Automated colour grading using colour distribution transfer

Pitié, François; Kokaram, Anil; Dahyot, Rozenn

doi:10.1016/j.cviu.2006.11.011

Cited by 333 publications

(444 citation statements)

References 24 publications

(37 reference statements)

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“…In the field of image processing and computer vision, Pitié et al (2005Pitié et al ( , 2007 developed a method, which they refer to as the N-dimensional probability density function transform (N-pdft), for transferring colour information (e.g., red, green, and blue, RGB, colour channels) from one image to another with the goal of recolouring a target image to match the "feel" of a source image. To the best of the author's knowledge, the N-pdft algorithm has not been explored outside of this context.…”

Section: Introductionmentioning

confidence: 99%

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

2017

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Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.

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Section: Introductionmentioning

confidence: 99%

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

2017

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“…When source and target image pair has correspondences, it is more precise to calculate color transfer directly using correspondences, instead of indirectly matching color statistics [RAGS01,PKD05,PKD07] Luminance We found it sufficient to use a simple weighted gamma curve mapping T ({α, γ},Y ) to model pairwise luminance transfer, which is denoted as:…”

Section: Photometric Alignment Between Framesmentioning

confidence: 99%

Photometric Stabilization for Fast‐forward Videos

Zhang

Lee

Sunkavalli

et al. 2017

Computer Graphics Forum

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Videos captured by consumer cameras often exhibit temporal variations in color and tone that are caused by camera autoadjustments like white-balance and exposure. When such videos are sub-sampled to play fast-forward, as in the increasingly popular forms of timelapse and hyperlapse videos, these temporal variations are exacerbated and appear as visually disturbing high frequency flickering. Previous techniques to photometrically stabilize videos typically rely on computing dense correspondences between video frames, and use these correspondences to remove all color changes in the video sequences. However, this approach is limited in fast-forward videos that often have large content changes and also might exhibit changes in scene illumination that should be preserved. In this work, we propose a novel photometric stabilization algorithm for fast-forward videos that is robust to large content-variation across frames. We compute pairwise color and tone transformations between neighboring frames and smooth these pair-wise transformations while taking in account the possibility of scene/content variations. This allows us to eliminate high-frequency fluctuations, while still adapting to real variations in scene characteristics. We evaluate our technique on a new dataset consisting of controlled synthetic and real videos, and demonstrate that our techniques outperforms the state-of-the-art.

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“…Most approaches (see for instance [2,3,4,5] for gray level or color modification) are based on histogram manipulation (e.g. histogram equalization, affine transformation, etc).…”

Section: Previous Workmentioning

confidence: 99%

Wasserstein regularization of imaging problem

Rabin

Peyré

2011

2011 18th IEEE International Conference on Image Processing

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This paper introduces a novel and generic framework embedding statistical constraints for variational problems. We resort to the theory of Monge-Kantorovich optimal mass transport to define penalty terms depending on statistics from images. To cope with the computation time issue of the corresponding Wasserstein distances involved in this approach, we propose an approximate variational formulation for statistics represented as point clouds.We illustrate this framework on the problem of regularized color specification. This is achieved by combining the proposed approximate Wasserstein constraint on color statistics with a generic geometric-based regularization term in a unified variational minimization problem. We believe that this methodology may lead to some other interesting applications in image processing, such as medical imaging modification, texture synthesis, etc.

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Automated colour grading using colour distribution transfer

Cited by 333 publications

References 24 publications

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

Photometric Stabilization for Fast‐forward Videos

Wasserstein regularization of imaging problem

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