Electronic device, method of controlling the same, and storage medium

Abstract

An electronic device providing optimal image quality according to image characteristics is disclosed. The electronic device includes an interface circuit and a processor configured to correct an image based on whether the image has a predefined characteristic based on a plurality of frames acquired from a signal received through the interface circuit, wherein the processor is further configured to perform identification of the predefined characteristic based on a frame in which a pixel change between two consecutive frames among the plurality of frames is greater than or equal to a threshold value.

Description

Technical Field

[0001] This disclosure relates to an electronic device for automatically identifying optimal image quality based on image characteristics, a method for controlling such device, and a storage medium. Background Technology

[0002] Typically, computers or televisions process received images using some image processing method, regardless of the image characteristics, and then display the processed image on the screen. However, different signal processing methods can be used to obtain better image quality depending on the image characteristics (e.g., still images and moving images). To this end, existing image processing methods include: extracting the amount of color variation between frames of the image to be displayed; identifying the frame as a moving image when the extracted color variation is equal to or greater than a reference value and the color variation persists for a set time or longer; and identifying the frame as a still image when the color variation is lower than the reference value or does not persist for a set time or longer, thereby performing image quality processing on the image and displaying the processed image on the screen. Summary of the Invention

[0003] Technical issues

[0004] Embodiments of this disclosure provide an electronic device capable of improving optimal image quality, a method for controlling the electronic device, and a storage medium storing a computer program.

[0005] Solution to the problem

[0006] An electronic device according to an example embodiment of the present disclosure is provided. The electronic device includes: an interface circuit and a processor configured to correct an image based on whether an image obtained from a plurality of frames received from signals received through the interface circuit has predefined characteristics.

[0007] The processor can be configured to identify predefined features based on frames in a set of frames where the pixel change between two consecutive frames is greater than or equal to a threshold.

[0008] The processor can be configured to detect areas of pixel change.

[0009] A pixel variation region can be formed by connecting multiple outer pixels in a pixel that varies between two consecutive frames.

[0010] Thresholds can include thresholds representing the ratio of pixel variation areas to the effective screen area.

[0011] The processor can be configured to process images in the previous image quality mode where the ratio of pixel variation regions is less than a threshold.

[0012] The processor can be configured to process images with pixel variations less than a threshold in the previous image quality mode.

[0013] The processor can be configured to compare the ratio of similar feature points to identify characteristics.

[0014] The identification of features may include at least one of the identification of scene change information and the identification of motion information.

[0015] The processor can be configured to detect black areas in two consecutive frames of display on the valid screen.

[0016] The processor can be configured to identify a frame as a still image or a moving image based on the difference in black areas detected between two consecutive frames.

[0017] The processor can be configured to distinguish whether the second frame of two consecutive frames is a content image or a UI image.

[0018] The processor can be configured to identify whether an image is still or moving based on feature recognition results.

[0019] The processor can be configured to detect the frame size of a still image and perform additional feature recognition on frames with a predetermined size or larger.

[0020] Additional feature recognition may include the detection of at least one of the target scene or target object.

[0021] The processor can be configured to classify and map a target scene or target object to a predefined category based on the detection of at least one of the target scene and the target object.

[0022] An electronic device according to an example embodiment of the present disclosure is provided. The electronic device includes: an interface circuit and a processor configured to correct an image based on whether an image obtained from a plurality of frames received from signals received through the interface circuit has predefined characteristics.

[0023] The processor can be configured to: detect each black area of ​​a valid screen displaying two consecutive frames where the pixel change is greater than or equal to a threshold in the two consecutive frames; and, based on the detected black areas of the valid screen in the two frames, identify whether the image is a still image or a moving image.

[0024] The image may include a mirror image of the image displayed by an external device.

[0025] A method for controlling an electronic device according to an example embodiment of the present disclosure is provided. The method for controlling an electronic device includes: receiving a signal, and correcting the image based on whether an image of multiple frames acquired from the received signal has predefined characteristics.

[0026] Image correction can include performing the identification of predefined features based on frames where the pixel change between two consecutive frames in a plurality of frames is greater than or equal to a threshold.

[0027] A non-transitory computer-readable storage medium according to an example embodiment of the present disclosure is provided, wherein a computer-executable computer program is stored. When executed, the computer program can provide: detection of pixel changes between two consecutive frames in an image based on a plurality of frames, and identification of predefined characteristics based on frames in which the pixel changes are greater than or equal to a threshold.

