Jagged edge reduction using kernel regression

• US 9,558,532 B2
• Filed: 06/12/2015
• Issued: 01/31/2017
• Est. Priority Date: 11/30/2012
• Status: Expired due to Fees

• Current Assignee: Sharp Kabushiki Kaisha (Hon Hai Precision Industry Co., Ltd.)
• Original Assignee: Sharp Laboratories of America Incorporated (Hon Hai Precision Industry Co., Ltd.)
• Inventors: Aghagolzadeh, Mohammad, Segall, Christopher A.
• Primary Examiner(s): Bayat, Ali
• Application Number:
  US14/738,613
• Publication Number:
  US 20150356707A1
• Time in Patent Office: 599 Days
• Field of Search: 382/299
• US Class Current: 1/1
• CPC Class Codes: G06T 2207/10004 Still image; Photographic image
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/20016 Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
• CPC Class Codes: G06T 2207/20024 Filtering details
• CPC Class Codes: G06T 2207/20192 Edge enhancement; Edge preservation
• CPC Class Codes: G06T 3/40 Scaling of whole images or parts thereof, e.g. expanding or contracting
• CPC Class Codes: G06T 3/403 Edge-driven scaling; Edge-based scaling
• CPC Class Codes: G06T 5/20 using local operators
• CPC Class Codes: G06T 5/73 Deblurring; Sharpening
• CPC Class Codes: G06T 7/13 Edge detection

Abstract

An image decoder includes a base layer to decode at least a portion of an encoded video stream into a first image having a first image format. The image decoder can generate a color space prediction by scaling a color space of the first image from the first image format into a color space corresponding to a second image format. The image decoder includes an enhancement layer to decode the encoded video stream to generate a second image in the second image format based, at least in part, on the color space prediction.

Claims

1. A computer-implemented method comprising:

detecting edges in an image, including portions of the image that correspond to a jagged edge configuration;

selectively applying kernel regression to portions of the image that correspond to the jagged edge configuration; and

outputting a corrected image.

2. The computer-implemented method of claim 1, wherein selectively applying kernel regression to portions of the image further comprises:

dilating the detected edges of the image to include portions of the image adjacent to the detected edges in a dilated image region; and

selectively applying kernel regression to the portions of the image within the dilated image region.

3. The computer-implemented method of claim 1, wherein detecting edges in the image further comprises comparing the image to jagged edge configurations.

4. The computer-implemented method of claim 3, wherein the comparing of the image to jagged edge configurations is performed by a matched filter bank loaded with the jagged edge configurations.

5. The computer-implemented method of claim 3, wherein selectively applying kernel regression further comprises adjusting a size of kernels utilized in the kernel regression based on the comparison of the image to jagged edge configurations.

6. A computer-implemented method comprising:

detecting an edge in an image;

dilating the detected edge of the image to include portions of the image adjacent to the detected edge in a dilated image region;

selectively applying kernel regression to detecting edges in an image, including portions of the image that correspond to a jagged edge configuration;

selectively applying kernel regression to portions of the image that correspond to the jagged edge configuration; and

outputting a corrected image.

7. The computer-implemented method of claim 6, wherein dilating the detected edges of the image includes dilating the edges by more than one pixel in at least one direction around the detected edges.

8. The computer-implemented method of claim 6, wherein detecting the edge in the image employs Canny edge detection.

9. The computer-implemented method of claim 6, wherein selectively applying kernel regression to the portions of the image within the dilated image region includes applying kernel regression to the detected edge and not applying kernel regression to other portions of the image.

10. A computer-implemented method comprising:

detecting an edge in an image, including comparing the image to plural jagged edge configurations;

selectively applying kernel regression to portions of the image based, at least in part, on the detection of the edge in the image corresponding to the jagged edge configurations; and

outputting a corrected image.

11. The computer-implemented method of claim 10, wherein selectively applying kernel regression further comprises applying kernel regression to the portions of the image corresponding the jagged edge configurations.

12. The computer-implemented method of claim 10, wherein the comparing of the image to plural jagged edge configurations includes determining a score of a degree of match to each of the plural jagged edge configurations.

13. The computer-implemented method of claim 12, further including determining a composite score for jaggedness in the image by selecting a maximum score out of the scores of the degree of match to the plural jagged edge configurations.

14. The computer-implemented method of claim 12, further including determining a composite score for jaggedness in the image by aggregating the scores of the degree of match to the plural jagged edge configurations.

15. The computer-implemented method of claim 10, wherein selectively applying kernel regression further comprises adjusting a size of kernels utilized in the kernel regression based on the comparison of the image to jagged edge configurations.