Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos

Seshadrinathan, Kalpana; Bovik, Alan C.

doi:10.1109/tip.2009.2034992

Cited by 645 publications

(408 citation statements)

References 74 publications

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“…We also evaluated two FR VQA algorithms -Visual Quality Metric (VQM) [11] and the MOtion-based Video Integrity Evaluation (MOVIE) index [12]. VQM was obtained from [44] while MOVIE is freely available at [45].…”

Section: Evaluation Of Algorithm Performancementioning

confidence: 99%

Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies

Moorthy

Choi

Bovik

et al. 2012

IEEE J. Sel. Top. Signal Process.

270

168

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Abstract-We introduce a new video quality database that models video distortions in heavily-trafficked wireless networks and that contains measurements of human subjective impressions of the quality of videos. The new LIVE Mobile Video Quality Assessment (VQA) database consists of 200 distorted videos created from 10 RAW HD reference videos, obtained using a RED ONE digital cinematographic camera. While the LIVE Mobile VQA database includes distortions that have been previously studied such as compression and wireless packet-loss, it also incorporates dynamically varying distortions that change as a function of time, such as frame-freezes and temporally varying compression rates. In this article, we describe the construction of the database and detail the human study that was performed on mobile phones and tablets in order to gauge the human perception of quality on mobile devices. The subjective study portion of the database includes both the differential mean opinion scores (DMOS) computed from the ratings that the subjects provided at the end of each video clip, as well as the continuous temporal scores that the subjects recorded as they viewed the video. The study involved over 50 subjects and resulted in 5,300 summary subjective scores and time-sampled subjective traces of quality. In the behavioral portion of the article we analyze human opinion using statistical techniques, and also study a variety of models of temporal pooling that may reflect strategies that the subjects used to make the final decision on video quality. Further, we compare the quality ratings obtained from the tablet and the mobile phone studies in order to study the impact of these different display modes on quality. We also evaluate several objective image and video quality assessment (IQA/VQA) algorithms with regards to their efficacy in predicting visual quality. A detailed correlation analysis and statistical hypothesis testing is carried out. Our general conclusion is that existing VQA algorithms are not well-equipped to handle distortions that vary over time. The LIVE Mobile VQA database, along with the subject DMOS and the continuous temporal scores is being made available to researchers in the field of VQA at no cost in order to further research in the area of video quality assessment. Index Terms-Mobile video quality, objective algorithm evaluations, subjective quality, video quality assessment, video quality database.

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Section: Evaluation Of Algorithm Performancementioning

confidence: 99%

Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies

Moorthy

Choi

Bovik

et al. 2012

IEEE J. Sel. Top. Signal Process.

270

168

View full text Add to dashboard Cite

show abstract

“…The MS-SSIM index performs better (relative to human opinion) than the SS-SSIM index on images. The computation formula of MS-SSIM is as (4)    Motion-based Video Integrity Evaluation (MOIVE) [18] intergrates both spatial and temporal aspects of distortion assessment. MOIVE explicitly uses motion information from the reference video and evaluates the quality of the test video along the motion trajectories of the reference video.…”

Section: Resultsmentioning

confidence: 99%

A Study of FR Video Quality Assessment of Real Time Video Stream

Zhang¹,

Wu²,

Fang³

et al. 2012

IJACSA

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Abstract-To assess the real-time transmission video's quality, this paper persents a approach which used FR video quality assessment (VQA) model to satisfy the objective and subjective measurement requirement. If we want to get the reference video in the measuring terminal and to make a assessment, there are two problems which are how to certain the reference video frame and how to make the objective score close to the subject assessment. We present in this paper a novel method of computing the order number of the video frame in the test point. In order to establish the relationship between the objective distortion and the subjective score, we used the "best-fit" regressed curve model and the BP neural network to describe prediction formula. This work is the mainly aim to get the high accurency assessment results with the human subjective feeling. So we select huge video sources for testing and training. The experimental results show that the proposed approach is suit to assess the video quality using FR model and the converted subjective score is available.Keywords-video quailty assessment (VQA); full reference; objective performance; subjective score; BP neural network.

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“…Figures 5 and 6 illustrate the restoration quality in terms of SNR and MOVIE, for the different regularization approaches. The MOVIE indicator, introduced in [43], is a perceptual measure mimicking the human subjective judgment to assess the visual quality of a natural video, the smaller value MOVIE is, the better the video restoration result is.…”

Section: Non-blind Video Deconvolution Stepmentioning

confidence: 99%

An alternating proximal approach for blind video deconvolution

Abboud¹,

Chouzenoux

Pesquet

et al. 2019

Signal Processing: Image Communication

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Blurring occurs frequently in video sequences captured by consumer devices, as a result of various factors such as lens aberrations, defocus, relative camerascene motion, and camera shake. When it comes to the contents of archive documents such as old films and television shows, the degradations are even more serious due to several physical phenomena happening during the sensing, transmission, recording, and storing processes. We propose in this paper a versatile formulation of blind video deconvolution problems that seeks to estimate both the sharp unknown video sequence and the underlying blur kernel from an observed video. This inverse problem is ill-posed, and an appropriate solution can be obtained by modeling it as a nonconvex minimization problem. We provide a novel iterative algorithm to solve it, grounded on the use of recent advances in convex and nonconvex optimization techniques, and having the ability of including numerous well-known regularization strategies.

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Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos

Cited by 645 publications

References 74 publications

Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies

Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies

A Study of FR Video Quality Assessment of Real Time Video Stream

An alternating proximal approach for blind video deconvolution

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