[0028] Beneficial effects of the invention

[0029] As described above, the electronic device according to this disclosure can perform image feature recognition on frames in which the pixel change between two consecutive frames is equal to or greater than a threshold, thereby reducing the possible false recognition rate and reducing CPU computing power and utilization, thus providing the advantage of ensuring shared resources.

[0030] Furthermore, even when displaying an image from a mobile device in a screen mirror on an electronic device, since the pixel changes are significant due to the user's manipulation of the still image, the electronic device according to this disclosure can provide the user with optimal image quality by processing it as a still image.

[0031] Even if thumbnails and small-sized moving images are included in web or UI images, electronic devices according to this disclosure can provide users with optimal image quality by processing thumbnails and small-sized moving images into still images. Attached Figure Description

[0032] The above and other aspects, features, and advantages of certain embodiments of this disclosure will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which:

[0033] Figure 1 This is a diagram illustrating example image display scenes of electronic devices according to various embodiments;

[0034] Figure 2 This is a block diagram illustrating example configurations of an electronic device according to various embodiments;

[0035] Figure 3 This is a block diagram illustrating example configurations of an electronic device according to various embodiments;

[0036] Figure 4 This is a block diagram illustrating example configurations for processing images in optimal quality mode according to various embodiments;

[0037] Figure 5 This is a flowchart illustrating example methods for processing images in optimal quality mode according to various embodiments.

[0038] Figure 6 This is a diagram illustrating example structures of image signals according to various embodiments;

[0039] Figure 7 This is a flowchart illustrating example methods for processing images in optimal quality mode according to various embodiments;

[0040] Figure 8 This is a diagram illustrating the previous frame #11 according to various embodiments;

[0041] Figure 9 This is a diagram showing example pixel variation areas in the current frame #12 according to various embodiments;

[0042] Figure 10 This is a diagram illustrating example pixel changes in the current frame #12 according to various embodiments;

[0043] Figure 11 This is a diagram illustrating an example pixel-change frame of another frame #22 according to various embodiments;

[0044] Figure 12 This is a diagram illustrating an example pixel-change frame of another frame #32 according to various embodiments;

[0045] Figure 13 This is a diagram illustrating an example of a previous frame #41 according to various embodiments;

[0046] Figure 14 This is a diagram illustrating another example of the current frame #52 according to various embodiments;

[0047] Figure 15 This is a diagram illustrating another example of the current frame #62 according to various embodiments;

[0048] Figure 16 This is a diagram illustrating another example of the current frame #72 according to various embodiments;

[0049] Figure 17 This is a flowchart illustrating example methods for processing images in optimal quality mode according to various embodiments;

[0050] Figure 18 This is a flowchart illustrating example methods for processing images in optimal quality mode according to various embodiments;

[0051] Figure 19 This is a diagram illustrating an example of a previous frame #81 according to various embodiments;

[0052] Figure 20 This is a diagram illustrating an example of the current frame #82 according to various embodiments;

[0053] Figure 21 This is a flowchart illustrating example methods for selecting an image quality mode suitable for still or moving images according to various embodiments; and

[0054] Figure 22 This is a flowchart illustrating an example method for selecting an image quality mode suitable for a still image or a moving image, according to various embodiments. Detailed Implementation

[0055] In the following description, various exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. In the drawings, the same reference numerals or symbols denote components that perform substantially the same function, and the dimensions of each component may be enlarged for clarity and convenience. However, the present disclosure is not limited to the configurations or operations described in the following examples. In describing the present disclosure, detailed descriptions of known technologies or configurations related to the present disclosure may be omitted if it is determined that such detailed descriptions would unnecessarily obscure the subject matter of the disclosure.

[0056] In this disclosure, expressions such as “having,” “may have,” “include,” and “may include” indicate the presence of corresponding features (e.g., numerical values, functions, operations, components such as parts, etc.) and do not exclude the presence of additional features.

[0057] In this disclosure, expressions such as “A or B”, “at least one of A and / or B”, and “one or more of A and / or B” can include all possible combinations of the items listed together. For example, “A or B”, “at least one of A and B” or “at least one of A or B” can mean all of the following: (1) including at least one A, (2) including at least one B, or (3) including both at least one A and at least one B.

