Color correction for tone mapping

Tone mapping algorithms offer sophisticated methods for mapping a real‐world luminance range to the luminance range of the output medium but they often cause changes in color appearance. In this work we conduct a series of subjective appearance matching experiments to measure the change in image colorfulness after contrast compression and enhancement. The results indicate that the relation between contrast compression and the color saturation correction that matches color appearance is non‐linear and smaller color correction is required for small change of contrast. We demonstrate that the relation cannot be fully explained by color appearance models. We propose color correction formulas that can be used with existing tone mapping algorithms. We extend existing global and local tone mapping operators and show that the proposed color correction formulas can preserve original image colors after tone scale manipulation.

[1]  Greg Turk,et al.  LCIS: a boundary hierarchy for detail-preserving contrast reduction , 1999, SIGGRAPH.

[2]  Alexei A. Efros,et al.  Fast bilateral filtering for the display of high-dynamic-range images , 2002 .

[3]  Rafal Mantiuk,et al.  Display adaptive tone mapping , 2008, ACM Trans. Graph..

[4]  Michael H. Brill,et al.  Color appearance models , 1998 .

[5]  Christophe Schlick,et al.  Quantization Techniques for Visualization of High Dynamic Range Pictures , 1995 .

[6]  Mark D. Fairchild,et al.  Perceived Image Contrast and Observer Preference I. The Effects of Lightness, Chroma, and Sharpness Manipulations on Contrast Perception , 2003, Journal of Imaging Science and Technology.

[7]  D. Macleod,et al.  Color appearance depends on the variance of surround colors , 1997, Current Biology.

[8]  Rafal Mantiuk,et al.  Display adaptive tone mapping , 2008, SIGGRAPH 2008.

[9]  M. Ronnier Luo,et al.  The Fundamentals of Gamut Mapping: A Survey , 2001, Journal of Imaging Science and Technology.

[10]  Mark D. Fairchild,et al.  The iCAM Framework for Image Appearance, Image Differences, and Image Quality , 2002 .

[11]  Mark D. Fairchild,et al.  iCAM06: A refined image appearance model for HDR image rendering , 2007, J. Vis. Commun. Image Represent..

[12]  S. S. Stevens,et al.  Brightness function: effects of adaptation. , 1963, Journal of the Optical Society of America.

[13]  M Ayama,et al.  Constant hue loci of unique and binary balanced hues at 10, 100, and 1000 Td. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[14]  Peter Zolliker,et al.  Image-Dependent Gamut Mapping as Optimization Problem , 2007, IEEE Transactions on Image Processing.

[15]  Donald P. Greenberg,et al.  Time-dependent visual adaptation for fast realistic image display , 2000, SIGGRAPH.

[16]  E. J. Breneman,et al.  Brightness perception in complex fields. , 1967, Journal of the Optical Society of America.

[17]  Roy S. Berns,et al.  Effect of image size on the color appearance of image reproductions using colorimetrically calibrated LCD and DLP displays , 2006 .

[18]  R. Hunt.  Light and dark adaptation and the perception of color. , 1952, Journal of the Optical Society of America.

[19]  Hans-Peter Seidel,et al.  Lightness Perception in Tone Reproduction for High Dynamic Range Images , 2005, Comput. Graph. Forum.

[20]  Donald P. Greenberg,et al.  A multiscale model of adaptation and spatial vision for realistic image display , 1998, SIGGRAPH.

[21]  H. Ridder,et al.  Chroma variations and perceived quality of color images of natural scenes , 1997 .

[22]  Y. Nayatani.  Simple estimation methods for the Helmholtz—Kohlrausch effect , 1997 .

[23]  Erik Reinhard,et al.  Color appearance in high-dynamic-range imaging , 2006, J. Electronic Imaging.

[24]  Mark D. Fairchild,et al.  iCAM framework for image appearance, differences, and quality , 2004, J. Electronic Imaging.

[25]  Changjun Li,et al.  The CIECAM02 Color Appearance Model , 2002, CIC.