Color correction

Summary

Color correction is a process used in stage lighting, photography, television, cinematography, and other disciplines, which uses color gels, or filters, to alter the overall color of the light. Typically the light color is measured on a scale known as color temperature, as well as along a green–magenta axis orthogonal to the color temperature axis. Without color-correction gels, a scene may have a mix of various colors. Applying color-correction gels in front of light sources can alter the color of the various light sources to match. Mixed lighting can produce an undesirable aesthetic when displayed on a television or in a theatre. Conversely, gels may also be used to make a scene appear more natural by simulating the mix of color temperatures that occur naturally. This application is useful, especially where motivated lighting (lending the impression that it is diegetic) is the goal. Color gels may also be used to tint lights for artistic effect. Correlated color temperatu

Official source

https://en.wikipedia.org/wiki/Color_correction

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Color temperature is a parameter describing the color of a visible light source by comparing it to the color of light emitted by an idealized opaque, non-reflective body. The temperature of the id

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Segmenting memory colours

Francisco Estrada, Sabine Süsstrunk

Memory colours refer to the colour of specific image classes that have the essential attribute of being perceived in a consistent manner by human observers. In colour correction or rendering tasks, this consistency implies that they have to be faithfully reproduced; their importance, in that respect, is greater than other regions in an image. Before these regions can be properly addressed, one must in general detect them. There are various schemes and attributes to do so, but the preferred method remains to segment the images into meaningful regions, a task for which many algorithms exist. Memory colours’ regions are not, however, similar in their attributes. Significant variations in shape, size, and texture do exist. As such, it is unclear whether a single algorithm is the most adapted for all of these classes. In this work, we concern ourselves with three memory colours: blue sky, green vegetation, and skin tones. Using a large database of real-world images, we (randomly) select and manually segment 900 images that contain one of the three memory colours. The same images are then automatically segmented with four classical algorithms. Using class-specific eigenregions, we are able to provide insights into the underlying structures of the considered classes and class-specific features that can be used to improve classification’s accuracy. Finally, we propose a distance measure that effectively results in determining how well is an algorithm is adapted to segment a given class.

2008

Memory colours refer to the colour of specific image regions that have the essential attribute of being perceived in a consistent manner by human observers. In colour correction-or rendering-tasks, this consistency implies that they have to be faithfully reproduced; their importance, in that respect, is greater than for other regions in an image. There are various schemes and attributes to detect memory colours, but the preferred method remains to segment the images into meaningful regions, a task for which many algorithms exist. Memory colour regions are not, however, similar in their attributes. Significant variations in shape, size, and texture exist. As such, it is unclear whether a single segmentation algorithm is the most adapted for all of these classes. Using a large database of real-world images, we calculate class-specific geometrical features, eigenregions, that can be used to evaluate how well an algorithm is adapted to segment a given class and give a measure of localisation of memory colours. We also compare the performance of our class-specific eigenregions to general ones in the task of memory colour region classification and observe that they provide a noticeable improvement in classification rates.

2009

NTIRE 2021 Depth Guided Image Relighting Challenge

Majed El Helou, Sabine Süsstrunk, Radu Timofte, Ruofan Zhou

Image relighting is attracting increasing interest due to its various applications. From a research perspective, image relighting can be exploited to conduct both image normalization for domain adaptation, and also for data augmentation. It also has multiple direct uses for photo montage and aesthetic enhancement. In this paper, we review the NTIRE 2021 depth guided image relighting challenge. We rely on the VIDIT dataset for each of our two challenge tracks, including depth information. The first track is on one-to-one relighting where the goal is to transform the illumination setup of an input image (color temperature and light source position) to the target illumination setup. In the second track, the any-to-any relighting challenge, the objective is to transform the illumination settings of the input image to match those of another guide image, similar to style transfer. In both tracks, participants were given depth information about the captured scenes. We had nearly 250 registered participants, leading to 18 confirmed team submissions in the final competition stage. The competitions, methods, and final results are presented in this paper.

2021