IP Library › Granted Patent US 12,561,772
Granted Patent B2
US 12,561,772 · App. 18/106,260 · Granted Feb 24, 2026

Display apparatus and operating method thereof

Inventors: Chun Zhao (Suwon-si, KR); Jonghwan Kim (Suwon-si, KR); Guiwon Seo (Suwon-si, KR); Younghoon Jeong (Suwon-si, KR)
Assignee: SAMSUNG ELECTRONICS CO., LTD.
G06T5/92G06T5/20G06T5/50G06T5/94G06V10/56G06V10/60G06T2207/10024G06T2207/20081G06T2207/20084G06T2207/30168
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Quick Facts
Patent No.
US 12,561,772
App. No.
18/106,260
Granted
Feb 24, 2026
Kind
B2
Abstract

Provided are a display apparatus and a method thereof. The display apparatus includes a display; a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory to: receive an input image, obtain luminance information from the input image, obtain, based on the luminance information, perceptual luminance information in view of a visual response, predict scene intensity information indicating a degree of a side effect that occurs after a first image quality processing is performed on the input image, and perform the first image quality processing on the input image based on the scene intensity information and the perceptual luminance information.

Claims (65)

1 . A display apparatus comprising:

a display;

a memory storing one or more instructions; and

a processor configured to execute the one or more instructions stored in the memory to:

receive an input image,

obtain luminance information from the input image,

obtain perceptual luminance information based on the luminance information,

predict scene intensity information indicating a probability of an occurrence of a side effect after a first image quality processing is performed on the input image, and

perform the first image quality processing on the input image based on the scene intensity information and the perceptual luminance information.

2 . The display apparatus of claim 1 , wherein the processor is further configured to execute the one or more instructions to:

obtain a focus level map based on the scene intensity information and the perceptual luminance information, and

perform the first image quality processing on the input image by using the obtained focus level map.

3 . The display apparatus of claim 2 , wherein the processor is further configured to execute the one or more instructions to obtain the focus level map by applying a weight to the perceptual luminance information by using the scene intensity information.

4 . The display apparatus of claim 3 , wherein the processor is further configured to execute the one or more instructions to:

obtain a first focus level map by filtering the perceptual luminance information by using a predetermined filter,

obtain a second focus level map by applying a weight to the first focus level map by using the scene intensity information, and

obtain the focus level map by normalizing the second focus level map.

5 . The display apparatus of claim 4 , wherein the predetermined filter includes at least one of a Laplacian filter, a gradient filter, a variance filter, or a wavelet filter.

6 . The display apparatus of claim 2 , wherein the processor is further configured to execute the one or more instructions to perform the first image quality processing by performing at least one of luminance contrast enhancement, color contrast enhancement, or detail enhancement on the input image by using the focus level map.

7 . The display apparatus of claim 6 , wherein the processor is further configured to execute the one or more instructions to:

obtain a global contrast-enhanced image by applying, to the input image, global tone mapping curves, which are obtained by adjusting dominant luminances of N focus levels of the focus level map by using different gains,

obtain a local contrast-enhanced image by applying, to the input image, N different tone mapping curves, which correspond to respective areas corresponding to the N focus levels of the focus level map, and

obtain an output image by mixing the global contrast-enhanced image with the local contrast-enhanced image.

8 . The display apparatus of claim 6 , wherein the processor is further configured to execute the one or more instructions to:

obtain a hue-based enhanced image by applying color enhancement gains to dominant hue values corresponding to N focus levels of the focus level map,

obtain a saturation-based enhanced image by applying saturation enhancement gains to dominant saturation values corresponding to the N focus levels of the focus level map, and

obtain an output image by mixing the hue-based enhanced image with the saturation-based enhanced image.

9 . The display apparatus of claim 6 , wherein the processor is further configured to execute the one or more instructions to obtain an output image by applying detail enhancement gains to focus level areas of the focus level map, respectively.

10 . The display apparatus of claim 1 , wherein the processor is further configured to execute the one or more instructions to obtain, from the input image, the luminance information based on characteristic information of the display.

11 . The display apparatus of claim 10 , wherein the processor is further configured to execute the one or more instructions to obtain the perceptual luminance information from the luminance information by using a human visual system (HVS) model.

12 . The display apparatus of claim 1 , wherein the processor is further configured to execute the one or more instructions to:

obtain, from the input image, the scene intensity information of the input image by using a neural network,

wherein the neural network is trained, based on training images, to output the scene intensity information indicating intensity of the side effect that occurs after a second image quality processing is performed on the training images.

13 . A method of a display apparatus, the method comprising:

receiving an input image;

obtaining luminance information from the input image;

obtaining perceptual luminance information based on the luminance information;

predicting scene intensity information indicating a probability of an occurrence of a side effect after a first image quality processing is performed on the input image; and

performing the first image quality processing on the input image based on the scene intensity information and the perceptual luminance information.

14 . The method of claim 13 , further comprising:

obtaining a focus level map based on the scene intensity information and the perceptual luminance information; and

performing the first image quality processing on the input image by using the focus level map.

15 . The method of claim 13 , wherein the obtaining, based on the luminance information, the perceptual luminance information further comprises obtaining the perceptual luminance information by using a human visual system (HVS) model.

16 . The method of claim 13 , further comprising:

obtaining, from the input image, the scene intensity information of the input image by using a neural network,

wherein, based on training images, the neural network is trained to output the scene intensity information indicating intensity of a side effect that occurs after a second image quality processing is performed on the training images.

