A novel objective quality assessment method for perceptual video coding in conversational scenarios

Xu, Mai; Zhang, Jingze; Ma, Yuan; Wang, Zulin

doi:10.1109/vcip.2014.7051496

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…To calculate the Q-score (i.e. the pre-processed score of the QMET) for L, A, P, and T, the equations (1)- (4) have been employed respectively where Q1, Q2, Q3, and Q4 denote the Qscore for individual L, A, P, and T respectively which is better illustrated in Fig. 4.…”

Section: B Correlation Analysis Of Featuresmentioning

confidence: 99%

“…Usually the quality estimation is performed in two ways: objective and subjective where the former one is more widely used due to its simplicity, ease of use and having real-time applications. Thus, a good number of citable researches have been conducted based on the objective image quality estimation [4]- [7]. The quality estimation could be mainly categorized into full-reference (i.e.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: B Correlation Analysis Of Featuresmentioning

confidence: 99%

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)

View full text Add to dashboard Cite

show abstract

“…Usually the quality estimation is performed in two ways: objective and subjective, where the former one is more widely used due to its simplicity, ease of use and having real-time applications. Thus, a good number of citable researches have been conducted based on the objective image quality estimation [6]- [8]. The quality estimation could be further categorized into full-reference (i.e.…”

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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A Novel Objective Quality Assessment Method for Perceptually-Coded Cloud Gaming Video

Sabet

Hashemi

Shirmohammadi

et al. 2018

2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)

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A novel objective quality assessment method for perceptual video coding in conversational scenarios

Cited by 3 publications

References 15 publications

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

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

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

A Novel Objective Quality Assessment Method for Perceptually-Coded Cloud Gaming Video

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