NR objective continuous video quality assessment model based on frame quality measure

Kawayoke, Yoshikazu; Horita, Yuukou

doi:10.1109/icip.2008.4711772

Cited by 23 publications

(17 citation statements)

References 6 publications

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“…Kawayoke and Horita's metric [8] estimates quality by a Sobel spatial measurement operator on individual MPEG-2 digital frames first, then adjusts the frame quality using the information from spatial and temporal information respectively. The proposed metric focuses on monitoring the quality of service (QoS) for visual communications.…”

Section: ____________________________________________________________mentioning

confidence: 99%

Evaluation infrastructure for the measurement of content-based video quality and video analytics performance

Guan¹,

Zhou²,

Fiscus³

et al. 2017

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Section: ____________________________________________________________mentioning

confidence: 99%

Evaluation infrastructure for the measurement of content-based video quality and video analytics performance

Guan¹,

Zhou²,

Fiscus³

et al. 2017

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“…Similarly, most pixel-based video quality metrics are still full-reference or reducedreference metrics and only a small subset consists of no-reference metrics exits [57,58,70,135,224,370]. Note that there is no standardised pixel-based no-reference video quality metrics available, yet.…”

Section: Summary Of the State-of-the-artmentioning

confidence: 99%

“…Only a small subset of all reviewed metrics consider the temporal dimension of video without any significant temporal pooling or at least consider relatively large interval of a few seconds [11,135,172,203,220,332], but two of these metrics aim at providing continuous quality predictions and therefore provide no overall quality prediction [172,203].…”

Section: Summary Of the State-of-the-artmentioning

confidence: 99%

“…Compared to the overall number of technology-agnostic video quality metrics in the-state-of-the-art, only a small subset fulfils this requirement [57,58,70,135,224,370], in contrast to coding technology-specific metrics that in the majority fulfil this requirement [23-25, 178, 313, 314].…”

Section: Validationmentioning

confidence: 99%

“…Only a small subset of all metrics consider the temporal dimension of video without any significant temporal pooling or at least consider a relatively large interval of a few seconds [11,135,172,203,220,332].…”

Section: Motivation and Problem Statementmentioning

confidence: 99%

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Design of Video Quality Metrics with Multi-Way Data Analysis

Keimel¹

2016

T-Labs Series in Telecommunication Services

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Video quality metrics determine the visual quality of distorted video sequences by using prediction models based on objectively measurable features and are therefore an alternative to the time-consuming and costly subjective video quality assessment. In the conventional design approach to video quality metrics, however, the temporal nature of video is often considered only inadequately due to the use of temporal pooling in the prediction process. Moreover, this approach also often requires knowledge about the human visual system that is not readily or only partly available. In this thesis, I therefore propose a data driven design methodology using multi-way data analysis for the design of video quality metrics. This data driven design approach not only requires no detailed knowledge of the human visual system, but also allows for a proper consideration of the temporal nature of video by using a three-way prediction model, corresponding to the three-way structure of video. Using two simple example metrics, I demonstrate that this purely data driven approach not only outperforms video quality metrics in the state-of-the-art that are often highly optimised towards specific properties of the human visual system, but also that multi-way data analysis methods outperform the combination of two-way data analysis methods and temporal pooling.iii

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