[0058] In embodiments of this disclosure, terms such as first and second ordinal numbers are used only for the purpose of distinguishing one component from other components, and singular expressions include plural expressions unless the context clearly indicates otherwise.

[0059] Furthermore, in embodiments of this disclosure, terms such as “top,” “bottom,” “left,” “right,” “inner,” “outer,” “inner surface,” “outer surface,” “front,” and “rear” are defined based on the accompanying drawings, and the shape or position of each component is not limited thereto.

[0060] The expression “configured (or set) as” as used in this disclosure may be used interchangeably with the expressions “suitable for,” “capable of,” “designed for,” “adapted to,” “constructed to,” or “capable of.” The term “configured (or set) as” does not necessarily refer to hardware “specifically designed for.” Rather, the expression “the device is configured to” may refer to what the device can “do” together with other devices or components. For example, “configured (or set) as a subprocessor to perform A, B, and C” may refer to, for example, a dedicated processor (e.g., an embedded processor) for performing the respective operations, or a general-purpose processor (e.g., a central processing unit (CPU) or application processor) that can perform the respective operations by executing one or more software programs stored in a storage device.

[0061] Electronic devices according to various embodiments of this disclosure may include, but are not limited to, at least one of the following: smartphones, tablets, mobile phones, video phones, e-book readers, desktop computers, laptops, netbooks, workstations, servers, PDAs, portable multimedia players (PMPs), MP3 players, medical devices, cameras, wearable devices, etc. In various embodiments, electronic devices may include, but are not limited to, at least one of the following: Blu-ray players, digital video disc (DVD) players, set-top boxes, home automation control panels, security control panels, media boxes, game consoles, electronic dictionaries, camcorders, digital photo frames, etc.

[0062] In embodiments, electronic devices may include, but are not limited to, at least one of navigation devices, Global Navigation Satellite Systems (GNSS), Event Data Recorders (EDR), Flight Data Recorders (FDR), in-vehicle infotainment devices, marine electronic devices (e.g., marine navigation devices, gyrocompasses, etc.), avionics, safety devices, and in-vehicle head units.

[0063] In this disclosure, the term "user" may refer to a person using electronic device 1 or a device using electronic device 1 (e.g., an artificial intelligence electronic device).

[0064] Figure 1 This is a diagram illustrating an example image display scene of an electronic device 1 according to various embodiments.

[0065] refer to Figure 1 The electronic device 1 can display the screen of the mobile device 2 as a screen mirror on the display unit 15. The electronic device 1 is not limited to displaying images by screen mirroring.

[0066] Electronic device 1 needs to provide optimal image quality in response to various user scenarios, screen effects, configurations, etc., that may be displayed on mobile device 2. Electronic device 1 does not simply classify the screen as video quality simply because it is playing a moving image; instead, it can detect the size of the playing frame and examine motion and scene changes when the frame size is equal to or greater than a certain size. When motion and scene changes are detected, electronic device 1 can determine whether the playing frame is a still image or a moving image by performing image feature recognition.

[0067] When a frame is smaller than a certain size, electronic device 1 can process the frame in the previous image quality mode without performing feature recognition. For example, screens including small moving images such as thumbnails are often displayed alongside text. It is difficult to distinguish whether the area the user is focused on is the moving image or the text. Even if the image including the thumbnail is processed as a moving image, the effect may not be noticeable, and the user may not watch the moving image attentively, but only briefly. Since most moving images maintain a similar appearance over a certain period of time, it is not necessary to perform recognition for every frame. Recognition is only performed when the image changes abruptly to reduce the false recognition rate, reduce CPU computing power and utilization, etc., thus providing an advantage in ensuring shared resources. In addition, it is necessary to check for abrupt changes in the image because ending the moving image application may switch the scene to the main screen or other screens.

[0068] As an example, when a user moves a paused image (e.g., a photo) in the mobile device 2 by manipulating it, the electronic device 1 can process the paused image as a still image even if a sudden change in the image is detected.

[0069] Mobile device 2 may have a remote control application installed for controlling electronic device 1. Additionally, mobile device 2 may include a microphone for receiving user voice. Mobile device 2 can receive the user's analog voice signal via its built-in microphone, digitize the analog voice signal, and send the digitized voice signal to electronic device 1. Mobile device 2 may include a voice recognition function for automatically recognizing the received voice.

