KR20220037617A - Method and apparatus for video processing using edge computing service - Google Patents

Abstract

The present invention relates to a method for converting equirectangular projection (ERP) images to convert images corresponding to frames in an ERP video and an edge data network for converting ERP images. According to the present invention, a video processing apparatus comprises a network interface, a memory storing at least one instruction, and a processor executing the at least one instruction. The processor acquires an ERP image; divides the ERP image into a plurality of main images and a plurality of sub-images; downscales a partial area of each of the plurality of main images; downscales the entire area of each of the plurality of sub-images; and generates a converted ERP image by rearranging the downscaled plurality of main images and the downscaled plurality of sub-images, wherein the converted ERP image has a rectangular shape of a preset size.

Description

Video processing method and device using edge computing service {METHOD AND APPARATUS FOR VIDEO PROCESSING USING EDGE COMPUTING SERVICE}

Various embodiments relate to a video processing method and apparatus using an edge computing service (eg, a multi-access edge computing (MEC) service).

Recently, an edge computing technology for transmitting data using an edge server is being discussed. Edge computing technology may include, for example, multi-access edge computing (MEC) or fog computing (FOC). Edge computing technology provides data to an electronic device through a location geographically close to the electronic device, for example, a separate server installed inside or near the base station (hereinafter referred to as 'edge data network' or 'MEC server') It can mean the skill to For example, an application requiring low latency among at least one application installed in the electronic device is located in a geographically close location without going through a server located in an external data network (DN) (eg, the Internet). Data can be sent and received through the installed edge server.

Recently, services using edge computing technology (hereinafter referred to as 'MEC-based services' or 'MEC services') are being discussed, and research and development on electronic devices to support MEC-based services are in progress. For example, an application of the electronic device may transmit/receive edge computing-based data to and from an edge server (or an application of the edge server) on an application layer.

As research and development for supporting MEC-based services progress, technologies for providing moving images to electronic devices using MEC are being discussed.

Various embodiments relate to a video processing method and an apparatus for converting images corresponding to frames in an ERP (Equirectangular Projection) video using an edge computing service.

A method for an edge data network to convert an ERP (Equirectangular projection) image according to an embodiment includes acquiring an ERP image, dividing the ERP image into a plurality of main images and a plurality of sub images, the plurality of downscaling a partial region of each of the main images of to generate a converted ERP image, the converted ERP image may have a rectangular shape of a preset size.

The edge data network for converting an ERP image according to an embodiment includes a network interface, a memory for storing one or more instructions, and a processor for executing the one or more instructions, wherein the processor acquires the ERP image, and the ERP dividing an image into a plurality of main images and a plurality of sub images, downscaling a partial area of each of the plurality of main images, downscaling an entire area of each of the plurality of sub images, and the downscaling A converted ERP image is generated by rearranging the plurality of main images and the plurality of downscaled sub-images, and the converted ERP image may have a rectangular shape of a preset size.

A video processing method and apparatus according to an embodiment acquires an ERP video, classifies a plurality of areas in an image corresponding to a frame in the ERP video, and applies different video processing for each area to reduce image size to increase transmission efficiency , relates to a video processing method and apparatus capable of providing a user with a video having a smooth resolution change between a plurality of regions divided in an image.

1 is a diagram for explaining a multi-access edge computing (MEC) technology in a network environment according to an embodiment.
FIG. 2 is a diagram for explaining a method of generating a converted ERP image by an edge data network and streaming it to an electronic device according to an exemplary embodiment.
3 is a flowchart illustrating a method of generating a converted ERP image by an edge data network according to an embodiment.
4A to 4E are diagrams for further explaining a method of generating an ERP image converted by the edge data network according to the embodiment described with reference to FIG. 3 .
FIG. 5 is a diagram for explaining a method for an edge data network to reduce a resolution by downscaling a partial region of each of a plurality of main images, according to an embodiment.
FIG. 6 is a diagram for explaining a method for an edge data network to downscale a partial area of each of a plurality of main images to reduce a resolution in a step shape according to an exemplary embodiment;
7 is a diagram for explaining that a predetermined value K is changed when the edge data network downscales a partial region of each of a plurality of main images according to an embodiment.
8 is a diagram for explaining a main area within an ERP image.
9 is a diagram for explaining a step of classifying a plurality of main images in an ERP image based on gaze information obtained from an electronic device connected to an edge data network and an edge data network according to an embodiment.
FIG. 10 is a diagram for describing a method for an edge data network to determine resolutions of a first main image and a second main image according to an embodiment.
11 is a diagram for explaining the configuration of an edge data network and an electronic device.
FIG. 12 relates to the edge data network and the electronic device shown in FIG. 11 , wherein the edge data network receives sensor information from the electronic device, generates a converted ERP image based on the received sensor information, and transmits it to the electronic device It is a flowchart showing the operations to be performed.

Terms used in this specification will be briefly described, and an embodiment of the present invention will be described in detail.

The terms used in this specification have been selected as currently widely used general terms as possible while considering the functions in the present invention, but these may vary depending on the intention or precedent of a person skilled in the art, the emergence of new technology, and the like. In addition, in a specific case, there is a term arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the corresponding invention. Therefore, the terms used in this specification should be defined based on the meaning of the term and the overall content of the present invention, rather than the name of a simple term.

In the entire specification, when a part "includes" a certain element, this means that other elements may be further included, rather than excluding other elements, unless otherwise stated. In addition, terms such as "...unit" and "module" described in the specification mean a unit that processes at least one function or operation, which may be implemented as hardware or software, or a combination of hardware and software. .

Hereinafter, with reference to the accompanying drawings, the embodiments of the present invention will be described in detail so that those of ordinary skill in the art can easily implement them. However, the present invention may be embodied in several different forms and is not limited to the embodiments described herein. And in order to clearly explain the present invention in the drawings, parts irrelevant to the description are omitted, and similar reference numerals are attached to similar parts throughout the specification.

1 is a diagram for explaining a multi-access edge computing (MEC) technology in a network environment according to an embodiment.

Referring to FIG. 1 , the network environment 100 of the present disclosure may include an electronic device 1000 , an edge data network 2000 , a cloud server 3000 , and an access network (AN, 1100 ). However, the configuration included in the network environment 100 is not limited thereto.

According to an embodiment, each of the components included in the network environment 100 may mean a physical entity unit or a software or module unit capable of performing an individual function.

According to an embodiment, the electronic device 1000 may mean a device used by a user. For example, the electronic device 1000 may include a terminal, a user equipment (UE), a mobile station, a subscriber station, a remote terminal, and a wireless terminal. , or a user device.

