Eye Movements on Assessing Perceptual Image Quality

Tsai, Cheng-Min; Guan, Shing–Sheng; Tsai, Wang-Chin

doi:10.1007/978-3-319-39943-0_37

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…Research methodology in [25] use the eyeinformation based elective process to create a no-reference quality assessment outline. They experimentally prove that for inferior quality contents, partakers consume more time for quality evaluation which is opposite to the approach introduced by Tsai et al [26]. We also observe similar trend in [25] mostly for the still images which however, becomes impractical for videos as the frames move continually.…”

Section: Introductionsupporting

confidence: 71%

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: Introductionsupporting

confidence: 71%

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

2019

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“…Albanesi et al [19] used the eye-gaze data to create a voting algorithm to develop a noreference method. Using the scan path of eye movements, Tsai et al [20] subjectively assessed the perceived image and its colour quality. Conversely, the widely used subjective testing method-MOS [21][22] is often biased by the testing environment, viewers mode, expertise, domain knowledge, age range, and many other factors which may undesirably influence the effectiveness of actual quality assessment process.…”

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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“…Albanesi et al [18] use the eye data to create a voting algorithm to develop a no-reference method. Using the scan path of eye movements, Tsai et al [19] subjectively assess the perceived image and its colour quality. Tested results prove that percipients tend to spend more time in evaluating the image with relatively improved quality.…”

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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Eye Movements on Assessing Perceptual Image Quality

Cited by 7 publications

References 9 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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