Granted Patent B2

US 12,219,208 · App. 18/470,173 · Granted Feb 4, 2025

Inventors: Chanwon Seo (Suwon-si, KR); Yehoon Kim (Suwon-si, KR); Sojung Yun (Suwon-si, KR)

Assignee: SAMSUNG ELECTRONICS CO., LTD.

H04N21/440272G06N3/08G06V20/41G06V20/46H04N21/44008H04N21/4666

Abstract

Claims (60)

1. A server, comprising:

a memory storing one or more instructions; and

at least one processor configured to execute the one or more instructions to:

receive, from a device, identification information of a first video to be displayed in a display area of the device and information regarding an aspect ratio of the display area of the device;

compare an aspect ratio of the first video with the aspect ratio of the display area of the display;

determine a category of the first video based on at least one frame included in the first video, based on the aspect ratio of the first video being different from the aspect ratio of the display area;

select a trained artificial intelligence (AI) model related to the category of the first video;

obtain an expanded video corresponding to all frames of the first video by applying a reference frame included in the first video to the trained AI model, the expanded video having an aspect ratio corresponding to the aspect ratio of the display area; and

transmit the expanded video to the device so that the expanded video is displayed in the display area of the device,

wherein the trained AI model is generated by using the reference frame and at least one related frame associated with the reference frame as training data.

2. The server of claim 1 , wherein the at least one processor is further configured to execute the one or more instructions to identify a letterbox to be displayed in the display area based on the aspect ratio of the first video being different from the aspect ratio of the display area.

3. The server of claim 1 , wherein the at least one processor is further configured to execute the one or more instructions to:

extract frames included in the first video;

generate the training data to be used to generate the trained AI model based on the extracted frames; and

obtain the expanded video by updating the trained AI model by inputting the training data to the trained AI model.

4. The server of claim 1 , wherein the at least one processor is further configured to execute the one or more instructions to generate the trained AI model by using the reference frame and a resized frame obtained by resizing the reference frame as the training data.

5. The server of claim 1 , wherein the at least one processor is further configured to execute the one or more instructions to:

extract the reference frame, at least one previous frame, and at least one next frame included in the first video; and

generate the trained AI model by using the reference frame, the at least one previous frame, and the at least one next frame as the training data.

6. The server of claim 1 , wherein the at least one processor is further configured to execute the one or more instructions to:

transmit a list comprising identification information of a plurality of videos to the device; and

receive, from the device, the identification information of the first video as the first video is selected from the list.

7. The server of claim 1 , wherein the at least one processor is further configured to execute the one or more instructions to generate frames of the expanded video corresponding to all frames of the first video by training the trained AI model by inputting the reference frame, at least one previous frame, and at least one next frame, to the trained AI model.

8. The server of claim 1 , wherein the at least one processor is further configured to execute the one or more instructions to:

detect at least one of a pattern and a color constituting the reference frame included in the first video;

search for an image related to the detected at least one of the pattern and the color; and

generate the trained AI model by using the reference frame and the searched image as the training data.

9. The server of claim 1 , wherein the at least one processor is further configured to execute the one or more instructions to:

identify an object included in the reference frame of the first video; and

determine the category of the first video according to the object.

10. The server of claim 1 , wherein the category comprises at least one from among a science fiction (SF) movie, a documentary, a live performance, a two-dimensional (2D) animation, a three-dimensional (3D) animation, an augmented reality (AR) video, and a hologram video.

11. The server of claim 1 , wherein the aspect ratio of the display area varies as the device is folded or unfolded.

12. A method performed by a server, the method comprising:

receiving, from a device, identification information of a first video to be displayed in a display area of the device and information regarding an aspect ratio of the display area of the device;

comparing an aspect ratio of the first video with the aspect ratio of the display area in which the first video is to be displayed;

determining a category of the first video based on at least one frame included in the first video, based on the aspect ratio of the first video being different from the aspect ratio of the display area;

selecting a trained artificial intelligence (AI) model related to the category of the first video;

obtaining an expanded video corresponding to all frames of the first video by applying a reference frame included in the first video to the trained AI model, the expanded video having an aspect ratio corresponding to the aspect ratio of the display area; and

transmitting the expanded video to the device so that the expanded video is displayed in the display area of the device,

wherein the trained AI model is generated by using the reference frame and at least one related frame associated with the reference frame as training data.

13. The method of claim 12 , wherein the comparing the aspect ratio of the first video with the aspect ratio of the display area comprises identifying a letterbox to be displayed in the display area based on the aspect ratio of the first video being different from the aspect ratio of the display area.

14. The method of claim 12 , wherein the obtaining of the expanded video comprises:

extracting frames included in the first video;

generating the training data to be used to generate the trained AI model based on the extracted frames; and

obtaining the expanded video by updating the trained AI model by inputting the training data to the trained AI model.

15. The method of claim 12 , wherein the obtaining the expanded video comprises generating the trained AI model by using the reference frame and a resized frame obtained by resizing the reference frame as the training data.

16. The method of claim 12 , wherein the obtaining of the expanded video comprises:

extracting the reference frame, at least one previous frame, and at least one next frame included in the first video; and

generating the trained AI model by using the reference frame, the at least one previous frame, and the at least one next frame as the training data.

17. The method of claim 12 , wherein the obtaining of the expanded video comprises:

transmitting a list comprising identification information of a plurality of videos to the device; and

receiving, from the device, the identification information of the first video as the first video is selected from the list.

18. The method of claim 12 , wherein the obtaining of the expanded video comprises generating frames of the expanded video corresponding to all frames of the first video by training the trained AI model by inputting the reference frame, at least one previous frame, and at least one next frame, to the trained AI model.

19. The method of claim 12 , wherein the obtaining of the expanded video comprises:

detecting at least one of a pattern and a color constituting the reference frame included in the first video;

searching for an image related to the detected at least one of the pattern and the color; and

generating the trained AI model by using the reference frame and the searched image as the training data.

20. The method of claim 12 , wherein the determining the category of the first video further comprises:

identifying an object included in the reference frame of the first video; and

determining the category of the first video according to the object.

Priority Claims (1)

Continuity (3)

References Cited (49)