In addition, the electronic device 1000 allows a user to be immersed in a virtual environment including at least one of virtual reality (VR), augmented reality (AR), and mixed reality (MR). In order to do this, it may be a terminal that provides content. That is, according to an embodiment, the electronic device 1000 is a head mounted display (HMD) or a virtual reality headset (VRH) that provides content for virtual reality, augmented reality, or mixed reality. can

Referring to FIG. 1 , the electronic device 1000 includes a first application client (or application client) 122 , a second application client 124 , and an edge enabler client (or MEL (MEC) enabling layer) 130 . The electronic device 110 may perform a necessary operation by using the edge enabler client 130 to use the MEC service. For example, the edge enabler client 130 may be used to search for an application and supply required data to the application.

According to an embodiment, the electronic device 1000 may execute a plurality of applications. For example, the electronic device 1000 may execute the first application client 122 and the second application client 124 . A plurality of applications may include a required data transmission rate, delay time (or latency), reliability, number of electronic devices accessing a network, a network access period of the electronic device 110 , or average data. Different network services may be required based on at least one of usage. The different network services may include, for example, enhanced mobile broadband (eMBB), ultra-reliable and low latency communication (URLLC), or massive machine type communication (mMTC).

The application client of the electronic device 1000 may refer to a basic application pre-installed in the electronic device 1000 or an application provided by a third party. That is, it may mean a client application program that is run in the electronic device 1000 for a specific application service. Several application clients may be driven in the electronic device 1000 . At least one of these application clients may use a service provided from the edge data network 2000 . For example, the application client is an application installed and executed in the electronic device 1000 , and may provide a function of transmitting and receiving data through the edge data network 2000 . The application client of the electronic device 1000 may refer to application software executed on the electronic device 1000 to use a function provided by one or more specific edge applications.

According to an embodiment, the plurality of applications 122 and 124 of the electronic device 1000 perform data transmission with the cloud server 3000 or the edge data network 2000 based on a required network service type. Data transmission based on edge computing can be performed. For example, if the first application client 122 does not request a low delay time, the first application client 122 may perform data transmission with the cloud server 3000 . As another example, when the second application client 124 requests a low latency, the second application client 124 may perform MEC-based data transmission with the edge data network 2000 .

According to an embodiment, the application of the electronic device 1000 may be referred to as an application client, a client application, or a UE application. For convenience of description, an application of the electronic device 1000 will be referred to as an application client hereinafter.

According to an embodiment, the access network 1100 may provide a channel for wireless communication with the electronic device 1000 . For example, the access network 1100 is a radio access network (RAN), a base station, an eNodeB (eNB, eNodeB), a 5G node (5G node), a transmission/reception point (TRP, transmission/reception point), or It may mean 5th generation NodeB (5GNB).

According to an embodiment, the edge data network 2000 may refer to a server to which the electronic device 1000 accesses to use the MEC service. The edge data network 2000 may be installed in a location geographically close to the electronic device, for example, inside or near the base station.

According to an embodiment, the edge data network 2000 may transmit/receive data to and from the electronic device 1000 without using an external data network (DN) (eg, the Internet). In one embodiment, MEC may be referred to as multi-access edge computing or mobile-edge computing.

According to an embodiment, the edge data network 2000 may include an MEC host, an edge computing server, a mobile edge host, an edge computing platform, a MEC server, etc. may be referred to as For convenience, the MEC server will be referred to as an edge data network 2000 hereinafter.

Referring to FIG. 1 , the edge data network 2000 includes a first edge application 142 , a second edge application 144 , and an edge enabler server (or MEP (MEC platform)) 146 . ) may be included. The edge enabler server 146 is a configuration that provides MEC service or performs traffic control in the edge data network 2000, and the edge enabler server 146 provides application-related information (eg, of the application). Availability/enablement may be provided to the edge enabler client 130 .

According to an embodiment, the edge data network 2000 may execute a plurality of applications. For example, the edge data network 2000 may execute a first edge application 142 and a second edge application 144 . According to an embodiment, the edge application may mean an application application provided by a third party in the edge data network 2000 that provides the MEC service, and may also be referred to as an edge application. The edge application may be used to establish a data session with the application client in order to transmit and receive data related to the application client. That is, an edge application can establish a data session with an application client.

According to an embodiment, the data session may refer to a communication path formed in order to transmit and receive data between the application client of the electronic device 1000 and the edge application of the edge data network 2000 .

According to an embodiment, the application of the edge data network 2000 may be referred to as a MEC application, an edge application server, and an edge application. For convenience, hereinafter, the application of the edge data network 2000 is referred to as an edge application in the present disclosure. In this case, although described as an application, the edge application may mean an application server existing in the edge data network.

According to an embodiment, the cloud server 3000 may provide content related to an application. For example, the cloud server 3000 may be managed by a content provider. According to an embodiment, the cloud server 3000 may transmit/receive data to and from the electronic device 1000 through an external data network (DN) (eg, the Internet).

Although not shown in FIG. 1 , a core network (CN) and a data network (DN) may exist between the access network 1100 and the edge data network 2000 .

According to an embodiment, the data network transmits and receives data (or data packets) to and from the electronic device 1000 through the core network and the access network 1100 to provide a service (eg, an Internet service, an IP multimedia subsystem (IMS) service) can provide For example, the data network may be managed by a carrier. According to an embodiment, the edge data network 2000 may be connected to the access network 1100 or the core network through a data network (eg, a local DN).

According to an embodiment, when the first application client 122 or the second application client 124 is executed in the electronic device 1000 , the electronic device accesses the edge data network 2000 through the access network 1100 . By doing so, data for executing the application client can be transmitted/received.

According to the disclosed embodiments, a video processing method and a streaming method between the above-described electronic device 1000 , the edge data network 2000 and the cloud server 3000 may be provided. In more detail, the edge data network 2000 relates to a method of processing a video to be played on the electronic device and user interaction information (eg, gaze information, motion information) with respect to the video played on the electronic device 1000 . Based on this, a method for effectively providing a user with content for virtual reality, augmented reality or mixed reality is described.

FIG. 2 is a diagram for explaining a method in which the edge data network 2000 generates a converted ERP image and streams it to an electronic device according to an exemplary embodiment.

