IP Library › Granted Patent US 12,243,189
Granted Patent B2
US 12,243,189 · App. 17/977,616 · Granted Mar 4, 2025

Method to generate additional level of detail when zooming in on an image

Inventor: Michael Chen (Wallingford, PA)
Assignee: Comcast Cable Communications, LLC
G06T3/4053G06T11/001G06T19/006
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Quick Facts
Patent No.
US 12,243,189
App. No.
17/977,616
Granted
Mar 4, 2025
Kind
B2
Abstract

A viewer may zoom in on an image to see a portion of the image. The image may be analyzed to determine if zoom enhancement is necessary. The zoomed region may be matched to a replacement texture. The replacement texture may be used to enhance the image by replacing some or all of the image data.

Claims (67)

1. A method comprising:

determining, by a computing device and based on one or more image quality metrics associated with an area of interest of an image, the area of interest;

retrieving, from a database and based on information associated with the image, texture information, wherein the texture information comprises vector data; and

causing one or more adjustments, using the texture information, to the area of interest.

2. The method of claim 1 , wherein the one or more image quality metrics comprise:

spatial frequency content of the area of interest;

a quantity or percentage of source pixels in the area of interest;

a zoom ratio associated with the area of interest;

a distance from a virtual camera to a virtual object; or

a measure of detail associated with the area of interest.

3. The method of claim 1 , further comprising:

determining the information associated with the image based on a zoom ratio satisfying a zoom ratio threshold.

4. The method of claim 1 , further comprising:

causing the one or more adjustments to the area of interest by replacing a portion of the image with the texture information.

5. The method of claim 1 , further comprising:

causing the one or more adjustments to the area of interest by:

adjusting an opacity of the texture information; and

combining the image with the adjusted texture information.

6. The method of claim 1 , further comprising:

retrieving the texture information based on a zoom direction associated with the image.

7. The method of claim 1 , wherein the information associated with the image comprises a local binary pattern.

8. The method of claim 1 , wherein the image comprises a frame of a video sequence.

9. The method of claim 8 , further comprising:

retrieving the texture information based on a content type associated with the video sequence.

10. A method comprising:

segmenting, by a computing device and based on a first characteristic associated with an object included in an area of interest of an image, the area of interest into a plurality of segments; and

based on a determination of a second characteristic associated with each segment of the plurality of segments, selecting, for each segment, a texture from a texture database, wherein each texture comprises vector data; and

modifying, using a corresponding selected texture, each segment of the plurality of segments.

11. The method of claim 10 , further comprising:

searching, for each segment of the plurality of segments, a texture database for textures corresponding to a second characteristic associated with each segment of the plurality of segments; and

modifying each segment of the plurality of segments by combining a texture, selected from the textures, with image data associated with each segment of the plurality of segments.

12. The method of claim 10 , further comprising:

rejoining, after modification of each segment of the plurality of segments, the plurality of segments.

13. An apparatus comprising:

one or more processors; and

memory storing instructions that, when executed by the one or more processors, cause the apparatus to:

determine, based on one or more image quality metrics associated with an area of interest of an image, the area of interest;

retrieve, from a database and based on information associated with the image, texture information, wherein the texture information comprises vector data; and

cause one or more adjustments, using the texture information, to the area of interest.

14. The apparatus of claim 13 , wherein the one or more image quality metrics comprise:

spatial frequency content of the area of interest;

a quantity or percentage of source pixels in the area of interest;

a zoom ratio associated with the area of interest;

a distance from a virtual camera to a virtual object; or

a measure of detail associated with the area of interest.

15. The apparatus of claim 13 , wherein the one or more image quality metrics comprise a zoom ratio based on a dimension of the area of interest and a dimension of the image, and

wherein the instructions, when executed by the one or more processors, cause the apparatus to determine the information associated with the image based on the zoom ratio satisfying a zoom ratio threshold.

16. The apparatus of claim 13 , wherein the instructions, when executed by the one or more processors, cause the apparatus to cause the one or more adjustments to the area of interest by replacing a portion of the image with the texture information.

