QMET: A new quality assessment metric for no-reference video coding by using human eye traversal

Pallab Kanti Podder, Manoranjan Paul, Manzur Murshed

Research output: Book chapter/Published conference paperConference paper

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Abstract

The subjective quality assessment (SQA) is an ever demanding approach due to its in-depth interactivity to the human cognition. The addition of no-reference based scheme could equip the SQA techniques to tackle further challenges. Existing widely used objective metrics-peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) or the subjective estimator-mean opinion score (MOS) requires original image for quality evaluation that limits their uses for the situation having no-reference. In this work, we present a no-reference based SQA technique that could be an impressive substitute to the reference-based approaches for quality evaluation. The High Efficiency Video Coding (HEVC) reference test model (HM15.0) is first exploited to generate five different qualities of the HEVC recommended eight class sequences. To assess different aspects of coded video quality, a group of ten participants are employed and their eye-tracker (ET) recorded data demonstrate closer correlation among gaze plots for relatively better quality video contents. Therefore, we innovatively calculate the amount of approximation of smooth eye traversal (ASET) by using distance, angle, and pupil-size feature from recorded gaze trajectory data and develop a new-quality metric based on eye traversal (QMET). Experimental results show that the quality evaluation carried out by QMET is highly correlated to the HM recommended coding quality. The performance of the QMET is also compared with the PSNR and SSIM metrics to justify the effectiveness of each other.
Original languageEnglish
Title of host publicationProceedings of the 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ)
EditorsDonald Bailey, Gourab Sen Gupta, Stephen Marsland
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-6
Number of pages6
ISBN (Electronic)9781509027484
DOIs
Publication statusPublished - 05 Jan 2017
Event2016 International Conference on Image and Vision Computing New Zealand: IVCNZ - Massey University, Palmerston North, New Zealand
Duration: 21 Nov 201622 Nov 2016

Conference

Conference2016 International Conference on Image and Vision Computing New Zealand
CountryNew Zealand
CityPalmerston North
Period21/11/1622/11/16

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    Podder, P. K., Paul, M., & Murshed, M. (2017). QMET: A new quality assessment metric for no-reference video coding by using human eye traversal. In D. Bailey, G. S. Gupta, & S. Marsland (Eds.), Proceedings of the 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ) (pp. 1-6). [7804439] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IVCNZ.2016.7804439