80‐2: Spatiochromatic Model for Image‐Quality Prediction of High‐Dynamic‐Range and Wide‐Color‐Gamut Content

Wanat, Robert; Choudhury, Anustup; Daly, Scott

doi:10.1002/sdtp.14092

Cited by 3 publications

(11 citation statements)

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“…The range between 3 and 4 log 10 units is a transition between SDR and HDR, and may sometimes be marketed as HDR. [1][2][3] There are also qualitative aspects of HDR that are fundamentally different than SDR. Physical analysis of imagery has found that specular reflections can have larger luminance ranges than diffusely reflective objects 4 which were the primary purview of SDR.…”

Section: Unique Aspects Of Hdrmentioning

confidence: 99%

“…We presented preliminary versions of the spatial extension of ΔE ITP in our previous work. 2,3 In this article, we present a more detailed analysis and investigation that gives a better understanding of the spatial metrics.…”

Section: Contributionsmentioning

confidence: 99%

“…2 An equivalent unit of orders of magnitude (OoM) is also commonly used to express dynamic range in literature. 3 We assume 2 orders of magnitude of luminance, from 0.1 to 0.001 cd/m 2 , represent shadow detail and thus appear as shades of black. This corresponds to 6.7 stops.…”

Section: Data Availability Statementmentioning

confidence: 99%

“…High dynamic range (HDR) and wide color gamut (WCG) 1‐3 video imagery is now supported by several consumer ecosystems and is available on millions of playback devices ranging from smartphones to cinema projectors. Image capture, video processing algorithm design, system format development, display design, and comparison across products all require evaluation of the quality of HDR images in a perceptually relevant manner.…”

Section: Introductionmentioning

confidence: 99%

“…In the computer graphics field, the term low dynamic range (LDR) is used synonymously with SDR, but the display industry reserves the term LDR to refer to reflective displays which are always below 2 log 10 units, and usually only slightly higher than 1 log 10 unit in luminance range. The range between 3 and 4 log 10 units is a transition between SDR and HDR, and may sometimes be marketed as HDR 1‐3 …”

Section: Introductionmentioning

confidence: 99%

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Image quality evaluation for high dynamic range and wide color gamut applications using visual spatial processing of color differences

Choudhury

Wanat

Pytlarz

et al. 2020

Color Research & Application

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High dynamic range (HDR) and wide color gamut imagery has an established video ecosystem, spanning image capture to encoding and display. This drives the need for evaluating how image quality is affected by the multitudes of ecosystem parameters. The simplest quality metrics evaluate color differences on a pixel-bypixel basis. In this article, we evaluate a series of these color difference metrics on four HDR and three standard dynamic range publicly available distortion databases consisting of natural images and subjective scores. We compare the performance of the well-established CIE L*a*b* metrics (ΔE 00 , ΔE 94) alongside two HDR-specific metrics (ΔE Z [J z a z b z ], ΔE ITP [IC T C P ]) and a spatial CIE L*a*b* extension (ΔE S 00). We also present a novel spatial extension to ΔE ITP derived by optimizing the opponent color contrast sensitivity functions. We observe that this advanced metric, ΔE SC ITP , outperforms the other color difference metrics, and we quantify the improved performance with the steps of metric advancement.

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Section: Unique Aspects Of Hdrmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Data Availability Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Image quality evaluation for high dynamic range and wide color gamut applications using visual spatial processing of color differences

Choudhury

Wanat

Pytlarz

et al. 2020

Color Research & Application

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Riemannian color difference metric for spatial sinusoidal color variations

Candry,

De Visschere,

Neyts

2024

Opt. Express

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Several studies report on the sensitivity of human vision to static spatial sinusoidal achromatic and chromatic contrast variations. However, a Riemannian color difference metric, which includes the spatial and colorimetric properties of sinusoidal gratings, is lacking. Such a metric is important for various applications. Here we report on the development of a new Riemannian metric, for the prediction of detection ellipsoids in color space, for spatial sinusoidal gratings as a function of the grating’s size, spatial frequency, luminance and chromaticity. The metric is based on measurements and models of achromatic and isoluminous chromatic contrast sensitivity functions available in literature, and the Riemannian metric for split fields which we reported earlier. We find adequate agreement with various data sets of experimental achromatic and isoluminous chromatic contrast sensitivity functions and with experimentally determined threshold ellipses of isoluminous chromatic Gabor gratings.

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HDR and WCG Image Quality Assessment Using Color Difference Metrics

Choudhury¹,

Pytlarz²,

Daly³

2020

SMPTE Mot. Imag. J

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High dynamic range (HDR) and wide color gamut (WCG) imagery is now mainstream across content creation. Thus, having a reliable way of evaluating HDR systems is essential. One common way of measuring the quality of an HDR system is measuring the color errors introduced along the imaging pipeline. Unfortunately, development of color difference metrics has been based on test patches, as opposed to natural imagery, with evaluation mostly limited to standard dynamic range (SDR) databases. In this paper, we evaluate several color difference metrics on five publicly available HDR distortion databases consisting of natural images and subjective scores. Distortion types include lower frequency distortions such as those from tone mapping as well as higher-frequency distortions resulting from compression artifacts by the various compression schemes such as Joint Photographic Experts Group (JPEG). We analyze these databases with the well-established CIE L*a*b* metrics (ΔE 00 , ΔE 94 ) as well as two HDR-specific metrics: ΔE z (J z a z b z ) and ΔE ITP (IC T C P ). To quantify the performance, we use four standard performance evaluation procedures. We observe that ΔE ITP outperforms the other color difference metrics on four out of five databases. We also perform statistical analysis, which demonstrates the effectiveness of using ΔE ITP to assess the overall image quality of HDR/WCG content. Weaknesses of all metrics for one database suggest that more advanced spatio-chromatic visual models are worth pursuing.

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80‐2: Spatiochromatic Model for Image‐Quality Prediction of High‐Dynamic‐Range and Wide‐Color‐Gamut Content

Cited by 3 publications

References 14 publications

Image quality evaluation for high dynamic range and wide color gamut applications using visual spatial processing of color differences

Image quality evaluation for high dynamic range and wide color gamut applications using visual spatial processing of color differences

Riemannian color difference metric for spatial sinusoidal color variations

HDR and WCG Image Quality Assessment Using Color Difference Metrics

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