Color correction system and method

• US 9,998,632 B2
• Filed: 07/11/2017
• Issued: 06/12/2018
• Est. Priority Date: 05/15/2015
• Status: Active Grant

• Current Assignee: SZ DJI Technology Co., Ltd. (d/b/a DJI) (iFlight Technology Company Limited)
• Original Assignee: SZ DJI Technology Co., Ltd. (d/b/a DJI) (iFlight Technology Company Limited)
• Inventors: Chen, Wei, Cao, Zisheng
• Primary Examiner(s): Hung, Yubin
• Application Number:
  US15/646,301
• Publication Number:
  US 20170318194A1
• Time in Patent Office: 336 Days
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06T 7/90 Determination of colour characteristics
• CPC Class Codes: G09G 2320/0693 Calibration of display systems
• CPC Class Codes: G09G 2340/0407 Resolution change, inclusive of the use of different resolutions for different screen areas
• CPC Class Codes: G09G 2340/06 Colour space transformation
• CPC Class Codes: G09G 2370/12 Use of DVI or HDMI protocol in interfaces along the display data pipeline
• CPC Class Codes: G09G 2380/08 Biomedical applications
• CPC Class Codes: G09G 5/02 characterised by the way in which colour is displayed details of colour display specific for CRTs G09G1/28; specific for flat matrix panels other than liquid crystal displays G09G3/2003; specific for liquid crystal displays G09G3/3607
• CPC Class Codes: G09G 5/06 using colour palettes, e.g. look-up tables
• CPC Class Codes: H04N 1/60 Colour correction or control H04N1/54 takes precedence
• CPC Class Codes: H04N 1/6027 Correction or control of colour gradation or colour contrast H04N1/6058 takes precedence
• CPC Class Codes: H04N 1/6033 using test pattern analysis H04N1/6055 takes precedence
• CPC Class Codes: H04N 23/86 for controlling the colour saturation of colour signals, e.g. automatic chroma control circuits
• CPC Class Codes: H04N 23/88 for colour balance, e.g. white-balance circuits or colour temperature control
• CPC Class Codes: H04N 9/68 for controlling the amplitude of colour signals, e.g. automatic chroma control circuits H04N9/71, H04N9/73 take precedence; camera processing pipelines for controlling the colour saturation of colour signals H04N23/86

Abstract

A system and method for color correction is disclosed to reduce noise amplification during color correction. The method can comprise color correcting a noise evaluation image using a plurality of color correction parameters, determining a noise amplification metric by comparing the corrected noise evaluation image with the pre-correction noise evaluation image, and adjusting the plurality of color correction parameters based on the noise amplification metric.

Claims

1. A computer-implemented method for color correction, comprising:

color correcting a noise evaluation image using a plurality of color correction parameters;

determining a noise amplification metric by comparing the corrected noise evaluation image with the pre-correction noise evaluation image, wherein determining the noise amplification metric further comprises determining a ratio between a first variance of a color space component for the pre-corrected noise evaluation image and a second variance of the color space component for the post-corrected noise evaluation image; and

adjusting the plurality of color correction parameters based on the noise amplification metric.

2. The method of claim 1, wherein determining the noise amplification metric comprises determining a PSNR difference that is a difference between a peak signal-to-noise ratio (PSNR) for the pre-correction noise evaluation image and a PSNR for the corrected noise evaluation image.

3. The method of claim 2, wherein determining the noise amplification metric comprises determining a downsampled peak signal-to-noise ratio (PSNR) difference by:

downsampling the pre-correction noise evaluation image to obtain a downsampled pre-correction noise evaluation image;

downsampling the corrected noise evaluation image to obtain a downsampled corrected noise evaluation image; and

finding the downsampled PSNR difference as a difference between a PSNR for the downsampled pre-correction noise evaluation image and a PSNR for the downsampled corrected noise evaluation image.

4. The method of claim 3, wherein determining the noise amplification metric comprises determining a weighted average of the PSNR difference and the downsampled PSNR difference.

5. The method of claim 1, wherein determining the noise amplification metric comprises determining a weighted sum of noise amplification levels of each of a plurality of color patches of the noise evaluation image.