[0070] The remote control 3 may include a microphone for receiving user voice commands. The remote control 3 may digitize analog voice signals and transmit the digitized voice signals to the electronic device 1 via, for example, Bluetooth.

[0071] The set-top box 4 can provide image or voice content to the electronic device 1, and display or output the image or voice content.

[0072] Server 5 may include a content server that provides content to electronic device 1 or a speech recognition server that provides speech recognition services.

[0073] AI Speaker 6 can be equipped with applications that control peripheral devices (e.g., televisions and IoT devices). AI Speaker 6 can also be equipped with a voice recognition assistant that receives and recognizes the user's voice commands and operates accordingly. AI Speaker 6 can send images processed according to user commands to electronic device 1 and display those images. Speaker 6 can also display image content independently, including on a monitor.

[0074] As described above, the electronic device of this disclosure can provide images with optimized image quality by taking into account screen mirroring user scenarios.

[0075] Figure 2 This is a block diagram illustrating an example configuration of an electronic device 1 according to various embodiments.

[0076] refer to Figure 2 The electronic device 1 may include an interface circuit 11, which can send various data to and receive various data from the mobile device 2.

[0077] The interface circuit 11 may include wired interface circuits 1 to 6 112 and wireless interface circuits 1 to 3 114.

[0078] The wired interface circuit 1 may include a terrestrial / satellite broadcast antenna connection tuner for receiving broadcast signals, a connection interface for the wired broadcast cable, etc.

[0079] The wired interface circuit 2 may include HDMI, DP, DVI, component, S-Video, composite (RCA terminal), etc., for connecting image devices.

[0080] The wired interface circuit 3 may include a USB interface for connecting general electronic devices, etc.

[0081] The wired interface circuit 4 may include a connection interface for optical fiber equipment.

[0082] The wired interface circuit 5 may include audio device connection interfaces such as headsets, headphones, and external speakers.

[0083] The wired interface circuit 6 may include a connection interface for wired network devices such as Ethernet.

[0084] The wireless interface circuit 1 may include a connection interface for wireless network devices such as Wi-Fi, Bluetooth, ZigBee, Z-wave, RFID, WiGig, WirelessHD, Ultra-Wideband (UWB), Wireless USB, and Near Field Communication (NFC).

[0085] The wireless interface circuit 2 may include an IR transmitting / receiving module for transmitting and / or receiving remote control signals.

[0086] The wireless interface 3 may include a connection interface for mobile communication devices such as 2G to 5G.

[0087] The interface circuit 11 may include a dedicated communication module, which includes various communication circuits for performing communication specifically for each of the mobile device 2 and the server.

[0088] The interface circuit 11 may include a common communication module for communicating with both the mobile device 2 and the server. For example, both the mobile device 2 and the server can communicate via a Wi-Fi module.

[0089] Interface circuit 11 may include input interface circuit and output interface circuit. In this case, the input interface circuit and output interface circuit can be integrated into a module or implemented as separate modules.

[0090] Electronic device 1 may include an image processor (e.g., including image processing circuitry) 12, which processes images received through interface circuitry 11.

[0091] The image processor 12 may include various image processing circuits and perform various image processing procedures on the image signals received by the interface circuit 11. The types of image processing procedures performed by the image processor 12 are diverse and may include, for example, decoding corresponding to the image format, deinterleaving of interleaved image data into a progressive scan scheme, scaling of image data to a preset resolution, noise reduction to improve image quality, detail enhancement, frame refresh rate conversion, etc.

[0092] The image processor 12 can display the processed image signal on the display unit 15 embedded in the electronic device 1, or output the displayed image signal to an external display device 7 to display an image based on the corresponding image signal.

[0093] Electronic device 1 may include memory 13.

[0094] The memory 13 may include, but is not limited to, a computer-readable storage medium and stores data. The memory 13 is accessed by the processor 16 and operations such as reading, writing, modifying, deleting, and updating data are performed by the processor 16.

[0095] The memory 13 can store various contents received from mobile devices 2, set-top boxes 4, servers 5, USB, etc.

[0096] The data stored in memory 13 may include various image / audio content received through interface circuit 11, as well as multiple frames of data that are sequentially displayed by processing the received images. Memory 13 may include a speech recognition module (speech recognition engine) for speech recognition.