Referring to FIG. 2 , the electronic device 1000 according to an embodiment may acquire sensor information through a sensor unit. The sensor information obtained by the electronic device 1000 may include, but is not limited to, location tracking information related to a user's location, motion information related to a user's movement, and gaze information related to a user's gaze. The electronic device 1000 periodically transmits the sensor information acquired through the sensor unit to the edge data network 2000 or aperiodically transmits the sensor information to the edge data network 2000 only when there is a change in the sensed value of the sensor information. can be transmitted

The edge data network 2000 according to an embodiment may acquire an ERP video converted from a 360° sphere video (Equirectangular Projection Video). The edge data network 2000 determines main images and sub images for each of the plurality of ERP images corresponding to the plurality of frames of the obtained ERP video based on the received sensor information, and for each image By performing downscaling, a converted ERP image can be generated. Hereinafter, for convenience of description, the disclosed embodiments will be described by taking one ERP image as an example among a plurality of images corresponding to a plurality of frames included in the ERP video. Also, in the disclosed embodiments, a method of generating the converted ERP will be described based on a monocular image among images provided to both eyes of the user from the electronic device.

The edge data network 2000 according to an embodiment may determine an area determined to be an important part in the ERP image 210 as a main area based on sensor information. For example, a field of view (FOV) area, which is an area included in the user's field of view, may be determined as the main area based on sensor information. Also, the edge data network 2000 may determine an area having a lower importance than the main area and adjacent to the main area as a sub area.

The edge data network 2000 according to an embodiment includes a first main image 222 corresponding to a top area of the main area and a second main image corresponding to a bottom area of the main area from the determined main area. In step 224 , a first sub-image 226 corresponding to a sub-region adjacent to the left side of the main region and a second sub-image 228 corresponding to a sub-region adjacent to the right side of the main region may be determined.

The edge data network 2000 according to an embodiment includes a partial region of the first main image 222 , a partial region of the second main image 224 , the first sub-image 226 , and the second sub-image 228 . Scaling that reduces the resolution of the entire area may be performed. 2 , by scaling a portion of an upper region of the first main image 222 , the horizontal resolution may decrease toward the upper end of the first partial region. In addition, by scaling a portion of the lower portion of the second main image 224 , the horizontal resolution may decrease toward the lower portion of the second partial area. Also, the resolution may be reduced by scaling the entire area of the first sub-image 226 and the second sub-image 228 .

The edge data network 2000 according to an embodiment is a converted ERP image 230 by rearranging pixels constituting the scaled first main image, the scaled second main image, the scaled first sub-image, and the second sub-image. ) can be created. The edge data network 2000 may generate encoded ERP image information by encoding the converted ERP image 230 , and may transmit the encoded ERP image information to the electronic device 1000 .

The electronic device 1000 may decode the encoded ERP image information to obtain a converted ERP image, render the converted ERP image as a 360° spherical image, and then display main images in the user's FOV area.

The edge data network 2000 reduces the image size required for rendering by generating the converted ERP image, and reduces the resolution degradation felt by the user by reducing the horizontal resolution of the main images displayed in the main area toward the top or bottom, respectively. can do it

3 is a flowchart illustrating a method of generating a converted ERP image by the edge data network 2000 according to an embodiment.

In step S310 , the edge data network 2000 may acquire the ERP image in order to convert the ERP (Equirectangular Projection) image. The edge data network 2000 according to an embodiment may acquire the ERP image from the cloud server 3000 or other external device. Also, the edge data network 2000 may acquire a sphere image from the cloud server 3000 or other external device, and convert the acquired sphere image to acquire an ERP image.

In step S320 , the edge data network 2000 may divide the obtained ERP image into a plurality of main images and a plurality of sub images.

According to an embodiment, the plurality of main images may be images located in the main area of the obtained ERP image. Also, the plurality of sub-images may be images indicating sub-regions adjacent to the main area of the obtained ERP image.

The edge data network 2000 according to an embodiment may obtain gaze information related to the user's gaze position from the electronic device 1000 connected to the edge data network 2000 . The edge data network 2000 may convert the obtained ERP image so that the FOV area becomes the main area of the ERP image based on the obtained gaze information. In addition, the edge data network 2000 may convert the obtained spherical image into an ERP image so that the FOV area becomes the main area of the ERP image based on the obtained gaze information.

In the method for the edge data network 2000 according to an embodiment to distinguish a plurality of main images and a plurality of sub-images in the obtained ERP image, the edge data network 2000 determines the main area of the obtained ERP image. It may be divided into a first main image and a second main image which are vertically adjacent, but is not limited thereto. In addition, the edge data network 2000 may classify sub-images adjacent to the left sub-region and the right sub-region of the main region of the obtained ERP image, respectively, into a first sub image and a second sub image, but is not limited thereto. .

In operation S330 , the edge data network 2000 may reduce the resolution of each of the plurality of main images by downscaling a partial region of each of the plurality of main images.

For example, as described in step S320 , the edge data network 2000 may divide the upper end of the main region into a first main image and the lower end of the main region into a second main image.

The edge data network 2000 according to an embodiment may reduce the resolution of the first main image by downscaling an upper region that is a partial region of the first main image. The edge data network 2000 may increase the downscaling degree so that the horizontal resolution is further reduced as it goes toward the top of the first main image, so that the horizontal resolution of the first main image decreases as it goes toward the top. Also, the downscaled shape of the partial region of the first main image may be a trapezoid, but is not limited thereto, and the shape of the downscaled partial region of the first main image increases toward the top of the partial region of the first main image. With a larger width at which the horizontal resolution is reduced, it may become a triangle.

The edge data network 2000 according to an embodiment may reduce the resolution of the second main image by downscaling a lower region that is a partial region of the second main image. The edge data network 2000 may increase the downscaling degree to further decrease the horizontal resolution toward the bottom of the second main image, so that the horizontal resolution of the second main image decreases toward the bottom of the edge data network 2000 . In addition, the downscaled shape of the partial region of the second main image may be a trapezoid, but is not limited thereto, and the shape of the downscaled partial region of the second main image increases toward the bottom of the partial region of the second main image. It can also be a triangle with a larger width at which the horizontal resolution is reduced.

The edge data network 2000 according to an embodiment reduces the resolution of a part of the first main image and a part of the second main image located in the main area, so that the user's gaze is located above or below the center of the ERP image. It is possible to reduce the degree of image quality degradation perceived by the user when heading to . A detailed method for the edge data network 2000 to downscale a partial region of each of the plurality of main images will be described in detail in the description of FIGS. 4B and 4C .

In operation S340 , the edge data network 2000 may reduce the resolution of each of the plurality of sub images by downscaling the entire area of each of the plurality of sub images.

For example, as described in step S320, the edge data network 2000 may classify a sub-image adjacent to the left of the main images into a first sub-image, and a sub-image adjacent to the right of the main images into a second sub-image. there is.

The edge data network 2000 according to an embodiment may reduce the resolution of the first sub-image and the second sub-image by downscaling the entire area of the first sub-image and the entire area of the second sub-image by a predetermined ratio, respectively. there is. The shapes of the downscaled first and second sub-images may be quadrangular. A specific method for the edge data network 2000 to downscale a partial region of each of the plurality of sub-images will be described in detail in the description of FIG. 4D .