6. The method of claim 1, wherein said adjusting comprises determining a fitness function using the noise amplification metric and a color correction error.

7. The method of claim 6, wherein the color correction error is determined by:

color correcting input color values; and

comparing the corrected input color values with respective reference color values.

8. A system, comprising:

a memory that stores one or more computer-executable instructions; and

one or more processors configured to access the memory and execute the computer-executable instructions to perform a method comprising;

color correcting a noise evaluation image using a plurality of color correction parameters;

determining a noise amplification metric by comparing the corrected noise evaluation image with the pre-correction noise evaluation image, wherein determining the noise amplification metric further comprises determining a ratio between a first variance of a color space component for the pre-corrected noise evaluation image and a second variance of the color space component for the post-corrected noise evaluation image; and

adjusting the plurality of color correction parameters based on the noise amplification metric.

9. The system of claim 8, wherein determining the noise amplification metric comprises determining a PSNR difference that is a difference between a peak signal-to-noise ratio (PSNR) for the pre-correction noise evaluation image and a PSNR for the corrected noise evaluation image.

10. The system of claim 9, wherein determining the noise amplification metric comprises determining a downsampled peak signal-to-noise ratio (PSNR) difference by:

downsampling the pre-correction noise evaluation image to obtain a downsampled pre-correction noise evaluation image;

downsampling the corrected noise evaluation image to obtain a downsampled corrected noise evaluation image; and

finding the downsampled PSNR difference as a difference between a PSNR for the downsampled pre-correction noise evaluation image and a PSNR for the downsampled corrected noise evaluation image.

11. The system of claim 10, wherein determining the noise amplification metric comprises determining a weighted average of the PSNR difference and the downsampled PSNR difference.

12. The system of claim 8, wherein determining the noise amplification metric comprises determining a weighted sum of noise amplification levels of each of a plurality of color patches of the noise evaluation image.

13. The system of claim 8, wherein said adjusting comprises determining a fitness function using the noise amplification metric and a color correction error.

14. The system of claim 13, wherein the color correction error is determined by:

color correcting input color values; and

comparing the corrected input color values with respective reference color values.

15. One or more non-transitory computer-readable storage media storing computer-executable instructions that, when executed by a computing system, configure the computing system to perform a method comprising:

color correcting a noise evaluation image using a plurality of color correction parameters;

determining a noise amplification metric by comparing the corrected noise evaluation image with the pre-correction noise evaluation image, wherein determining the noise amplification metric further comprises determining a ratio between a first variance of a color space component for the pre-corrected noise evaluation image and a second variance of the color space component for the post-corrected noise evaluation image; and

adjusting the plurality of color correction parameters based on the noise amplification metric.

16. The one or more non-transitory computer-readable storage media of claim 15, wherein determining the noise amplification metric comprises determining a PSNR difference that is a difference between a peak signal-to-noise ratio (PSNR) for the pre-correction noise evaluation image and a PSNR for the corrected noise evaluation image.

17. The one or more non-transitory computer-readable storage media of claim 16, wherein determining the noise amplification metric comprises determining a downsampled peak signal-to-noise ratio (PSNR) difference by:

downsampling the pre-correction noise evaluation image to obtain a downsampled pre-correction noise evaluation image;

downsampling the corrected noise evaluation image to obtain a downsampled corrected noise evaluation image; and

finding the downsampled PSNR difference as a difference between a PSNR for the downsampled pre-correction noise evaluation image and a PSNR for the downsampled corrected noise evaluation image.

18. The one or more non-transitory computer-readable storage media of claim 17, wherein determining the noise amplification metric comprises determining a weighted average of the PSNR difference and the downsampled PSNR difference.

19. The one or more non-transitory computer-readable storage media of claim 15, wherein determining the noise amplification metric comprises determining a weighted sum of noise amplification levels of each of a plurality of color patches of the noise evaluation image.

20. The one or more non-transitory computer-readable storage media of claim 15, wherein said adjusting comprises determining a fitness function using the noise amplification metric and a color correction error.