Balancing Attended and Global Stimuli in Perceived Video Quality Assessment

You, Junyong; Korhonen, Jari; Perkis, Andrew; Ebrahimi, Touradj

doi:10.1109/tmm.2011.2172591

Cited by 28 publications

(19 citation statements)

References 46 publications

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“…[43][44][45][46][47] There are also mechanisms available that can objectively estimate the ROI. [48][49][50][51][52] It is therefore already possible to implement the functionality that would optimize the encryption of different regions of images while predicting how it will affect the overall quality.…”

Section: Discussionmentioning

confidence: 99%

Studying the effect of optimizing image quality in salient regions at the expense of background content

Alers

Redi

Liu

et al. 2013

J. Electron. Imaging

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. (2013). Studying the effect of optimizing image quality in salient regions at the expense of background content. Journal of Electronic Imaging, 22(4), 043012-1/10. [043012]. DOI: 10.1117/1.JEI.22.4.043012 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 ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Abstract. Manufacturers of commercial display devices continuously try to improve the perceived image quality of their products. By applying postprocessing techniques on the incoming signal, they aim to enhance the quality level perceived by the viewer. These postprocessing techniques are usually applied globally over the whole image but may cause side effects, the visibility and annoyance of which differ with local content characteristics. To better understand and utilize this, a three-phase experiment was conducted where observers were asked to score images that had different levels of quality in their regions of interest and in the background areas. The results show that the region of interest has a greater effect on the overall quality of the image than the background. This effect increases with the increasing quality difference between the two regions. Based on the subjective data we propose a model to predict the overall quality of images with different quality levels in different regions. This model, which is constructed on empirical bases, can help craft weighted objective metrics that can better approximate subjective quality scores.

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Section: Discussionmentioning

confidence: 99%

Studying the effect of optimizing image quality in salient regions at the expense of background content

Alers

Redi

Liu

et al. 2013

J. Electron. Imaging

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“…Temporal nature of video Many video quality metrics account for the temporal nature of video by using pooling approaches as the Minkowski summation [21,58,183,343,349,353,354] or percentile pooling [27,116,190], while others include frames in a small interval around the current frame into the prediction [21,183,282] or use the differences to the preceding frame in the prediction [78,212,224,227,228,232,277,341,370,373]. 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%

“…Validation of visual quality metrics is mostly limited to a single data set and only a small number of quality metrics are validated with multiple datasets [218,232,279,302,340,373], but none of the video quality metrics are.…”

Section: Validationmentioning

confidence: 99%

See 1 more Smart Citation

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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“…It was shown that 40 visual artifacts are likely more annoying in a salient region attended by viewers than those in other areas by Ninassi et al [10]. You et al [11] proposed a quality metric based on balancing the influence of the entire visual stimuli and attended stimuli on the perceived video quality. Video compression can also exploit different visual sensitivity of humans for attended and unattended regions, which is referred to as perceptual 45 video compression [12].…”

Section: Introductionmentioning

confidence: 99%