In step S350 , the edge data network 2000 may generate a converted ERP image by rearranging the plurality of downscaled main images and the plurality of downscaled sub images.

For example, one side of the downscaled first main image and one side of the downscaled second main image are arranged to be adjacent to each other, and each of the downscaled first subimage and the downscaled second subimage is down It may be arranged to be adjacent to both the scaled first main image and the downscaled second main image.

Hereinafter, the method of generating a converted ERP image by rearranging the plurality of downscaled main images and the plurality of downscaled sub-images by the edge data network 2000 is further described with reference to FIGS. 4A to 4C. to describe

4A to 4E are diagrams for further explaining a method of generating a converted ERP image by the edge data network 2000 according to an embodiment described with reference to FIG. 3 .

4A is a diagram for explaining that the edge data network 2000 according to an embodiment divides the ERP image 400 into a plurality of main images and a plurality of sub images.

Referring to FIG. 4A , the edge data network 2000 may divide the acquired ERP image 400 into a main region and sub-regions adjacent to the main region. In this case, the main images may be images located in the main area 402 . Also, the sub-images may be images located in the sub-regions 404 and 406 adjacent to the main area.

The edge data network 2000 includes the obtained ERP image 400, the upper area of the main area 402 as the first main image 410, the lower area of the main area 402 as the second main image 412, A left sub-region 404 of the main region 402 may be divided into a first sub-image 414 , and a right sub-region 406 of the main region 402 may be divided into a second sub-image 416 .

4B is a diagram for explaining a method of downscaling a partial area of each of a plurality of main images and downscaling an entire area of each of a plurality of sub images by the edge data network 2000 according to an embodiment.

Referring to FIG. 4B , the edge data network 2000 may downscale the first main image 410 , the second main image 412 , the first sub-image 414 , and the second sub-image 416 , respectively. there is. Also, the first main image 410 may include a partial region of the first main image and the remaining region of the first main image, and the second main image 412 includes a partial region of the second main image and the second main image. It can consist of the rest of the image. Hereinafter, for convenience of explanation, the partial region of the first main image is the first partial region 420 , the remaining region of the first main image is the first remaining region 422 , and the partial region of the second main image is the second The partial area 424 and the remaining area of the second main image will be referred to as a second remaining area 423 .

The first main image 410 may be divided into a first partial area 420 and a first remaining area 422 . The edge data network 2000 according to an embodiment may reduce the resolution by downscaling the first partial region 420 . When the edge data network 2000 downscales the first partial region 420 , the edge data network 2000 adjusts the downscaling degree so that the horizontal resolution is further reduced as the edge data network 2000 goes toward the top of the first partial region 420 . By enlarging it, the downscaled first partial region 421 may be generated. That is, the downscaled first main image may include the downscaled first partial area 421 and the first remaining area 422 . Also, the downscaled first partial region 421 may have a trapezoidal shape, but is not limited thereto.

Also, the second main image 412 may be divided into a second partial area 424 and a second remaining area 423 . The edge data network 2000 according to an embodiment may reduce the resolution by downscaling the second partial region 424 . When the edge data network 2000 downscales the second partial region 424 , the edge data network 2000 adjusts the downscaling degree so that the horizontal resolution is further reduced as it goes toward the bottom of the second partial region 422 . By enlarging it, the downscaled second partial region 425 may be created. That is, the downscaled second main image may include the downscaled second partial area 425 and the second remaining area 423 . Also, the downscaled second partial region 425 may have a trapezoidal shape, but is not limited thereto.

The edge data network 2000 according to an embodiment may reduce resolution by downscaling the entire area of the first sub-image 414 and the second sub-image 416 .

FIG. 4C is a diagram for explaining a method of scaling, by the edge data network 2000, a partial region of each of a plurality of main images at various ratios, according to an exemplary embodiment.

Referring to FIG. 4C , when downscaling a partial region of the first main image 410 , the edge data network 2000 may downscale a partial region of the first main image 410 at various ratios.

For example, as shown in 430 of FIG. 4C , the ratio of the height of the first remaining region 422 to the height of the first main image 410 and the first partial region to the width of the first main image 410 is The first main image 410 may be downscaled so that the ratio to the upper width is the same.

In another example, as shown in 432 of FIG. 4C , the ratio of the height of the first remaining region 422 to the height of the first main image 410 and the first partial region to the width of the first main image 410 are The first main image 410 may be downscaled so that the ratio to the upper width is different from each other.

When the edge data network 2000 downscales a partial area of the second main image 412 , the edge data network 2000 may also downscale a partial area of the second main image 412 at various ratios. In this case, when downscaling the second main image 412 , the edge data network 2000 may downscale the first main image 410 at the same rate as the downscaling rate. Also, the shape of the downscaled second main image 412 may be diagonally symmetrical with the shape of the downscaled first main image 410 . Since the edge data network 2000 downscaling the second main image 412 is the same as the edge data network 2000 downscaling the first main image 410, the description will be omitted. .

4D is a diagram for explaining a method of scaling the entire area of each of a plurality of sub-images by the edge data network 2000 according to an exemplary embodiment. In the description of FIG. 4D, the reference numerals of FIG. 4B will be used together.

Referring to FIG. 4D , the edge data network 2000 downscales the entire area of the first sub image 414 based on the downscaling ratio of the first main image 410 and the second main image 412 . can do. For example, as in the rearranged image 445 of FIG. 4D , when the trapezoidal hypotenuse portion of the downscaled first main image and the trapezoidal hypotenuse portion of the downscaled second main image are disposed adjacent to each other, edge data The network 2000 may downscale the entire area of the first sub-image 414 so that the down-scaled first sub-image 440 may be disposed in the empty space 442 of the rearranged image 445 . .

Also, the edge data network 2000 may downscale the entire area of the second sub-image 416 based on the down-scaling ratio of the first main image 410 and the second main image 412 . Since the method of downscaling the entire area of the second sub-image 416 is the same as the method of downscaling the entire area of the first sub-image 414 , a description thereof will be omitted.

4E is a diagram for explaining a method of rearranging a plurality of downscaled main images and a plurality of downscaled sub images by the edge data network 2000 according to an embodiment. In the description of FIG. 4E, the reference numerals of FIG. 4B will be used together.

The edge data network 2000 may generate a converted ERP image by rearranging the downscaled first main image, the downscaled second main image, the downscaled first main image, and the downscaled second sub-images. . The converted ERP image may have a preset rectangular shape.

Specifically, the edge data network 2000 may arrange the downscaled first main image and the downscaled second main image to be adjacent to each other. In this case, the edge data network 2000 may arrange the hypotenuses of the downscaled first partial region 421 in the trapezoid shape and the second partial region 425 in the trapezoid shape to be adjacent to each other.

