18.3: Rendering Digital Cinema and Broadcast TV Content to Wide Gamut Display Media

Abstract

Much more is possible in achieving brighter, more vibrant colors for a richer visual experience in emerging, wide-gamut display media. Yet, both digital cinema and broadcast TV content fall well short of this promise. In this paper, we provide the means for realizing this promise by identifying certain memory colors (e.g., green grass, red, and blue sky) and rendering each independently making use of the full gamut of the display media while identifying and maintaining flesh tones to their original intent. Further, a means is provided for extending the gamut of not just wide-gamut, but any display media by simply lowering the white point of the display.

Vision Models for Wide Color Gamut Imaging in Cinema

“…A few methods [44], [45] perform gamut extension using functions learned from user studies. Unlike the aforementioned GEAs, some global methods [46], [47], [48] first classify the colors of the input image according to a criterion, and then perform gamut extension differently for each class. For example, labelling each color of a given image as skin or non-skin [46]; dealing with objects of low chroma and high chroma differently [47]; identifying certain memory colors such as green grass and blue sky, and rendering them independently [48].…”

“…Unlike the aforementioned GEAs, some global methods [46], [47], [48] first classify the colors of the input image according to a criterion, and then perform gamut extension differently for each class. For example, labelling each color of a given image as skin or non-skin [46]; dealing with objects of low chroma and high chroma differently [47]; identifying certain memory colors such as green grass and blue sky, and rendering them independently [48]. Other approaches [49], [50] propose three types of extensions: chroma extension, extension along lines from the origin, and adaptive mapping that is a compromise between the first two strategies.…”

Spatial gamut mapping among non-inclusive gamuts

“…Other methods label the data in different categories in order to perform gamut extension differently in each category. For example, [19,20] are defined to preserve skin tones, while [21] performs a different extension depending on the chroma of the object, and [22] categorizes different memory colors. In [6] different methods are proposed and evaluated.…”

Gamut Extension for Cinema

“…Later, Kang et al [21] and Anderson et al [5] presented user assisted methods to deal with the problem of gamut extension. While all these aforementioned algorithms treat each color without analyzing the content of the input image, Pan and Daly [33], Casella et al [12], Heckaman and Sullivan [18] introduced methods that first classify the colors of the input image according to some criterion and then extend them. In particular, the work of [33] labels each color of a given image as skin or nonskin, [12] deals with objects of low chroma and high chroma differently, and [18] identifies certain memory colors such as green grass and blue sky, and renders them independently.…”