17. The apparatus of claim 13 , wherein the instructions, when executed by the one or more processors, cause the apparatus to cause the one or more adjustments to the area of interest by:

adjusting an opacity of the texture information; and

combining the image with the adjusted texture information.

18. The apparatus of claim 13 , wherein the instructions, when executed by the one or more processors, cause the apparatus to retrieve the texture information based on a zoom direction associated with the image.

19. The apparatus of claim 13 , wherein the information associated with the image is a local binary pattern.

20. The apparatus of claim 13 , wherein the image comprises a frame of a video sequence.

21. The apparatus of claim 20 , wherein the instructions, when executed by the one or more processors, cause the apparatus to retrieve the texture information based on a content type associated with the video sequence.

22. An apparatus comprising:

one or more processors; and

memory storing instructions that, when executed by the one or more processors, cause the apparatus to:

segment, based on a first characteristic associated with an object included in an area of interest of an image, the area of interest into a plurality of segments; and

for each one or more segments of the plurality of segments:

select, based on a determination of a second characteristic associated with each of the one or more segments, a texture from a texture database, wherein each texture comprises vector data; and

modify, using a corresponding selected texture, each of the one or more segments.

23. The apparatus of claim 22 , wherein the instructions, when executed by the one or more processors, cause the apparatus to:

for each of the one or more segments:

search a texture database for textures corresponding to the second characteristic associated with the segment; and

modify the one or more segments by combining a texture, selected from the textures, with image data associated with the segment.

24. The apparatus of claim 22 , wherein the instructions, when executed by the one or more processors, further cause the apparatus to rejoin, after modification of the one or more segments, the plurality of segments.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 2, 2024
From: CHEN, MICHAEL
To: COMCAST CABLE COMMUNICATIONS, LLC
Reel/Frame 066976/0791 →
Continuity (4)
Continuation 17205129 · Mar 18, 2021
Continuation 16751055 · Jan 23, 2020
Continuation 15818158 · Nov 20, 2017
Related Publication 20230124329A1 · Apr 20, 2023
References Cited (26)
US 6782143B1 · Dube et al. · 2004 [cited by applicant]
US 7577313B1 · Georgiev · 2009 [cited by examiner]
US 9111378B2 · Chui et al. · 2015 [cited by applicant]
US 9117285B2 · Clemie et al. · 2015 [cited by applicant]
US 9729899B2 · Tourapis et al. · 2017 [cited by applicant]
US 10580117B2 · Chen · 2020 [cited by applicant]
US 10997694B2 · Chen · 2021 [cited by applicant]
US 20040036687A1 · Nakamura et al. · 2004 [cited by applicant]
US 20050111739A1 · Ida · 2005 [cited by examiner]
US 20090129630A1 · Gloudemans et al. · 2009 [cited by applicant]
US 20110032251A1 · Pothana · 2011 [cited by applicant]
US 20120086850A1 · Irani et al. · 2012 [cited by applicant]
US 20120134588A1 · Zhang · 2012 [cited by examiner]
US 20130044965A1 · Chien et al. · 2013 [cited by applicant]
US 20140300599A1 · Tuzel · 2014 [cited by examiner]
US 20150093015A1 · Liang et al. · 2015 [cited by applicant]
US 20170200304A1 · Li · 2017 [cited by applicant]
US 20170347125A1 · Tourapis et al. · 2017 [cited by applicant]
US 20170359586A1 · Xue et al. · 2017 [cited by applicant]
US 20180144535A1 · Ford · 2018 [cited by examiner]
US 20180144547A1 · Shakib et al. · 2018 [cited by applicant]
US 20180286106A1 · Apodaca et al. · 2018 [cited by applicant]
US 20180350036A1 · VanReenen et al. · 2018 [cited by applicant]
US 20200364878A1 · Bradski et al. · 2020 [cited by applicant]
US 20220058407A1 · Yang et al. · 2022 [cited by applicant]
US 20220130032A1 · Bendall et al. · 2022 [cited by applicant]