IP Library Granted Patent US 9,264,581
Granted Patent B2
US 9,264,581 · App. 14/584,166 · Granted Feb 16, 2016

Systems and methods for lossy compression of image color profiles

Inventors: Apostolos Lerios (Austin, TX); Ryan David Mack (Waltham, MA)
Assignee: Facebook, Inc.
H04N1/54G06T3/00H04N1/603H04N1/6058
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Quick Facts
Patent No.
US 9,264,581
App. No.
14/584,166
Granted
Feb 16, 2016
Kind
B2
Abstract

In one embodiment, an image is received by a computing device, the image corresponding to a first color space. A color profile is created for transforming the image from the first color space to a second color space, the creation of the color profile being based on an approximation function for transforming the image. The approximation function is determined by a polyline which comprises a number of line segments. The polyline is separated from an ideal function curve for transforming the image by an error value that is within a pre-determined threshold.

Claims (45)

1. A method comprising:

by one or more computing devices, receiving an image that corresponds to a first color space; and

by one or more computing devices, creating, based on an approximation function, a color profile for transforming the image from the first color space to a second color space, wherein:

the approximation function is determined by a polyline;

the polyline comprises a number of line segments; and

the polyline is separated from an ideal function curve for transforming the image by an error value that is within a pre-determined threshold.

2. The method of claim 1 , wherein the error value is calculated using an error metric for the color profile.

3. The method of claim 2 , wherein the determining the error metric comprises calculating a numerical difference between an ideal color and an approximate color.

4. The method of claim 2 , wherein determining the error metric comprises calculating a Delta-E for an ideal color and an approximate color.

5. The method of claim 2 , wherein determining the error metric comprises:

determining a frequency with which a color occurs in the image;

assigning a weight to the color based on the frequency; and

calculating the error metric based on the weight.

6. The method of claim 2 , wherein determining the error metric comprises:

dividing the image into a plurality of sections;

assigning a plurality of importance values to the plurality of sections; and

calculating the error metric based on the plurality of importance values.

7. The method of claim 2 , wherein determining the error metric comprises computing the error metric based on at least one color.

8. The method of claim 2 , wherein determining the error metric comprises computing the error metric based on a plurality of colors.

9. The method of claim 8 , wherein the plurality of colors are represented by consecutive bytes.

10. The method of claim 1 , wherein the plurality of polyline segments approximate the ideal function curve.

11. The method of claim 10 , wherein each of the polyline line segments has one or more control points which lie on the ideal function curve.

12. The method of claim 11 , wherein:

the number of line segments is determined by a binary search algorithm; and

the control points are determined by a simulated annealing algorithm.

13. The method of claim 1 , wherein the image is classified into one of a plurality of image classes based at least in part on a predominant color of the image, and the color profile is created based at least in part on the image class.

14. The method of claim 13 , wherein the predominant color is based at least in part on a histogram of the image.

15. The method of claim 13 , wherein the image is divided into one or more regions, and the predominant color is determined by a prevalent color in a subset of the regions.

16. The method of claim 15 , wherein the predominant color is further determined by a weight value assigned to each of the one or more regions and the corresponding prevalent color.

17. The method of claim 1 , wherein the creating the color profile comprises omitting consideration of a color that does not appear in the image.

18. The method of claim 1 , wherein the first color space comprises at least one of an RGB color space and an XYZ color space.

19. A system comprising:

at least one processor; and

a memory storing instructions configured to instruct the at least one processor to:

receive an image that corresponds to a first color space; and

create, based on an approximation function, a color profile for transforming the image from the first color space to a second color space, wherein:

the approximation function is determined by a polyline;

the polyline comprises a number of line segments; and

the polyline is separated from an ideal function curve for transforming the image by an error value that is within a pre-determined threshold.

20. A non-transitory computer-readable storage medium storing computer-executable instructions that, when executed, cause a computer system to:

receive an image that corresponds to a first color space; and

create, based on an approximation function, a color profile for transforming the image from the first color space to a second color space, wherein:

the approximation function is determined by a polyline;

the polyline comprises a number of line segments; and

the polyline is separated from an ideal function curve for transforming the image by an error value that is within a pre-determined threshold.

Assignments (2)
CHANGE OF NAME Recorded Jan 3, 2022
From: FACEBOOK, INC.
To: META PLATFORMS, INC.
Reel/Frame 058605/0840 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 29, 2021
From: LERIOS, APOSTOLOS; MACK, RYAN DAVID
To: FACEBOOK, INC.
Reel/Frame 058504/0453 →
Continuity (2)
Continuation 13591948 · Aug 22, 2012
Related Publication 20150110389A1 · Apr 23, 2015