Also, the edge data network 2000 may arrange the downscaled first sub-image and the downscaled second sub-image to be adjacent to both the downscaled first main image and the downscaled second main image.

As described above, the edge data network 2000 may generate a converted ERP image by rearranging a plurality of downscaled main images and a plurality of sub images. In this case, the converted ERP image may be configured in the form of the first batch 451 , the second batch 452 , the third batch 453 , and the fourth batch 454 , but is not limited thereto.

The edge data network 2000 according to an embodiment may reduce network delay time by generating a converted ERP image and transmitting the converted ERP having an image size smaller than the obtained ERP image to the electronic device 1000 . there is. In addition, the central portion of the image displayed in the main area does not reduce the resolution, and the resolution that decreases toward the top and bottom is linearly decreased, thereby reducing the resolution degradation felt by the user.

FIG. 5 is a diagram for explaining a method for the edge data network 2000 to reduce resolution by downscaling a partial region of each of a plurality of main images, according to an embodiment.

Referring to FIG. 5 , the edge data network 2000 does not scale the first remaining area 530 of the first main image 510, among those obtained by dividing the obtained ERP image into a plurality of areas, and The downscaled first partial area 520 may be generated in a trapezoidal shape by downscaling the partial area.

Specifically, the edge data network 2000 may downscale the pixels constituting the first partial area in a row unit. For example, the horizontal resolution may be reduced by downscaling the plurality of pixel rows constituting the first partial area at different ratios, respectively. The edge data network 2000, among a plurality of pixel rows constituting the first partial region, downscales a pixel row located at the closest distance to the first remaining region 530 with the smallest downscaling degree, 1 A pixel row located at the furthest distance from the remaining area 530 has the greatest downscaling degree, and the pixel row located at a farther distance from the first remaining area 530 has a greater downscaling degree. By performing downscaling in such a way as to increase the size of the image, the horizontal resolution of the first main image 510 may be decreased toward the upper region.

A method for the edge data network 2000 according to an exemplary embodiment to downscale the second main image to reduce the resolution is the same as the method for downscaling the first main image, and thus a description thereof will be omitted.

FIG. 6 is a diagram for explaining a method in which the edge data network 2000 downscales a partial area of each of a plurality of main images to reduce the resolution in a step shape according to an exemplary embodiment.

As described in FIG. 5 , the edge data network 2000 according to an embodiment does not scale the first remaining area 530 of the first main image 510, but downscales the first partial area to form a trapezoidal shape. A downscaled first partial region 520 may be generated. In this case, the edge data network 2000 may generate a plurality of pixel row groups by grouping a plurality of pixel rows constituting the first partial region. For example, a plurality of pixel groups may be created by grouping pixel rows. The edge data network 2000 may reduce horizontal resolution by downscaling each of the plurality of generated pixel row groups at different ratios. Accordingly, as shown in 610 of FIG. 6 , the hypotenuse portion of the trapezoidal shape may have a stepped shape.

The edge data network 2000 may group a plurality of pixel rows constituting the first partial region to include K pixel rows in each group based on a predetermined value K. In this case, the predetermined value K may be a preset value.

The edge data network 2000, among the plurality of pixel row groups, downscales a pixel row located at the closest distance to the first remaining area 530 with the smallest downscaling degree, and the first remaining area 530 . . By performing , the horizontal resolution of each of the plurality of pixel row groups included in the first main image 510 may be decreased toward the upper region of the first main image 510 .

A method for the edge data network 2000 according to an embodiment to downscale the second main image so that the resolution is reduced in a step shape is the same as the method for downscaling the first main image, and thus a description thereof will be omitted. . The edge data network 2000 groups a plurality of pixel rows constituting the first main image and the second main image, and performs downscaling for each pixel row group, thereby downgrading in units of one pixel or one pixel row. Downscaling can be performed at a faster speed by reducing the amount of computation compared to the case of scaling.

7 is a diagram for explaining that a predetermined value K is changed when the edge data network 2000 downscales a partial region of each of a plurality of main images according to an embodiment.

Since the method of downscaling the first partial region of the first main image and the second partial region of the second main image by the edge data network 2000 is the same, the following description will be based on the first partial region of the first main image. decide to do

Referring to FIG. 7 , when the edge data network 2000 downscales the first partial region of the first main image, K pixel rows for each pixel row group of a plurality of pixel rows constituting the first partial region The first partial region may be stair-stepped by grouping to include , and varying degrees of downscaling of each pixel row group. Here, stair stepping means making at least one side of the image of the first partial region with the reduced resolution in a stepped shape by decreasing the horizontal resolution toward the top of the first partial region.

For example, when the value of K is 10 as shown in 710 of FIG. 7 , the edge data network 2000 according to an exemplary embodiment divides a plurality of pixel rows constituting the first partial area into 10 pixel rows for each pixel row group. Grouping to include pixel rows, and varying degrees of downscaling of each pixel row groups, the first partial region may be stepped.

In another example, when the value of K is 20 as shown in 720 of FIG. 7 , the edge data network 2000 according to an embodiment divides a plurality of pixel rows constituting the first partial region of 20 for each pixel row group. Grouping to include pixel rows, and varying degrees of downscaling of each pixel row groups, the first partial region may be stepped.

The value of K according to an embodiment may be a preset value. In this case, the edge data network 2000 may group a plurality of pixel rows constituting the first partial region such that K pixel rows are included in each pixel row group.

The value of K according to an embodiment may be determined based on characteristic information of the obtained ERP image. The characteristic information of the ERP image may include, but is not limited to, content type information of the image, distribution information of pixel values included in the image, object information included in the image, and the like. For example, when the change in pixel values included in the upper region of the first main image or the lower region of the second main image is not large, such as when the upper region of the first main image is an image representing the sky, the value of K Even if is increased, the extent to which the image quality deteriorates toward the upper/lower regions of the image felt by the user may not be large. In this case, the edge data network 2000 may increase the K value. In another example, when the change in pixel values included in the upper/lower regions of the main images is large, the edge data network 2000 may decrease the K value.

The value of K according to an embodiment may be determined based on a load degree of the edge data network 2000 network. For example, when the amount of data currently processed by the edge data network 2000 is greater than the average throughput, the value of K may be increased to reduce the data processing time. In another example, when the amount of data currently processed by the edge network 2000 is less than the average throughput, the K value may be decreased to further reduce the resolution when downscaling the main images. The value of K according to an embodiment may be changed based on the changed load degree of the network as the network condition is changed in real time.

8 is a diagram for explaining a main area within an ERP image.