[0097] The memory 13 may include an operating system, various applications that can be executed on the operating system, image data, additional data, etc.

[0098] The memory 13 may include a non-volatile memory containing the control program and a volatile memory that loads at least a portion of the installed control program.

[0099] The memory 13 may include at least one of the following storage media: flash memory, hard disk, micro multimedia card, card-type memory (e.g., SD or XD memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, magnetic disk, and optical disk.

[0100] Electronic device 1 may include a voice recognition unit (e.g., a voice recognition circuit) 14.

[0101] The voice recognition unit 14 can execute the voice recognition module stored in the first memory 13 to recognize voice input or transmission from the microphone embedded in the electronic device 1 and the microphone embedded in the mobile device 2 or the remote control 3.

[0102] When receiving voice from a microphone embedded in a mobile device 2 or a remote control 3, the mobile device 2 or remote control 3 can digitize the analog voice signal and send the digitized voice signal to the electronic device 1 via, for example, Bluetooth.

[0103] When a voice signal is received from a microphone embedded in the electronic device 1 itself, the received analog voice signal can be digitized and sent to the processor 16 of the electronic device 1.

[0104] Electronic device 1 can send the received voice signal to a server. In this case, the server can be a speech-to-text (STT) server that converts voice signal-related data into appropriate text, or a main server that also performs STT server functions.

[0105] The data processed by the STT server can be received again by electronic device 1 or sent directly to another server.

[0106] Electronic device 1 can process the received voice signal internally without sending it to the STT server. In other words, electronic device 1 itself can act as an STT server.

[0107] Electronic device 1 can perform a specific function using text sent from the server or text converted by itself. In this case, the processor 16 in electronic device 1 can perform the function, or the converted text can be sent to a separate server (a server other than the STT server or a server acting as the STT server) to perform the function.

[0108] Electronic device 1 may include a display unit (e.g., a display) 15.

[0109] The display unit 15 can display images processed by the image processor 12.

[0110] The implementation scheme of the display unit 15 is not limited, and the display unit 15 can be implemented in various display panels, such as liquid crystal, plasma, light-emitting diode, organic light-emitting diode, surface electron gun conduction electron emitter, carbon nanotube, nanocrystal, etc.

[0111] According to the implementation scheme, the display unit 15 may additionally include additional components. For example, the display may include an LCD panel, an LCD panel driver for driving the LCD panel, and a backlight unit for supplying light to the LCD panel.

[0112] The processor (e.g., including processing circuitry) 16 may include various processing circuits and control each component of the electronic device 1.

[0113] Processor 16 can identify whether an image based on multiple frames obtained from signals received through interface circuit 11 has predefined characteristics.

[0114] Processor 16 can correct images based on image characteristics to provide optimal image quality.

[0115] Processor 16 can detect pixel changes between two consecutive frames in a plurality of frames. Processor 16 can perform predefined characteristic recognition on frames where the detected pixel changes are equal to or greater than a first threshold. The “threshold” can be represented as a “preset value” or “threshold”.

[0116] The processor 16 may not identify predefined characteristics in frames where the detected pixel changes are below a threshold, and may display the frame in a predefined image quality mode. The first threshold for pixel changes may be set to the number of pixels that have changed.

[0117] Processor 16 can calculate the image difference between two consecutive frames on a pixel-by-pixel basis, and when the difference exceeds a certain amount, detect the pixel change region where the difference occurs. Processor 16 can detect frame regions based on the detected pixel change regions. When the detected frame region is equal to or smaller than a certain size, processor 16 can classify the image characteristics as a still image, and when the frame region is equal to or larger than a certain size, processor 16 can perform frame analysis.

[0118] Processor 16 can detect the ratio of similar feature points between two consecutive frames before performing frame analysis. When the ratio of similar feature points is equal to or less than a second threshold, processor 16 can identify that a scene change has occurred, while when the ratio of similar feature points exceeds the second threshold, processor 16 can identify that no scene change has occurred. If the current frame is identified as not having a scene change, processor 16 can maintain the existing image quality mode without performing feature recognition and detect pixel changes in the next frame.

[0119] When a scene change is detected, the processor 16 can extract vector information from two frames, identify motion information from the extracted vector information, and identify screen states, such as the size of the motion and the ratio of each identification direction.

