Color correction system and method

• US 10,560,607 B2
• Filed: 03/20/2019
• Issued: 02/11/2020
• 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:
  US16/358,946
• Publication Number:
  US 20190215418A1
• Time in Patent Office: 328 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 23/88 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 computer-implemented method for color correction includes determining a peak signal-to-noise ratio (PSNR) for a noise evaluation image, determining a corrected PSNR for a corrected noise evaluation image, determining a downsampled PSNR for a downsampled noise evaluation image obtained by downsampling the noise evaluation image, determining a downsampled corrected PSNR for a downsampled corrected noise evaluation image obtained by downsampling the corrected noise evaluation image, and determining a noise amplification metric based on the PSNR, the corrected PSNR, the downsampled PSNR, and the downsampled corrected PSNR.

Claims

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

determining a peak signal-to-noise ratio (PSNR) for a noise evaluation image;

determining a corrected PSNR for a corrected noise evaluation image;

determining a downsampled PSNR for a downsampled noise evaluation image obtained by downsampling the noise evaluation image;

determining a downsampled corrected PSNR for a downsampled corrected noise evaluation image obtained by downsampling the corrected noise evaluation image; and

determining a noise amplification metric based on the PSNR, the corrected PSNR, the downsampled PSNR, and the downsampled corrected PSNR.

2. The method of claim 1, wherein determining the noise amplification metric includes:

determining a PSNR difference that is a difference between the PSNR and the corrected PSNR;

determining a downsampled PSNR difference that is a difference between the downsampled PSNR and the downsampled corrected PSNR; and

determining the noise amplification metric as a weighted average of the PSNR difference and the downsampled PSNR difference.

3. The method of claim 1, further comprising:

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

4. The method of claim 1, further comprising:

adjusting a plurality of color correction parameters based on a fitness function determined using the noise amplification metric and a color correction error.

5. The method of claim 4, further comprising:

color correcting input color values; and

comparing the corrected input color values with respective reference color values to determine the color correction error.

6. The method of claim 1, further comprising:

obtaining the noise evaluation image by adding noise to a noise-free image.

7. The method of claim 1, further comprising:

color correcting the noise evaluation image using a plurality of color correction parameters to obtain the corrected noise evaluation image.

8. The method of claim 1, wherein the noise evaluation image includes a virtual noise evaluation image.

9. 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 instructions to;

determine a peak signal-to-noise ratio (PSNR) for a noise evaluation image;

determine a corrected PSNR for a corrected noise evaluation image;

determine a downsampled PSNR for a downsampled noise evaluation image obtained by downsampling the noise evaluation image;

determine a downsampled corrected PSNR for a downsampled corrected noise evaluation image obtained by downsampling the corrected noise evaluation image; and

determine a noise amplification metric based on the PSNR, the corrected PSNR, the downsampled PSNR, and the downsampled corrected PSNR.

10. The system of claim 9, wherein the one or more processors are further configured to execute the instructions to:

determine a PSNR difference that is a difference between the PSNR and the corrected PSNR;

determine a downsampled PSNR difference that is a difference between the downsampled PSNR and the downsampled corrected PSNR; and

determine the noise amplification metric as a weighted average of the PSNR difference and the downsampled PSNR difference.

11. The system of claim 9, wherein the one or more processors are further configured to execute the instructions to:

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

12. The system of claim 9, wherein the one or more processors are further configured to execute the instructions to:

adjust a plurality of color correction parameters based on a fitness function determined using the noise amplification metric and a color correction error.

13. The system of claim 12, wherein the one or more processors are further configured to execute the instructions to:

color correct input color values; and

compare the corrected input color values with respective reference color values to determine the color correction error.

14. The system of claim 9, wherein the one or more processors are further configured to execute the instructions to:

obtain the noise evaluation image by adding noise to a noise-free image.

15. The system of claim 9, wherein the one or more processors are further configured to execute the instructions to:

color correct the noise evaluation image using a plurality of color correction parameters to obtain the corrected noise evaluation image.

16. The system of claim 9, wherein the noise evaluation image includes a virtual noise evaluation image.

17. 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:

determine a peak signal-to-noise ratio (PSNR) for a noise evaluation image;

determine a corrected PSNR for a corrected noise evaluation image;

determine a downsampled PSNR for a downsampled noise evaluation image obtained by downsampling the noise evaluation image;

determine a downsampled corrected PSNR for a downsampled corrected noise evaluation image obtained by downsampling the corrected noise evaluation image; and

determine a noise amplification metric based on the PSNR, the corrected PSNR, the downsampled PSNR, and the downsampled corrected PSNR.

18. The one or more non-transitory computer-readable storage media of claim 17, wherein the instructions further configure the computing system to:

determine a PSNR difference that is a difference between the PSNR and the corrected PSNR;

determine a downsampled PSNR difference that is a difference between the downsampled PSNR and the downsampled corrected PSNR; and

determine the noise amplification metric as 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 18, wherein the instructions further configure the computing system to:

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

20. The one or more non-transitory computer-readable storage media of claim 19, wherein the instructions further configure the computing system to:

adjust a plurality of color correction parameters based on a fitness function determined using the noise amplification metric and a color correction error.