HDR-VDP-2.2: a calibrated method for objective quality prediction of high-dynamic range and standard images

Narwaria, Manish; Mantiuk, Rafał; Silva, Mattheiu Perreira Da; Callet, Patrick Le

doi:10.1117/1.jei.24.1.010501

Cited by 209 publications

(140 citation statements)

References 7 publications

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“…To the best of our knowledge, there are only two metrics for HDR quality assessment that have a publicly available implementation: (1) HDR-VDP: high dynamic range visible difference predictor [10,12,13] and (2) HDR-VQM: an objective quality measure for high dynamic range video [15]. The original HDR-VDP metric [10] was the first metric designed for HDR content.…”

Section: Full-reference Metricsmentioning

confidence: 99%

“…The most relevant work on this domain is the HDR visual detection predictor (HDR-VDP) metric proposed by Mantiuk et al [10], which is an extension of Daly's VDP [11] for the HDR domain. The second version of this metric, HDR-VDP-2 [12,13], is considered as the state-of-the-art in HDR image quality assessment. The dynamic range independent metric (DRIM) proposed in [14] can also be used for HDR quality assessment.…”

Section: Introductionmentioning

confidence: 99%

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Benchmarking of objective quality metrics for HDR image quality assessment

Hanhart

Bernardo

Pereira

et al. 2015

J Image Video Proc.

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Recent advances in high dynamic range (HDR) capture and display technologies have attracted a lot of interest from scientific, professional, and artistic communities. As in any technology, the evaluation of HDR systems in terms of quality of experience is essential. Subjective evaluations are time consuming and expensive, and thus objective quality assessment tools are needed as well. In this paper, we report and analyze the results of an extensive benchmarking of objective quality metrics for HDR image quality assessment. In total, 35 objective metrics were benchmarked on a database of 20 HDR contents encoded with 3 compression algorithms at 4 bit rates, leading to a total of 240 compressed HDR images, using subjective quality scores as ground truth. Performance indexes were computed to assess the accuracy, monotonicity, and consistency of the metric estimation of subjective scores. Statistical analysis was performed on the performance indexes to discriminate small differences between metrics. Results demonstrated that metrics designed for HDR content, i.e., HDR-VDP-2 and HDR-VQM, are the most reliable predictors of perceived quality. Finally, our findings suggested that the performance of most full-reference metrics can be improved by considering non-linearities of the human visual system, while further efforts are necessary to improve performance of no-reference quality metrics for HDR content.

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Section: Full-reference Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Benchmarking of objective quality metrics for HDR image quality assessment

Hanhart

Bernardo

Pereira

et al. 2015

J Image Video Proc.

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“…The Perceptually Uniform (PU) encoding [37] can be used to modify traditional metrics such as PSNR and SSIM [38] to be more amiable to HDR video quality validation. Metrics designed directly for HDR, for example, the High Dynamic Range-Visible Difference Predictor (HDR-VDP) [39], and others designed directly for HDR video such as High Dynamic Range-Visual Quality Metric (HDR-VQM) [40] and Dynamic Range Independent Visual Quality Metric (DRI-VQM) [41] perform well also and, as with the PU-modified metrics, provide a good objective measure of the quality of HDR video [22].…”

Section: Quality Metricsmentioning

confidence: 99%

HDR video past, present and future: A perspective

Chalmers

Debattista

2017

Signal Processing: Image Communication

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High Dynamic Range (HDR) video has emerged from research labs around the world and entered the realm of consumer electronics. The dynamic range that a human can see in a scene with minimal eye adaption (approximately 1,000,000 : 1) is vastly greater than traditional imaging technology which can only capture about 8 f-stops (256 : 1). HDR technology, on the other hand, has the potential to capture the full range of light in a scene; even more than a human eye can see. This paper examines the field of HDR video from capture to display; past, present and future. In particular the paper looks beyond the current marketing hype around HDR, to show how HDR video in the future can and, indeed, should bring about a step change in imaging, analogous to the change from black and white to colour.

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“…Existing LDR QA metrics such as PSNR, SSIM [46] and VIF [40] have been extended to handle HDR values using Perceptually Uniform (PU) encoding [3]. Recently, a few full-reference HDR QA/VQA metrics have been proposed such as High Dynamic Range-Visible Difference Predictor (HDR-VDP) [28,34], High Dynamic Range-Visual Quality Metric (HDR-VQM) [36] and Dynamic Range Independent-Visual Quality Metric (DRI-VQM) [2].…”

Section: Related Workmentioning

confidence: 99%

“…This takes several aspects of the human visual system into account such as a wide range of viewing conditions, intra-ocular light scatter, photo-receptor spectral sensitivities and contrast sensitivity across the full range of visible luminance. This evaluation uses the Q correlate of version 2.2.1 [34].…”

Section: Quality Assessment (Qa) Metricsmentioning

confidence: 99%

Objective and subjective evaluation of High Dynamic Range video compression

Mukherjee

Debattista

Bashford-Rogers

et al. 2016

Signal Processing: Image Communication

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Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP URL' above for details on accessing the published version and note that access may require a subscription. AbstractA number of High Dynamic Range (HDR) video compression algorithms proposed to date have either been developed in isolation or only-partially compared with each other. Previous evaluations were conducted using quality assessment error metrics, which for the most part were developed for qualitative assessment of Low Dynamic Range (LDR) videos. This paper presents a comprehensive objective and subjective evaluation conducted with six published HDR video compression algorithms. The objective evaluation was undertaken on a large set of 39 HDR video sequences using seven numerical error metrics namely: PSNR, logPSNR, puPSNR, puSSIM, Weber MSE, HDR-VDP and HDR-VQM. The subjective evaluation involved six short-listed sequences and two ranking-based subjective experiments with hidden reference at two different output bitrates with 32 participants each, who were tasked to rank distorted HDR video footage compared to an uncompressed version of the same footage. Results suggest a strong correlation between the objective and subjective evaluation. Also, non-backward compatible compression algorithms appear to perform better at lower output bit rates than backward compatible algorithms across the settings used in this evaluation.

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HDR-VDP-2.2: a calibrated method for objective quality prediction of high-dynamic range and standard images

Cited by 209 publications

References 7 publications

Benchmarking of objective quality metrics for HDR image quality assessment

Benchmarking of objective quality metrics for HDR image quality assessment

HDR video past, present and future: A perspective

Objective and subjective evaluation of High Dynamic Range video compression

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