Referring to FIG. 8 , as the user of the electronic device 1000 moves his gaze, the main area of the ERP image provided to the user may be different from each other. For example, in FIG. 8 , an azimuth of 360° is set based on a predetermined reference point, and the user of the electronic device 1000 may gaze at a point of 180° in the azimuth. When the user of the electronic device 1000 gazes at a point having an azimuth angle of 180°, the main area of the ERP image provided to the user may be the first area 810 or the second area 820 of FIG. 8 . The first area 810 or the second area 820 representing the main area of the ERP image may be determined according to the FOV of the electronic device 1000 .

Also, according to the FOV of the electronic device 1000 , the size of the ERP image provided to the user may be different. For example, when the FOV of the electronic device 1000 is 90°, the ERP image provided to the user's main field of view may be the first area 810 having a size corresponding to a quarter of the entire ERP image. there is. In another example, when the FOV of the electronic device 1000 is 120°, the ERP image provided to the user's main field of view may be the second area 820 having a size corresponding to a third area of the entire ERP image. there is.

The edge data network 2000 according to an embodiment may acquire an ERP image or a spherical image in order to generate a converted ERP image.

The edge data network 2000 according to an embodiment obtains the user's gaze information, and, based on the obtained information, converts the obtained ERP image so that the FOV area the user gazes at becomes the main area of the ERP image. . In addition, the edge data network 2000 according to an embodiment may convert the obtained spherical image into an ERP image so that the FOV area that the user gazes at becomes the main area of the ERP image based on the obtained information.

The edge data network 2000 according to an embodiment divides the image indicating the main region of the ERP image into a plurality of main images, and divides the images indicating the sub regions adjacent to the main region of the ERP image into a plurality of sub images. can be distinguished

9 illustrates a step of classifying a plurality of main images in an ERP image based on gaze information obtained from the electronic device 1000 connected to the edge data network 2000 and the edge data network 2000 according to an embodiment. is a drawing for

Referring to FIG. 9 , the edge data network 2000 may obtain gaze information related to the gaze position of the user from the electronic device 1000 . The gaze information acquired by the edge data network 2000 may include information on the location of the user's pupil, information on the gaze point that is a point at which the user gazes at the display in the electronic device 1000 , FOV information of the electronic device 1000 , and the like. However, the present invention is not limited thereto.

The edge data network 2000 according to an embodiment may classify a plurality of main images and a plurality of sub-images in the ERP image based on the obtained gaze information.

For example, when the FOV of the electronic device 1000 is 120°, as shown in 910 of FIG. 9 , the edge data network 2000 has a size corresponding to a third of the entire ERP image, and the electronic device 1000 ) can be determined as the main area of the ERP image that the user gazes at.

The edge data network 2000 may divide the determined main region into a first main image 912 and a second main image 914 in the ERP image. In addition, the edge data network 2000 is located on the left side of the first main image 912 and the second main image 914, and provides an image indicating an area having a size corresponding to a third area of the entire ERP image. It can be divided into 1 sub-image 916 . In addition, an image pointing to an area located on the right side of the first main image 912 and the second main image 914 and having a size corresponding to a third area of the entire ERP image is used as the second sub image 918 . can be distinguished However, the criterion for discriminating the first sub-image 916 and the second sub-image 918 is not limited thereto, and the edge data network 2000 includes the first sub-image 916 and the second sub-image 918 . may be divided to have a size smaller than the size corresponding to one-third of the entire ERP image, respectively.

In another example, a method of distinguishing a first main image, a second main image, a first sub-image, and a second sub-image when the FOV of the electronic device 1000 is 90° will be described. When the FOV of the electronic device 1000 is 90°, as shown in 920 of FIG. 9 , the edge data network 2000 has a size corresponding to a quarter of the entire ERP image, and the user of the electronic device 1000 You can determine the area you are looking at as the main area of the ERP image. In addition, the edge data network 2000 according to an embodiment further acquires motion information related to the user's movement from the electronic device 1000 , and based on the obtained motion information of the gaze information, a plurality of main images in the ERP image , and a plurality of sub-images may be distinguished.

The edge data network 2000 may divide the determined main area into a first main image and a second main image in the ERP image. In addition, the edge data network 2000 is located on the left side of the first main image and the second main image, and an image pointing to an area having a size corresponding to a quarter area of the entire ERP image can be divided into a first sub image. there is. In addition, an image that is located on the right side of the first main image and the second main image and indicates a region having a size corresponding to a quarter region of the entire ERP image may be divided into a second sub-image. However, the criterion for classifying the first sub-image and the second sub-image is not limited thereto, and the edge data network 2000 applies the first sub-image and the second sub-image to a quarter of the entire ERP image, respectively. It may be classified to have a size larger or smaller than the corresponding size.

The edge data network 2000 according to an embodiment receives gaze information from the electronic device 1000 connected to the edge data network 2000 and divides the ERP image into a plurality of main images and a plurality of sub images based on the gaze information. And, by downscaling each image, a converted ERP image suitable for the FOV of the electronic device 1000 may be generated.

The edge data network 2000 may transmit the converted ERP image to the electronic device 1000 connected to the edge data network 2000 .

10 is a diagram for explaining a method of determining, by the edge data network 2000, resolutions of a first main image and a second main image according to an exemplary embodiment.

Referring to FIG. 10 , the resolutions of the first main image and the second main image may be determined based on a load degree of a network in which the edge data network 2000 is connected to the electronic device 1000 . For example, when the amount of data currently being processed by the network is large or data communication is not smooth, the resolutions of the first main image and the second main image may be determined as smaller resolutions based on this. As a result, when the edge data network 2000 classifies a plurality of main images in the ERP image, the first main image 1012 with reduced resolution and the second main image 1018 with reduced resolution may be distinguished. In addition, the edge data network 2000 is based on the resolution of the reduced resolution of the first main image 1012 and the reduced resolution of the second main image 1014, the first sub image 1016 and It can be divided into the second sub-image 1018 with reduced resolution. In this case, the edge data network 2000 downscales a partial area of each of the first main image 1012 and the second main image 1014 with reduced resolution, and each of the first sub images with reduced resolution ( 1016) and the entire area of the second sub-image 1018 may be downscaled, and a converted ERP image may be generated.

In addition, as the resolution of the first main image and the second main image is changed in real time when the network condition in which the edge data network 2000 and the electronic device 1000 are connected is changed, the resolution of the first main image and the second main image in a good network condition is determined. The main images are classified higher than when the resolution of the main image is higher than the resolution when the network condition is poor, and the resolutions of the first main image and the second main image when the network condition is bad are lower than the resolution when the network condition is good The main images can be distinguished.

