A system and method for effectively rendering high dynamic range images may include a rendering manager that initially divides an original luminance image into a plurality of original subband images. The rendering manager may then convert the original subband images into original contrast images which may then be converted into original perceived contrast images. The rendering manager may then perform a compression procedure upon the original perceived contrast images to produce compressed perceived contrast images. The rendering manager may next convert the compressed perceived contrast images into compressed contrast images which may then be converted into compressed subband images. The rendering manager may then perform a subband combination procedure for combining the compressed subband images together with a lowest-frequency subband image to thereby generate a rendered luminance image. The rendering manager may finally combine rendered the rendered luminance image with corresponding chrominance information to generate a rendered composite image that may be utilized in any appropriate manner.

System and method for effectively rendering high dynamic range images

http://facweb.cs.depaul.edu/research/vc/seminar/paper/cheng_2004_digital-signal-processing.pdf

A simple and effective histogram equalization approach to image enhancement

Two new techniques for the conversion of color images to gray scale images are discussed. The necessary components for producing visually pleasing gray scale images are identified, and the inadequacies of previous methods are discussed. Several examples of the new techniques are included. The techniques are extended to the problem of recoloring images to preserve visual information for color deficient viewers. Results of a perceptual experiment are discussed, showing the advantages of the new techniques over existing techniques.

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Re-coloring images for gamuts of lower dimension

Recent work in color difference has led to the recommendation of CIEDE2000 for use as an industrial color difference equation. While CIEDE2000 was designed for predicting the visual difference for large isolated patches, it is often desired to determine the perceived difference of color images. The CIE TC8-02 has been formed to examine these differences. This paper presents an overview of spatial filtering combined with CIEDE2000, to assist TC8-02 in the evaluation and implementation of an image color difference metric. Based on the S-CIELAB spatial extension, the objective is to provide a single reference for researchers desiring to utilize this technique. A general overview of how S-CIELAB functions, as well as a comparison between spatial domain and frequency domain filtering is provided. A reference comparison between three CIE recommended color difference formulae is also provided. © 2003 Wiley Periodicals, Inc. Col Res Appl, 28, 425–435, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10195

https://www.cis.rit.edu/people/faculty/johnson/pub/ciede_scielab.pdf

A top down description of S-CIELAB and CIEDE2000

This article aims to give a survey of the fundamentals of gamut mapping by describing the cross-media color 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 color space used, the category into which a gamut mapping algorithm belongs and whether the approach is image or medium dependent. A succinct summary is then given of the principal trends in gamut mapping studies conducted to date.

The fundamentals of gamut mapping : A survey

In color gamut mapping of pictorial images, the lightness rendition of the mapped images plays a major role in the quality of the final image. For color gamut mapping tasks, where the goal is to produce a match to the original scene, it is important to maintain the perceived lightness contrast of the original image. Typical lightness remapping functions such as linear compression, soft compression, and hard clipping reduce the lightness contrast of the input image. Sigmoidal remapping functions were utilized to overcome the natural loss in perceived lightness contrast that results when an image from a full dynamic range device is scaled into the limited dynamic range of a destination device. These functions were tuned to the particular lightness characteristics of the images used and the selected dynamic ranges. The sigmoidal remapping functions were selected based on an empirical contrast enhancement model that was developed for the result of a psychophysical adjustment experiment. The results of this study showed that it was possible to maintain the perceived lightness contrast of the images by using sigmoidal contrast enhancement functions to selectively rescale images from a source device with a full dynamic range into a destination device with a limited dynamic range.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Image lightness rescaling using sigmoidal contrast enhancement functions

Color gamut mapping plays a crucial role in color management Depending on the application, it is sometimes desirable to perform color gamut mapping by shifting the lightness and compressing the chroma of an out-of-gamut color while preserving the perceived hue of the color The term Operceived hueO is used to distinguish between the visual sensation of hue and metric hue angle (eg, CIELAB hue angle (hab) ) If a gamut-mapping task constrains CIELAB metric hue angle in the "blue" region of CIELAB, a perceived-hue shift will result Due to these nonlinearities, two hue-linearized versions of the CIELAB color space were generated, one from the Hung and Berns visual data (1995) and one from the Ebner and Fairchild data set (1998) Both data sets consist of visually mapped hue data to planes of constant visual hue These modified versions of the CIELAB color space were psychophysically tested for their hue-linearity characteristics against the CIELAB color space The results of these experiments show that, in the "blue" region of CIELAB, the hue-corrected color spaces are more visually uniform and perform better than CIELAB in gamut mapping situations with respect to perceived hue However, the CIELAB color space performed as good as or better than either hue-corrected spaces outside of the blue region

/pdf/color-gamut-mapping-in-a-hue-linearized-cielab-color-space-3bjfvilyrg.pdf

Color gamut mapping in a hue-linearized CIELAB color space

Brake drum for motor vehicle wheels having an encompassing band of high tensile metal, such as steel, embedded in and bonded to the cast metal forming the main body of the drum to minimize circumferential end expansion or bell-mounting of the drum, to reduce cracking of the cast metal, and to retain the drum in serviceable condition if cracks should develop.

Techniques for Gamut Surface Definition and Visualization.

General-Purpose Gamut-Mapping Algorithms : Evaluation of Contrast-Preserving Rescaling Functions for Color Gamut Mapping(Gamut Mapping II)(Report on CIC)

Experiments were performed to test a set of general-purpose gamut-mapping functions. These gamut-mapping algorithms utilized contrast-preserving scaling functions. These algorithms were tested against the GCUSP gamut-mapping algorithm proposed by Morovic and Luo, which was shown to have very good universal gamut-mapping characteristics. The results of these experiments showed that vast improvements were obtained when linear lightness and chroma rescaling functions were replaced with contrast-preserving lightness and chroma rescaling functions. For these experiments, the gamut mapping consisted of sigmoidal lightness-remapping functions followed by either knee or sigmoid-like chromatic compression functions.

Gustav J. Braun

Papers

Image lightness rescaling using sigmoidal contrast enhancement functions

Color gamut mapping in a hue-linearized CIELAB color space

Techniques for Gamut Surface Definition and Visualization.

General-Purpose Gamut-Mapping Algorithms : Evaluation of Contrast-Preserving Rescaling Functions for Color Gamut Mapping(Gamut Mapping II)(Report on CIC)

General-purpose gamut-mapping algorithms : Evaluation of contrast-preserving rescaling functions for color gamut mapping