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A Gamut Boundary Metadata Format
Abstract
Recent display technologies (LCD backlight, OLED) allow watching images with more contrast and more saturated colors than even digital cinema. Unfortunately, today's video content and broadcast cannot convey such colors due to the currently used colorimetry standard (ITU-R.BT 709). Solutions exist for more contrast and wider color gamut, but they are different in the video and photography worlds. New standardization initiatives for video (IEC 61966-2-4, ITU-R) try to set up a new, extended but fixed colorimetry, while digital photography applies - since a decade – flexible color management (ICC). However, in all these approaches the color gamut of either devices or contents is not described explicitly. This paper presents the new international standard IEC 61966-12-1 “Metadata for identification of colour gamut (Gamut ID)”. This standard allows the precise and flexible description of a color gamut. The metadata supports graphics hardware, scalability, memory footprint efficiency, convex handling of non-convex gamuts, handling of fuzzy color gamuts, and handling of gamut cusps. This standard may be used in future systems for video color management or for image-dependent gamut mapping.
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Abstract The present paper aims to give an survey of the fundamentals,of gamut mapping,by describing the cross‐media colour reproduction context in which it occurs, by giving definitions of terms used in conjunction with it, by describing its aims and by giving an overview of parameters that influence it. These parameters are primarily the choice of colour space used, the category into which a gamut mapping,algorithm belongs and whether the approach is image ormedium,dependent. A succinct summary,is then given of the principal trends in gamut mapping,studies conducted,to date whereby,this summary,is based,on a,survey of a,large number,of publications.Individual reviews of these publications can also be accessed on‐line atthe web site of the CIE Technical Committee 8‐03 on Gamut,Mapping on whose,behalf this paper was submitted for publication. Key words gamut mapping, cross‐media reproduction 3
The Segment Maxima Method for calculating gamut boundary descriptors of both colour reproduction media and colour images is introduced. Methods for deter-mining the gamut boundary along a given line of mapping used by gamut mapping algorithms are then described, whereby these methods use the Gamut Boundary Descriptor obtained using the Segment Maxima Method. Throughout the article, the focus is both on colour reproduction media and colour images as well as on the suitability of the methods for use in gamut mapping.
Using a paired comparison paradigm, various gamut mapping algorithms were evaluated using simple rendered images and artificial gamut boundaries. The test images consisted of simple rendered spheres floating in front of a gray background. Using CIELAB as our device-independent color space, cut-off values for lightness and chroma, based on the statistics of the images, were chosen to reduce the gamuts for the test images. The gamut mapping algorithms consisted of combinations of clipping and mapping the original gamut in linear piecewise segments. Complete color space compression in RGB and CIELAB was also tested. Each of the colored originals (R,G,B,C,M,Y, and Skin) were mapped separately in lightness and chroma. In addition, each algorithm was implemented with saturation (C(*)/L(*)) allowed to vary or retain the same values as in the original image. Pairs of test images with reduced color gamuts were presented to twenty subjects along with the original image. For each pair the subjects chose the test image that better reproduced the original. Rank orders and interval scales of algorithm performance with confidence limits were then derived. Clipping all out-of-gamut colors was the best method for mapping chroma. For lightness mapping at low lightness levels and high lightness levels particular gamut mapping algorithms consistently produced images chosen as most like the original. The choice of device-independent color space may also influence which gamut mapping algorithms are best.