Dynamic gamut mapping selection

• US 20030053683A1
• Filed: 09/20/2001
• Published: 03/20/2003
• Est. Priority Date: 09/20/2001
• Status: Active Grant

• Current Assignee: Canon Ayutthaya Limited (Canon Inc.)
• Original Assignee: Canon Ayutthaya Limited (Canon Inc.)
• Inventors: Kohler, Timothy L., Haikin, John S., Newman, Todd D.
• Granted Patent: US 6,947,589 B2
• Time in Patent Office: Days
• US Class Current: 382/162
• CPC Class Codes: G06T 11/001 Texturing; Colouring; Generation of texture or colour inpainting G06T5/77
• CPC Class Codes: H04N 1/6058 Reduction of colour to a range of reproducible colours, e.g. to ink- reproducible colour gamut

Abstract

The present invention is directed to a mechanism of dynamically selecting a gamut mapping component for use in a color management system which transforms colors specified in image data from a first color space to a second color space, the method comprising automatically generating prediction information for use in selecting from a plurality of gamut mapping components, wherein the prediction information is based on determined gamut mapping preferences corresponding to at least one characteristic of the image data, and automatically selecting at least one of the plurality of gamut mapping components based on the prediction information.

Claims

1. A method of dynamically selecting a gamut mapping component for use in a color management system which transforms colors specified in image data from a first color space to a second color space, the method comprising:

automatically generating prediction information for use in selecting from a plurality of gamut mapping components, wherein the prediction information is based on determined gamut mapping preferences corresponding to at least one characteristic of the image data; and

automatically selecting at least one of the plurality of gamut mapping components based on the prediction information.

2. A method according to claim 1, wherein the prediction information is generated based on the colors of the image data.

3. A method according to claim 1, wherein the image data includes metadata, the prediction information is generated based at least in part on the metadata.

4. A method according to claim 3, wherein the metadata is automatically generated during generation of the image data.

5. A method according to claim 4, wherein the image data is generated by an image capture device, and wherein the metadata comprises information associated with the image capture.

6. A method according to claim 5, wherein the image capture information includes one or more of aperture, exposure time and flash usage information.

7. A method according to claim 4, wherein the metadata is user-specified, and wherein the metadata comprises annotations to the image.

8. A method according to claim 1, wherein the prediction information is based at least in part on an image pixel hue characteristic.

9. A method according to claim 1, wherein the prediction information is based at least in part on an image pixel chroma characteristic.

10. A method according to claim 1, wherein the prediction information is based at least in part on a redness characteristic.

11. A method according to claim 10, wherein the redness characteristic is defined as a proportion of pixels having a CIELAB metric hue angle within a given range and a CIELAB metric chromas that satisfies a given chroma threshold.

12. A method according to claim 1, wherein the prediction information comprises a colorfulness predictor.

13. A method according to claim 12, wherein the colorfulness characteristic is defined as a proportion of pixels with CIELAB metric chromas greater than or equal to a colorfulness threshold.

14. A method according to claim 1, wherein dynamic selection of a GMA is performed using a neural network.

15. A method according to claim 1, wherein the prediction information comprises at least one predictor of output quality for each of the plurality of gamut mapping components.

16. A method according to claim 15, wherein a gamut mapping component is selected based on the at least one predictor associated with each of the plurality of gamut mapping components for the given predictor.

17. A method according to claim 1, further comprising:

transforming the image data from the first color space to the second color space using the selected gamut mapping component.

18. A method according to claim 17, wherein transformation of the image data is performed in a transformation pipeline, the selection of a gamut mapping component is performed outside the transformation pipeline.

19. A method according to claim 17, wherein transformation of the image data is performed in a transformation pipeline, the selection of a gamut mapping component is performed within the transformation pipeline.

20. A method according to claim 1, further comprising a step of:

storing the at least one characteristic of the image as metadata of the image.

21. A system for dynamically selecting a gamut mapping component for use in a color management system to transform colors specified by image data from a first color space to a second color space, the system comprising:

at least one image characterizer configured to automatically identify a characteristic of image data;

selection engine configured to generate prediction information for use in selecting from a plurality of gamut mapping components, wherein the prediction information is based on determined gamut mapping preferences corresponding to the characteristic of the image data, and wherein the selection engine is configured to automatically select at least one of the plurality of gamut mapping components based on the prediction information.

22. A system according to claim 21, wherein the prediction information comprises at least one predictor of output quality for each of the plurality of gamut mapping components.

23. A system according to claim 22, wherein a gamut mapping component is selected based on the at least one predictor associated with each of the plurality of gamut mapping components.