[0120] Processor 16 can identify screen information by performing feature recognition on the current frame in two consecutive frames. In this case, processor 16 can use a deep learning-based learning model to distinguish whether a frame is a general content image or a UI image.

[0121] In an embodiment, the frame to be subject to feature identification can be the entire content.

[0122] In an embodiment, the frame to be identified may be an EPG, wherein the entire frame includes the UI.

[0123] In an embodiment, the frame to be identified may be an EPG, where a certain area of ​​the entire frame is a UI image or a mixture of UI and content.

[0124] In an embodiment, the frame to be identified by features can be its entire UI or text.

[0125] In an embodiment, the frame to be identified may be a frame displayed in picture-in-picture (PIP) mode.

[0126] In an embodiment, the frame to be identified may include at least one thumbnail in the entire frame, which includes UI or text.

[0127] In this embodiment, the processor 16 can detect and compare black areas in two consecutive frames displayed within the effective screen. When the rate of change of the black area in each of the two frames displayed within the effective screen is greater than a predetermined fourth threshold, the processor 16 can process the frame as a still image.

[0128] The processor 16 can identify image characteristics, i.e., still images or moving images, based on the screen state and screen information that have been identified. However, when the image is a still image and has a frame larger than a certain size, the processor 16 can additionally perform image characteristic identification by treating the image as a target from which detailed characteristic information can be extracted.

[0129] When a target scene or object is detected due to image feature recognition, the processor 16 can subdivide the target scene or object into corresponding image features. When no target scene or object is detected, the processor 16 can process the target scene or object into pre-recognized general still image features. The recognized features can be mapped to predefined image quality patterns. The results of this mapping can be accumulated in an image quality pattern stack. In this case, when the accumulation exceeds a certain number of times, the image quality pattern with the most votes can finally be selected from the accumulated image quality patterns.

[0130] Processor 16 can repeat the above process for newly acquired frames and the previous frame.

[0131] The processor 16 can collect data for generating a model that identifies frame characteristics (i.e., whether the frame is a content image or a UI image), and can perform data analysis, processing, and generate at least a portion of the resulting information using machine learning, neural networks, or deep learning algorithms as rule-based or artificial intelligence algorithms.

[0132] For example, processor 16 can perform the functions of a learning unit and a recognition unit. The learning unit can perform functions such as generating a trained neural network, and the recognition unit can perform functions such as using the trained neural network to recognize (or reason, predict, estimate, and determine) data. The learning unit can generate or update the neural network. The learning unit can obtain learning data to generate the neural network. For example, the learning unit can obtain learning data from first memory 13 or from an external source. The learning data can be data used to learn the neural network.

[0133] Before using training data to learn from a neural network, the learning unit can perform preprocessing operations on the acquired training data or select data to be used for learning from multiple training data sets. For example, the learning unit can process or filter training data in a predetermined format, or process the data by adding / removing noise to make it suitable for learning. A trained neural network can include multiple neural networks (or layers). The nodes of the multiple neural networks have weights, and the multiple neural networks can be interconnected so that the output value of one neural network is used as the input value of other neural networks. Examples of neural networks can include models such as Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), Bidirectional Recurrent Deep Neural Networks (BRDNNs), Deep Q-Networks, etc.

[0134] To identify frame characteristics, the recognition unit can obtain target data. The target data can be obtained from memory 13 or externally. The target data can be data to be recognized by the neural network. Before applying the target data to the trained neural network, the recognition unit can perform preprocessing operations on the obtained target data, or select data to be used for recognition from multiple target data sets. For example, the recognition unit can process or filter the target data in a predetermined format, or process the data in a form suitable for recognition by adding / removing noise. The recognition unit can obtain the output value from the neural network by applying the preprocessed target data. According to various embodiments, the recognition unit can obtain a probability value (or reliability value) along with the output value.

[0135] Processor 16 includes at least one general-purpose processor that loads at least a portion of a control program, including instructions from a non-volatile memory containing a control program, into volatile memory and executes the instructions of the loaded control program. The general-purpose processor may be implemented as, for example, a central processing unit (CPU), an application processor (AP), or a microprocessor.

[0136] Processor 16 may include a single-core, dual-core, triple-core, quad-core, or multi-core processor. Multiple processors 16 may be provided. Processor 16 may include, for example, a main processor and a subprocessor operating in a sleep mode (e.g., a mode that only supplies standby power). Additionally, the processor, ROM, and RAM may be interconnected via an internal bus.

