" in RAW uncompressed progressive scan YUV420 format with a resolution of 768×432 and frame rate of 30 frames per second (f/s) from LIVE video database [31][32] is used. The video chosen depicted the scene of a busy market place with people moving in and out of frame with camera remaining still.…”

Section: ) Video Quality Assessmentmentioning

confidence: 99%

Interference Shaping for Improved Quality of Experience for Real-Time Video Streaming

Singh

Andrews

Veciana

2012

IEEE J. Select. Areas Commun.

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Abstract-The unpredictability of the wireless medium poses a major challenge to delivering a high quality of experience (QoE) for real-time video services. Bursty co-channel interference is a prominent cause of wireless throughput variability, which leads to video QoE degradation, even for a fixed average channel quality. In this paper, we propose and analyze a network-level resource management algorithm termed interference shaping to smooth out the throughput variations (and hence improve the QoE) of video users by decreasing the peak rate of co-channel best effort users. Wireless link capacity variations are mapped to the realtime video packet loss rate, and the interference shaping QoE gain for video users is quantified by benchmarking against a modified multi-scale structural similarity (H-MS-SSIM) index. H-MS-SSIM is an accurate perceptual video quality metric that incorporates the important hysteresis effect whereby the current QoE (which is subjective) may strongly depend on the recent past. The proposed technique increases mean QoE and reduces the QoE variability over time, with a net perceptual increase of about 2-3x in illustrative settings while incurring insignificant decrease in the QoE for co-channel best effort users. Interference shaping can be implemented in both unicast and multicast real-time video streaming with much higher potential gains for multicast.Index Terms-Quality of experience; resource allocation and interference management; cross-layer design; bursty interference, heterogeneous networks.

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“…However, capturing conditions such as irregular camera motion (IRRCM) can degrade the perceived video quality. (2) The commonly-used video QA measurements such as signal-to-noise ratio (SNR), peak-signal-to-noise ratio (PSNR) and mean squared error (MSE) [5], are computationally simply, however, they disregard the characteristics of human visual perception. (3) A lot of research interest has been focused on objective video QA [2,6], however, methods to assess the visual quality of digital video as perceived by human observer are becoming increasingly important, due to the large number of applications that target humans as the end users of video.…”

Section: Introductionmentioning

confidence: 99%

“…The only reliable method to assess the video quality is to ask human subjects for their own opinions, which is termed subjective video QA. The subjective methods are based on groups of trained or untrained users viewing the video content, and rating for quality [5,7]. It is impractical for most applications, and also time consuming, laborious and expensive, due to the human involvement in the process.…”

Section: Introductionmentioning

confidence: 99%

Quality Assessment of User-Generated Video Using Camera Motion

Guo

Gurrin

Hopfgartner

et al. 2013

Lecture Notes in Computer Science

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Abstract. With user-generated video (UGV) becoming so popular on the Web, the availability of a reliable quality assessment (QA) measure of UGV is necessary for improving the users' quality of experience in videobased application. In this paper, we explore QA of UGV based on how much irregular camera motion it contains with low-cost manner. A blockmatch based optical flow approach has been employed to extract camera motion features in UGV, based on which, irregular camera motion is calculated and automatic QA scores are given. Using a set of UGV clips from benchmarking datasets as a showcase, we observe that QA scores from the proposed automatic method and subjective method fit well. Further, the automatic method reports much better performance than the random run. These confirm the satisfaction of the automatic QA scores indicating the quality of the UGV when only considering visual camera motion. Furthermore, it also shows that the UGV quality can be assessed automatically for improving the end users quality of experience in video-based applications.

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Study of Subjective and Objective Quality Assessment of Video

Cited by 1,007 publications

References 25 publications

Computational Inference and Control of Quality in Multimedia Services

Computational Inference and Control of Quality in Multimedia Services

Interference Shaping for Improved Quality of Experience for Real-Time Video Streaming

Quality Assessment of User-Generated Video Using Camera Motion

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