No reference video quality assessment model based on eye tracking datas

Jia, Lixiu; Zhong, Xuefei; Tu, Yan

doi:10.2991/icieac-14.2014.22

Cited by 7 publications

(6 citation statements)

References 14 publications

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“…Unlike objective estimation, the subjective studies could yield valuable data to evaluate the performance of objective methods towards aiming the ultimate goal of matching human perception [22]. To this end, researches in [23] aim at a no-reference objective evaluation metric by utilizing eyetracker based contrast distortion information. The authors in [24] introduce a model to judge the video quality based on psychological merits such as electroencephalogram signaling, and pupil dilation.…”

Section: Introductionmentioning

confidence: 99%

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EMAN: The Human Visual Feature Based No-Reference Subjective Quality Metric

2019

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As the human vision is a definitive assessor of video quality, the expanded interest for no-reference subjective quality assessment (SQA) is focusing on a definitive goal of coordinating with human observation. However, the widely used subjective estimator-mean opinion score (MOS) is often biased by the testing environment, viewers mode, expertise, domain knowledge, and other factors which may influence on actual assessment. In this paper, a no-reference SQA metric is devised by simply exploiting the nature of human eye browsing on videos and analyzing the associated quality correlation features. The high efficiency video coding (HEVC) reference test model is first employed to produce different forms of coded video quality which then displayed to a number of partakers. Their eye-tracker recorded spatiotemporal gaze-data indicate more concentrated eye-traversing approach for relatively better quality. Thus, we calculate the quality assessment related to assorted features such as length pursuit, angle deflection, pupil deviation, and gaze interlude from recorded gaze trajectory. The content and resolution invariant operations are carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric-eye maneuver (EMAN). Tested results reveal that the quality evaluation carried out by the EMAN is comparatively better than MOS and structural similarity (SSIM) in terms of assessing different aspects of coded video quality for a wide range of single view video contents. For the free viewpoint video (FVV), where the reference frame is not available, the EMAN could also better distinguish different qualities compared to the MOS and SSIM.

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Section: Introductionmentioning

confidence: 99%

“…FIGURE23. The Excellent and Very-poor quality distinguishing capability of the PSNR, SSIM, EMAN, and MOS both on SVV and FVV.…”

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confidence: 98%

EMAN: The Human Visual Feature Based No-Reference Subjective Quality Metric

2019

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“…Thus, a number of quality assessment algorithms have been proposed which are closely related to the studies of human visual attention and cognition. The study in [17] proposed a no-reference model using blur and blockiness metric to improve the performance of objective model based on eye-tracker data. The authors in [18] introduced a model to judge the video quality on the basis of psychological merits including-the pupil dilation and electroencephalogram signalling.…”

Section: Introductionmentioning

confidence: 99%

A Novel No-reference Subjective Quality Metric for Free Viewpoint Video Using Human Eye Movement

Podder

Paul

Murshed

2018

Image and Video Technology

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The free viewpoint video (FVV) allows users to interactively control the viewpoint and generate new views of a dynamic scene from any 3D position for better 3D visual experience with depth perception. Multiview video coding exploits both texture and depth video information from various angles to encode a number of views to facilitate FVV. The usual practice for the single view or multiview quality assessment is characterized by evolving the objective quality assessment metrics due to their simplicity and real time applications such as the peak signal-to-noise ratio (PSNR) or the structural similarity index (SSIM). However, the PSNR or SSIM requires reference image for quality evaluation and could not be successfully employed in FVV as the new view in FVV does not have any reference view to compare with. Conversely, the widely used subjective estimator-mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain knowledge, and many other factors that may actively influence on actual assessment. To address this limitation, in this work, we therefore devise a no-reference subjective quality assessment metric by simply exploiting the pattern of human eye browsing on FVV. Over different quality contents of FVV, the participants' eye-tracker recorded spatio-temporal gazedata indicate more concentrated eye-traversing approach for relatively better quality. Thus, we calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the proposed QMET performs better than the SSIM and MOS in terms of assessing different aspects of coded video quality for a wide range of FVV contents.

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“…To this end, a number of quality assessment algorithms have been proposed which are closely related to the studies of human visual attention and cognition. Jia et al [16] propose a no-reference model using blur and blockiness metric to improve the performance of objective model based on eye-tracker data. The authors in [17] introduce a model to judge the video quality on the basis of psychological merits including-the pupil dilation and electroencephalogram signalling.…”

Section: Introductionmentioning

confidence: 99%

A Novel Quality Metric Using Spatiotemporal Correlational Data of Human Eye Maneuver

Podder¹,

Paul²,

Murshed

2017

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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The popularly used subjective estimator-mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain expertise, and many other factors that may actively influence on actual assessment. We therefore, devise a noreference subjective quality assessment metric by exploiting the nature of human eye browsing on videos. The participants' eyetracker recorded gaze-data indicate more concentrated eyetraversing approach for relatively better quality. We calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the quality evaluation carried out by QMET demonstrates a strong correlation with the most widely used peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and the MOS.

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No reference video quality assessment model based on eye tracking datas

Cited by 7 publications

References 14 publications

EMAN: The Human Visual Feature Based No-Reference Subjective Quality Metric

EMAN: The Human Visual Feature Based No-Reference Subjective Quality Metric

A Novel No-reference Subjective Quality Metric for Free Viewpoint Video Using Human Eye Movement

A Novel Quality Metric Using Spatiotemporal Correlational Data of Human Eye Maneuver

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