[0137] The processor 16 can be implemented as an integral part of the main SoC, which is mounted on a PCB of the embedded electronic device 1. In an embodiment, the main SoC may also include an image processor.

[0138] The control program may include one or more programs implemented in at least one of the following: BIOS, device drivers, operating systems, firmware, platforms, and applications. The application may be pre-installed or pre-stored during the manufacture of electronic device 1, or it may be installed later based on application data received from an external source. The application data may be downloaded to electronic device 1 from an external server, such as an application marketplace. Control programs, external servers, etc., are examples of computer program products, but are not limited to these.

[0139] Figure 3 This is a block diagram illustrating an example configuration of an electronic device 1 according to various embodiments. Reference Figure 3 In electronic device 1, a display unit that displays images independently is excluded, and the processed image content can be output to a display device (e.g., a monitor) 7, such as a television or monitor, via interface circuit 11 (e.g., HDMI). Electronic device 1 may include a display unit that displays simple notifications, control menus, etc.

[0140] Figure 4 This is a block diagram illustrating example configurations for processing images in optimal quality mode according to various embodiments.

[0141] refer to Figure 4 The processor 16 may include a feature recognition target identification module (e.g., including processing circuitry and / or executable program elements) 162, an image feature recognition module (e.g., including processing circuitry and / or executable program elements) 164, and an image quality mode determination module (e.g., including processing circuitry and / or executable program elements) 166.

[0142] The feature recognition target identification module 162 may include various processing circuits and / or executable program elements, and determines whether an image input to the electronic device 1 is subject to feature recognition. The electronic device 1 may sequentially display multiple frames of the image on the display unit 15. Each frame includes pixels as the smallest display unit.

[0143] The feature recognition target identification module 162 can select two consecutive frames from multiple frames and detect pixel changes between the two frames.

[0144] When the detected pixel change is equal to or greater than a predetermined first threshold, the feature recognition target identification module 162 can identify the detected pixel change as a feature recognition target. When the detected pixel change is lower than the first threshold, the feature recognition target identification module 162 can apply and display the previous image quality mode. Here, the first threshold can be set as the number of pixel changes relative to the total number of pixels in the frame. The feature recognition target identification module 162 can detect pixel change regions in the current frame where the pixel change is equal to or greater than the predetermined first threshold. In this case, the pixel change region can be formed by connecting the outer pixels of the pixels that change between two frames. The feature recognition target identification module 162 can extract the frame to which the pixel change region belongs, and when the size of the extracted frame is equal to or greater than a predetermined second threshold, the extracted frame is identified as a feature recognition target. When the size of the extracted frame is lower than the predetermined second threshold, the extracted frame is excluded from the feature recognition targets. Here, the second threshold can be set as the ratio of the size of the extracted frame to the effective screen size.

[0145] The image feature recognition module 164 may include various processing circuits and / or executable program elements, and identifies whether the scene has changed by detecting the ratio of similar feature points relative to frames identified as recognition targets. When a scene change is detected, the image feature recognition module 164 can extract vector information from two input frames and identify motion information from the extracted vector information to identify screen states, such as the size of the motion and the ratio in each recognition direction.

[0146] When a scene change is detected, the image feature recognition module 164 identifies screen information by performing image recognition on the current frame in two consecutive frames. In this case, image recognition can use a deep learning-based learning model to distinguish whether the frame is a general content image or a UI image.

[0147] In this embodiment, the image feature recognition module 164 can detect black areas in two consecutive frames of the effective screen display. In this case, when the rate of change of the black areas in these two frames is equal to or greater than a predetermined fourth threshold, the still image has already moved within the screen, so even if the scene changes greatly, the frame may be processed as a still image.

[0148] The image feature recognition module 164 can determine whether the current frame is a still image or a moving image based on screen information and black area information.

[0149] The image quality mode determination module 166 may include various processing circuits and / or executable program elements, and classifies and maps still images and moving images determined by the image feature recognition module 164 to predefined image quality modes, storing each mapped image quality mode in a stack. For example, still images may include UI, Web, text, gallery, etc. These still images can be classified into multiple categories through additional feature recognition, target scene or target object recognition, mapped to predefined image quality modes, and stored in a quality mode stack.