The edge data network 2000 according to an embodiment downscales each of a plurality of main images and a plurality of sub-images having a smaller resolution, and generates a converted ERP image, thereby transforming having a smaller size according to network conditions. The ERP image may be transmitted to the electronic device 1000 .

11 is a diagram for explaining the configuration of the edge data network 2000 and the electronic device 1000 .

According to an embodiment, the electronic device 1000 may include a sensing unit 1110 , a network interface 1120 , a processor 1130 , and a memory 1140 . However, the configuration of the electronic device 1000 is not limited thereto, and the electronic device 1000 may include more or fewer configurations.

The electronic device 1000 may decode images received from the edge data network 2000 or the cloud server 3000 and display the decoded images on the display of the electronic device. Also, the electronic device 1000 may acquire sensor information including motion information and gaze information of a user on the reproduced images by using the sensing unit 111 .

The electronic device 1000 may transmit sensor information including motion information and gaze information to the edge data network 2000 using the network interface 1120 . In this case, the electronic device 1000 may transmit frame index information of an image when motion information and gaze information are sensed to the edge data network 2000 . Here, the frame index information of the image may be information indicating the encoding/decoding order of the image, but is not limited thereto, and may be information indicating the rendering order of the frame.

The processor 1130 may control the overall operation of the electronic device 1000 by executing one or more instructions in the memory 1140 . For example, the processor 1130 may control the sensing unit 1110 and the network interface 1120 by executing one or more instructions stored in the memory 1140 . According to an embodiment, the processor 1130 may receive the encoded converted ERP image from the edge data network 2000, decode the encoded converted ERP image, and restore the downscaled ERP image. The processor 1130 may render the downscaled ERP image to generate a spherical image, and display the generated spherical image on the display of the electronic device 1000 .

According to an embodiment, the memory 1140 may include a decoder module 1142 that stores instructions for decoding the encoded transformed ERP images received from the edge data network 2000, but is not limited thereto. not.

According to an embodiment, the edge data network 2000 may include a network interface 1160 , a processor 1170 , and a memory 1180 . However, the configuration of the edge data network 2000 is not limited thereto, and the edge data network 2000 may include more or fewer configurations.

The edge data network 2000 obtains sensor information including motion information and gaze information from the electronic device 1000 using the network interface 1160 , and the converted ERP encoded in the edge data network based on the sensor information, etc. The image may be transmitted to the electronic device 1000 .

The processor 1170 may control the overall operation of the edge data network 2000 by executing one or more instructions in the memory 1180 .

For example, the processor 1170 may acquire the ERP image in which the FOV area of the user is the main area of the ERP image based on at least one of motion information and gaze information. The processor 1170 divides the obtained ERP image into a plurality of main images and a plurality of sub images, downscales some areas of the plurality of main images, downscales all areas of the plurality of sub images, and downscales A converted ERP image may be obtained by rearranging the plurality of scaled main images and the plurality of downscaled sub images. The processor 1170 may encode the converted ERP image and transmit it to the electronic device 1000 .

According to an embodiment, the memory 1180 may include an encoder module 1182 that stores instructions for encoding the converted ERP images to be transmitted by the edge data network 2000 to the electronic device 1000 , but is limited thereto. it's not going to be

FIG. 12 relates to the edge data network and the electronic device shown in FIG. 11 . The edge data network 2000 receives sensor information from the electronic device 1000 and generates a converted ERP image based on the received sensor information. , and is a flowchart illustrating operations of transmitting to the electronic device 1000 .

In operation S1210, the electronic device 1000 may acquire sensor information. The sensor information obtained by the electronic device 1000 may include, for example, motion information related to a movement of the user of the electronic device 1000 and gaze information related to the user's gaze, but is not limited thereto. In addition, the gaze information may include, but is not limited to, information on the location of the user's pupil, information on the gaze point that is a point at which the user gazes at the display in the electronic device 1000 , information on the FOV of the electronic device 1000 , and the like.

In operation S1215 , the edge data network 2000 may receive sensor information from the electronic device 1000 .

In operation S1220 , the edge data network 2000 may acquire the ERP image based on the sensor information received from the electronic device 1000 . For example, the edge data network 2000 may acquire an ERP image in which the FOV area of the user is the main area of the ERP image based on at least one of motion information and gaze information included in the received sensor information. In addition, the spherical image is acquired, and the edge data network 2000 converts the acquired spherical image based on at least one of motion information and gaze information included in the received sensor information to convert the user's FOV area into the main ERP image. It is possible to acquire an ERP image as an area.

In steps S1230 to S1260, the edge data network 2000 divides the obtained ERP image into a plurality of main images and a plurality of sub images, downscales a partial region of each of the plurality of main images, and A converted ERP image may be generated by downscaling the entire area of each of the sub-images, and rearranging the plurality of downscaled main images and the plurality of downscaled sub-images. However, since the detailed description of the operation of steps S1230 to S1260 is the same as that described in steps S320 to S350 of FIG. 3 , the description thereof will be omitted.

In step S1270, the edge data network 2000 may encode the converted ERP image. The edge data network 2000 according to an embodiment may encode the converted ERP image generated in step S1260 using various conventional codecs (H.264, HEVC, VP9, AV1, etc.).

In operation S1275 , the edge data network 2000 may transmit the encoded and converted ERP image to the electronic device 1000 .

In step S1280 , the electronic device 1000 may decode the encoded converted ERP image and relocate the converted ERP image to restore the downscaled ERP image. The electronic device 1000 may generate a spherical image by rendering the downscaled ERP image, and may display the generated spherical image on the display of the electronic device 1000 .

The method for the edge data network to convert an ERP (Equirectangular projection) image according to an embodiment is implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the present invention, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like.

In addition, the method for the edge data network according to the disclosed embodiments to convert the ERP (Equirectangular projection) image may be provided included in a computer program product (computer program product). Computer program products may be traded between sellers and buyers as commodities.

The computer program product may include a S/W program and a computer-readable storage medium in which the S/W program is stored. For example, computer program products may include products (eg, downloadable apps) in the form of S/W programs distributed electronically through manufacturers of electronic devices or electronic markets (eg, Google Play Store, App Store). there is. For electronic distribution, at least a portion of the S/W program may be stored in a storage medium or may be temporarily generated. In this case, the storage medium may be a server of a manufacturer, a server of an electronic market, or a storage medium of a relay server temporarily storing a SW program.

The computer program product, in a system consisting of a server and a client device, may include a storage medium of the server or a storage medium of the client device. Alternatively, if there is a third device (eg, a smart phone) that is communicatively connected to the server or the client device, the computer program product may include a storage medium of the third device. Alternatively, the computer program product may include the S/W program itself transmitted from the server to the client device or the third device, or transmitted from the third device to the client device.

