Progressive histogram reshaping for creative color transfer and tone reproduction

Pouli, Tania; Reinhard, Erik

doi:10.1145/1809939.1809949

Cited by 61 publications

(56 citation statements)

References 25 publications

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“…In the experiments which follow, we call our method-3D homography color transform + mean intensity mapping-"3D-H." Similarly, the 2D homography approach for color transfer re-coding [11] is denoted as "2D-H." We first show some visual results of color transfer approximations of [16,20,21,23] in Fig. 4.…”

Section: Resultsmentioning

confidence: 99%

“…By adding a gradient preservation constraint, these artifacts can be mitigated or removed at the cost of more blurry artifacts [20]. Pouli and Reinhard [21] adopted a progressive histogram matching in L*a*b* color space. Their method generates image histograms at different scales.…”

Section: Photo-realistic Color Transfermentioning

confidence: 99%

“…Their method proposed that the mean and variance of the source image, in a specially chosen color space, should be manipulated to B Han Gong h.gong@uea.ac.uk, gong@fedoraproject.org 1 School of Computing Sciences, University of East Anglia, Norwich, UK 2 School of Informatics, University of Edinburgh, Edinburgh, UK match those of a target. More recent methods [1,16,[19][20][21] might adopt more aggressive color transfers-e.g., color distribution force matches [19,20]-and yet, these aggressive changes often do not preserve the original intensity gradients and new spatial type artifacts may be introduced into an image (e.g., JPEG blocks become visible or there is false contouring). In addition, the complexity of a color transfer method usually leads to longer processing time.…”

Section: Introductionmentioning

confidence: 99%

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3D color homography model for photo-realistic color transfer re-coding

et al. 2017

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Color transfer is an image editing process that naturally transfers the color theme of a source image to a target image. In this paper, we propose a 3D color homography model which approximates photo-realistic color transfer algorithm as a combination of a 3D perspective transform and a mean intensity mapping. A key advantage of our approach is that the re-coded color transfer algorithm is simple and accurate. Our evaluation demonstrates that our 3D color homography model delivers leading color transfer re-coding performance. In addition, we also show that our 3D color homography model can be applied to color transfer artifact fixing, complex color transfer acceleration, and color-robust image stitching.

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Section: Resultsmentioning

confidence: 99%

Section: Photo-realistic Color Transfermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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3D color homography model for photo-realistic color transfer re-coding

et al. 2017

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“…For example, σ in Equation 2.1 can be formulated using the image mean or median. However, for an improved distribution of tone-mapped values the shape of the tone-curve can be controlled by means of the image histogram [69,170,203,264], similarly as in histogram equalization. For the TMO presented in Paper C [77], we use the image histogram in order to minimize the differences in contrasts between input and tone-mapped images.…”

Section: Tone-curvementioning

confidence: 99%

The high dynamic range imaging pipeline : Tone-mapping, distribution, and single-exposure reconstruction

Eilertsen¹

2018

Linköping Studies in Science and Technology. Dissertations

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“…Since this task is beyond the ability of today's algorithms, most techniques simply shift the appearance of the foreground to better resemble the background. For example, color transfer techniques [Reinhard et al 2001;Reinhard et al 2004;Pouli and Reinhard 2010] align the means and variances of the color histograms of the two image regions. Adobe Photoshop's widely used "Match Color" feature is based on this idea.…”

Section: Related Workmentioning

confidence: 99%

Understanding and improving the realism of image composites

Xue

Agarwala

Dorsey

et al. 2012

TOG

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Figure 1: Our method can automatically adjust the appearance of a foreground region to better match the background of a composite. Given the proposed foreground and background on the left, we show the compositing results of unadjusted cut-and-paste, Adobe Photoshop's Match Color, the method of Lalonde and Efros [2007], and our method. AbstractCompositing is one of the most commonly performed operations in computer graphics. A realistic composite requires adjusting the appearance of the foreground and background so that they appear compatible; unfortunately, this task is challenging and poorly understood. We use statistical and visual perception experiments to study the realism of image composites. First, we evaluate a number of standard 2D image statistical measures, and identify those that are most significant in determining the realism of a composite. Then, we perform a human subjects experiment to determine how the changes in these key statistics influence human judgements of composite realism. Finally, we describe a data-driven algorithm that automatically adjusts these statistical measures in a foreground to make it more compatible with its background in a composite. We show a number of compositing results, and evaluate the performance of both our algorithm and previous work with a human subjects study.

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Progressive histogram reshaping for creative color transfer and tone reproduction

Cited by 61 publications

References 25 publications

3D color homography model for photo-realistic color transfer re-coding

3D color homography model for photo-realistic color transfer re-coding

The high dynamic range imaging pipeline : Tone-mapping, distribution, and single-exposure reconstruction

Understanding and improving the realism of image composites

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