17 . A computer-readable recording medium having recorded thereon a program for performing a method of a display apparatus, the method comprising:

receiving an input image;

obtaining luminance information from the input image;

obtaining perceptual luminance information based on the luminance information;

predicting scene intensity information indicating a probability of an occurrence of a side effect that occurs after a first image quality processing is performed on the input image; and

performing the first image quality processing on the input image based on the scene intensity information and the perceptual luminance information.

18 . An image processing apparatus comprising:

a memory storing one or more instructions and including a scene intensity prediction module, a focus level map generation module, and a perspective enhancement module; and

a processor configured to execute the one or more instructions to instruct:

the scene intensity prediction module to receive pixels of an input image and generate scene intensity information,

the focus level map generation module to receive the pixels of the input image, receive the scene intensity information, generate a focus level map about the pixels of the input image at least based on the scene intensity information and the pixels of the input image, and

the perspective enhancement module to receive the focus level map from the focus level map generation module and generate an output image,

wherein:

the focus level map includes normalized values of focus response values for the pixels of the input image, and

the focus response values correspond to the pixels and indicates degrees of sensitivity of human visual responses to the pixels of the input image.

19 . The image processing apparatus of claim 18 , wherein:

the output image includes at least one pixel in the pixels of the input image, based on the focus level map received from the focus level map generation module, and

the at least one pixel included in the output image is enhanced by an enhancement processing performed by the perspective enhancement module.

20 . The image processing apparatus of claim 19 , wherein the enhancement processing is at least one of inter-level global contrast enhancement, intra-level local contrast enhancement, hue-based enhancement, saturation-based enhancement, or detail enhancement.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 6, 2023
From: ZHAO, CHUN; KIM, JONGHWAN; SEO, GUIWON; JEONG, YOUNGHOON
To: SAMSUNG ELECTRONICS CO., LTD.
Reel/Frame 062683/0415 →
Priority Claims (1)
KR 10-2021-0155161 · Nov 11, 2021 · national
Continuity (2)
Continuation PCTKR2022017793 · Nov 11, 2022
Related Publication 20230186440A1 · Jun 15, 2023
References Cited (33)
US 8280184B2 · Chen et al. · 2012 [cited by applicant]
US 8406559B2 · Wu · 2013 [cited by applicant]
US 9135865B2 · Yang et al. · 2015 [cited by applicant]
US 9390482B2 · Lee et al. · 2016 [cited by applicant]
US 10963676B2 · Wang · 2021 [cited by examiner]
US 11410283B2 · Jeong et al. · 2022 [cited by applicant]
US 11481586B2 · Tai et al. · 2022 [cited by applicant]
US 20070230788A1 · Lei · 2007 [cited by applicant]
US 20080170783A1 · Yoo et al. · 2008 [cited by applicant]
US 20130083248A1 · Suzuki · 2013 [cited by applicant]
US 20170116963A1 · Wanat · 2017 [cited by examiner]
US 20210073954A1 · Mantiuk et al. · 2021 [cited by applicant]
US 20210150676A1 · Sytnik · 2021 [cited by examiner]
US 20210158077A1 · Tai · 2021 [cited by examiner]
US 20210166604A1 · Lim et al. · 2021 [cited by applicant]
US 20230066869A1 · Kim · 2023 [cited by examiner]
EP 3825955A1 · 2021 [cited by applicant]
KR 1020160035192A · 2016 [cited by applicant]
KR 1020160140366A · 2016 [cited by applicant]
KR 101767094B1 · 2017 [cited by applicant]
KR 20190027131A · 2017 [cited by examiner]
KR 1020190027131A · 2019 [cited by applicant]
KR 1020200031470A · 2020 [cited by applicant]
KR 1020210062477A · 2021 [cited by applicant]
WO 2021085679A1 · 2021 [cited by applicant]
Kundu et al., No-Reference Quality Assessment of Tone-Mapped HDR Pictures, IEEE Transactions on Image Processing, vol. 26, No. 6, Jun. 2017 2957 (Year: 2017). [cited by examiner]
Jiang et al., Quantitative Measurement of Perceptual Attributes and Artifacts for Tone-Mapped HDR Display, IEEE Transactions on Instrumentation and Measurement, vol. 71, 2022 5014511 (Year: 2022). [cited by examiner]
Yang et al., An Adaptive Method for Image Dynamic Range Adjustment, IEEE Transactions on Circuits and Systems for Video Technology, vol. 29, No. 3, Mar. 2019 (Year: 2019). [cited by examiner]
Maragatham et al., “Contrast Enhancement by object based Histogram Equalization,” 2011 World Congress on Information and Communication Technologies, pp. 1122-1126, Dec. 2011, Total 6 pages. [cited by applicant]
Sciencedirect, “Photoreceptors,” From: Biomechatronics, 2019, Total 8 pages. [cited by applicant]
Communication dated Feb. 20, 2023, issued by the International Searching Authority in counterpart International Application No. PCT/KR2022/017793 (PCT/ISA/220, PCT/ISA/210, and PCT/ISA/237). [cited by applicant]
Björn Ottosson, “A perceptual color space for image processing”, Dec. 23, 2020, 14 pages, https://bottosson.github.io/posts/oklab/. [cited by applicant]
European Extended Search Report issued Nov. 5, 2024 by the European Patent Office for EP Patent Application No. 22893284.4. [cited by applicant]