In this case, one of the server, the client device and the third device may execute the computer program product to perform the method according to the disclosed embodiments. Alternatively, two or more of a server, a client device, and a third device may execute a computer program product to distribute the method according to the disclosed embodiments.

For example, a server (eg, a cloud server or an artificial intelligence server) may execute a computer program product stored in the server to control a client device communicatively connected with the server to perform the method according to the disclosed embodiments.

Although the embodiments have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements by those skilled in the art using the basic concept of the present invention as defined in the following claims are also included in the scope of the present invention. belongs to

Claims (21)

In a method for an edge data network to transform an ERP (Equirectangular projection) image,
acquiring an ERP image;
dividing the ERP image into a plurality of main images and a plurality of sub images;
downscaling a partial region of each of the plurality of main images;
downscaling the entire area of each of the plurality of sub images;
Relocating the downscaled plurality of main images and the downscaled plurality of sub-images to generate a converted ERP image,
The converted ERP image will have a rectangular shape of a preset size, the method. According to claim 1,
The step of dividing the ERP image into a plurality of main images and a plurality of sub images,
The method of dividing the ERP image into a first main image, a second main image, a first sub-image and a second sub-image. 3. The method of claim 2,
The step of downscaling a partial region of each of the plurality of main images,
downscaling a partial region of the first main image and a partial region of the second main image;
Downscaling the entire area of each of the plurality of sub images comprises:
downscaling the entire area of the first sub-image and the entire area of the second sub-image;
The step of generating the converted ERP image is,
Relocating the downscaled first main image, the downscaled second main image, the downscaled first subimage and the downscaled second subimage to generate a converted ERP image . 4. The method of claim 3,
The step of downscaling a partial region of the first main image and a partial region of the second main image includes:
and downscaling the partial region of the first main image and the partial region of the second main image to a trapezoidal shape, respectively. 5. The method of claim 4,
Downscaling the entire area of the first sub-image and the entire area of the second sub-image comprises:
and downscaling the entire area of the first sub-image and the entire area of the second sub-image to a rectangular shape. 5. The method of claim 4,
A shape of the downscaled first main image and a shape of the downscaled second main image are diagonally symmetrical. 6. The method of claim 5,
The step of generating the converted ERP image is,
disposing so that the trapezoidal hypotenuse portion of the downscaled first main image and the trapezoidal hypotenuse portion of the downscaled second main image are adjacent to each other;
By disposing each of the downscaled first sub-image and the downscaled second sub-image adjacent to both the downscaled first main image and the downscaled second main image,
The downscaled first main image, the downscaled second main image, the downscaled first subimage, and the downscaled second subimage form a preset rectangular shape. 5. The method of claim 4,
The step of downscaling the partial region of the first main image and the partial region of the second main image into trapezoidal shapes, respectively, includes:
generating a plurality of pixel row groups by grouping pixel rows constituting a partial region of the first main image and pixel rows constituting a partial region of the second main image in units of a predetermined value K; and
Each of a plurality of pixel row groups constituting a partial region of the first main image and a partial region of the second main image is downscaled at different ratios to obtain a partial region of the first main image and a partial region of the second main image. A method comprising downscaling some regions each into a trapezoidal shape. According to claim 1,
acquiring gaze information related to a gaze position of a user from an electronic device connected to the edge data network; and,
Further comprising the step of transmitting the generated converted ERP image to the electronic device,
The step of dividing the ERP image into a plurality of main images and a plurality of sub images,
Based on the gaze information, the method comprising the step of dividing the ERP image into a plurality of main images and a plurality of sub-images. 10. The method of claim 9,
Further comprising the step of obtaining motion information related to the user's motion from the electronic device,
The step of dividing the ERP image into a plurality of main images and a plurality of sub images,
and dividing the ERP image into a plurality of main images and a plurality of sub images. In the edge data network that converts the ERP image,
network interface;
a memory storing one or more instructions;
a processor that executes the one or more instructions;
The processor is
Acquire the ERP image,
Divide the ERP image into a plurality of main images and a plurality of sub images,
downscaling a partial region of each of the plurality of main images,
downscaling the entire area of each of the plurality of sub-images,
A converted ERP image is generated by rearranging the downscaled plurality of main images and the downscaled plurality of sub-images,
The converted ERP image will have a rectangular shape of a preset size, edge data network. 12. The method of claim 11,
The processor divides the ERP image into a first main image, a second main image, a first sub-image and a second sub-image, an edge data network. 13. The method of claim 12,
The processor is
downscaling a partial region of the first main image and a partial region of the second main image;
downscaling the entire area of the first sub-image and the entire area of the second sub-image;
An edge data network that generates a converted ERP image by rearranging the downscaled first main image, the downscaled second main image, the downscaled first subimage and the downscaled second subimage. 14. The method of claim 13,
and the processor downscales a partial region of the first main image and a partial region of the second main image to a trapezoidal shape, respectively. 15. The method of claim 14,
and the processor downscales the entire area of the first sub-image and the entire area of the second sub-image to a rectangular shape. 15. The method of claim 14,
The shape of the downscaled first main image and the shape of the downscaled second main image will be diagonally symmetrical, edge data network. 16. The method of claim 15,
The processor is
disposing so that the trapezoidal hypotenuse portion of the downscaled first main image and the trapezoidal hypotenuse portion of the downscaled second main image are adjacent to each other;
By disposing each of the downscaled first sub-image and the downscaled second sub-image adjacent to both the downscaled first main image and the downscaled second main image,
The downscaled first main image, the downscaled second main image, the downscaled first subimage, and the downscaled second subimage form a preset rectangular shape. 15. The method of claim 14,
The processor is
generating a plurality of pixel row groups by grouping pixel rows constituting a partial region of the first main image and pixel rows constituting a partial region of the second main image by a predetermined value K;
Each of a plurality of pixel row groups constituting a partial region of the first main image and a partial region of the second main image is downscaled at different ratios to obtain a partial region of the first main image and a partial region of the second main image. Edge data network, each downscaling some areas into a trapezoidal shape. 12. The method of claim 11,
The processor is
controlling the network interface to obtain gaze information related to a gaze position of a user from an electronic device connected to the edge data network;
Based on the gaze information, the ERP image is divided into a plurality of main images and a plurality of sub images,
An edge data network for controlling the network interface to transmit the generated converted ERP image to the electronic device. 20. The method of claim 19,
The processor is
control the network interface to obtain motion information related to the user's motion from the electronic device,
An edge data network that further uses the obtained motion information to divide the ERP image into a plurality of main images and a plurality of sub images. A computer-readable recording medium in which a program for executing the method of claim 1 in a computer is recorded.

Images