Video quality assessment using a statistical model of human visual speed perception

Wang, Zhou; Li, Qiang

doi:10.1364/josaa.24.000b61

Cited by 186 publications

(120 citation statements)

References 27 publications

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“…Furthermore, we would like to incorporate the visual attention model using motion information to SV-CIELAB. Information of visual attention is effective in image quality assessment [7], [8], [24]. In particular, motion information plays an important role in predicting visual attention [25].…”

Section: Discussionmentioning

confidence: 99%

“…Since images are perceived through the human visual system, objective IQA or VQA methods should be designed by incorporating human visual characteristics [1]. Based on this observation, Wang et al proposed structural similarity (SSIM) [6]- [8]. Though SSIM is an IQA method based on the assumption of human visual perception, it does not contain exact human visual characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Video Quality Assessment Using Spatio-Velocity Contrast Sensitivity Function

Hirai

Tumurtogoo

Kikuchi

et al. 2010

IEICE Trans. Inf. & Syst.

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SUMMARYDue to the development and popularization of highdefinition televisions, digital video cameras, Blu-ray discs, digital broadcasting, IP television and so on, it plays an important role to identify and quantify video quality degradations. In this paper, we propose SV-CIELAB which is an objective video quality assessment (VQA) method using a spatio-velocity contrast sensitivity function (SV-CSF). In SV-CIELAB, motion information in videos is effectively utilized for filtering unnecessary information in the spatial frequency domain. As the filter to apply videos, we used the SV-CSF. It is a modulation transfer function of the human visual system, and consists of the relationship among contrast sensitivities, spatial frequencies and velocities of perceived stimuli. In the filtering process, the SV-CSF cannot be directly applied in the spatial frequency domain because spatial coordinate information is required when using velocity information. For filtering by the SV-CSF, we obtain video frames separated in spatial frequency domain. By using velocity information, the separated frames with limited spatial frequencies are weighted by contrast sensitivities in the SV-CSF model. In SV-CIELAB, the criteria are obtained by calculating image differences between filtered original and distorted videos. For the validation of SV-CIELAB, subjective evaluation experiments were conducted. The subjective experimental results were compared with SV-CIELAB and the conventional VQA methods such as CIELAB color difference, Spatial-CIELAB, signal to noise ratio and so on. From the experimental results, it was shown that SV-CIELAB is a more efficient VQA method than the conventional methods. key words: objective video quality assessment, spatio-velocity contrast sensitivity function, image diffrence, subjective evaluation, rank order correlation coefficient

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Video Quality Assessment Using Spatio-Velocity Contrast Sensitivity Function

Hirai

Tumurtogoo

Kikuchi

et al. 2010

IEICE Trans. Inf. & Syst.

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“…The Video Quality Experts Group 5 (VQEG) Phase II validation tests show that VQM has a high correlation with subjective video quality scores and as a result it has been adopted by ANSI (ANSI T1.801.03-2003), and by ITU-T (ITU-T J.144, and ITU-R BT.1683) along with three other metrics as standard for measuring video quality. -Structural Similarity Index [19] (SSIM) uses a structural distortion based measurement approach. Structure and similarity in this context refer to samples of the signals having strong dependencies between each other, especially when they are close in space [20].…”

Section: Visual Qualitymentioning

confidence: 99%

Techniques for Measuring Quality of Experience

Kuipers

Kooij

Vleeschauwer³

et al. 2010

Lecture Notes in Computer Science

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Abstract. Quality of Experience (QoE) relates to how users perceive the quality of an application. To capture such a subjective measure, either by subjective tests or via objective tools, is an art on its own. Given the importance of measuring users' satisfaction to service providers, research on QoE took flight in recent years. In this paper we present an overview of various techniques for measuring QoE, thereby mostly focusing on freely available tools and methodologies.

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“…Ref. [4]- [6] give us some new algorithms based on SSIM to product different "structure" to estimate the video quality.…”

Section: Introductionmentioning

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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Video quality assessment using a statistical model of human visual speed perception

Cited by 186 publications

References 27 publications

Video Quality Assessment Using Spatio-Velocity Contrast Sensitivity Function

Video Quality Assessment Using Spatio-Velocity Contrast Sensitivity Function

Techniques for Measuring Quality of Experience

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

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