[0150] In an embodiment, the image quality mode determination module 166 can distinguish whether a still image is a complete still image or a partial moving image that includes small moving images such as thumbnails.

[0151] The image quality mode determination module 166 can select and apply the image quality mode mapped in the maximum number of images stored in the image quality mode stack as the final image quality mode.

[0152] Figure 5 This is a flowchart illustrating example methods for processing images in optimal quality mode according to various embodiments, and Figure 6 This is a diagram illustrating example structures of image signals according to various embodiments.

[0153] In operation S11, electronic device 1 can receive images via interface circuit 11. The received images may include multiple frames #1 to #N, such as... Figure 6 As shown. The multiple frames #1 to #N can be displayed sequentially, one frame at a time.

[0154] In operation S12, the feature recognition target identification module 162 can detect... Figure 6 The multiple frames #1 to #N shown represent the pixel changes between consecutive previous frame #1 and current frame #2. In other words, the feature recognition target identification module 162 can detect different pixels by comparing the pixels of the previous frame #1 with the pixels of the current frame #2.

[0155] In operation S13, the feature recognition target identification module 162 can identify whether the pixel change in operation S12 is equal to or greater than a first threshold. When the pixel change is less than the first threshold ("No" in operation S13), the feature recognition target identification module 162 can not perform image feature recognition in operation S14, apply the previous image quality mode, and detect pixel changes in the next two frames #2 and #3. When the pixel change is equal to or greater than the first threshold ("Yes" in operation S13), the feature recognition target identification module 162 can perform image feature recognition in operation S14. Here, the first threshold can be set to the number of pixels in the current frame #2 that are different from the pixels in the previous frame #1.

[0156] In operation S14, when the pixel change is equal to or greater than the first threshold, the image feature recognition module 164 can extract vector information from the two input frames and recognize motion information from the extracted vector information to identify the screen state, such as the size of the motion and the ratio of each recognition direction.

[0157] The image feature recognition module 164 identifies screen information by performing image recognition on the current frame in two consecutive frames. The image feature recognition module 164 can use a deep learning-based learning model and can distinguish image information about whether a frame is a content image or a UI image.

[0158] In operations S15 and S16, the image feature recognition module 164 can recognize screen information and determine whether the current frame #2 is a still image or a moving image based on the screen state and screen information.

[0159] In operation S17, the image quality mode determination module 166 can determine an image quality mode suitable for the current frame #2 corresponding to the still image or moving image identified by the above method.

[0160] According to the first embodiment of the present disclosure, the electronic device 1 can perform image feature recognition only on frames in which a predetermined threshold or greater pixel change is detected for all multiple frames of the received image, thereby reducing the false recognition rate, reducing CPU computing power and utilization, etc.

[0161] Figure 7 This is a flowchart illustrating example methods for processing images in an optimal image quality mode according to various embodiments. Figure 8 It shows the image from the previous frame #11, and Figure 9 and Figure 10 This is a diagram illustrating an example pixel change region and a pixel change frame of the current frame #12 according to an embodiment of the present disclosure.

[0162] In operation S11, electronic device 1 can receive an image including multiple frames #11 to #1N through interface circuit 11.

[0163] In operation S22, the feature recognition target identification module 162 can detect... Figure 6 The multiple frames #11 to #1N shown represent the pixel changes between consecutive previous frame #11 and current frame #12. In other words, the feature recognition target identification module 162 can detect different pixels by comparing the pixels of the previous frame #11 with the pixels of the current frame #12.

[0164] In operation S23, the feature recognition target identification module 162 can extract the pixel change region PA1 of the current frame #12. The pixel change region PA1 can be formed by connecting the outermost pixels among the changed pixels of the current frame #12. Figure 9 Including with Figure 8 The pixel change region PA1 in the current frame #12 differs from the previous frame #11. The pixel change region PA1 can be formed by connecting the outermost pixels among the changed pixels. Figure 9 It can be a quadrilateral region, but it can also be a circular, polygonal, or irregular region.

[0165] In operation S24, the feature recognition target identification module 162 can extract the pixel change frame 102 based on the pixel change region PA1. (Reference) Figure 10 Pixel change frame 102 can be set as a continuous content region, which includes pixel change region PA1, which is part of the current frame #12. In this case, pixel change frame 102 can be the same as pixel change region PA1 or the current frame #12.

[0166] In operation S25, the feature recognition target identification module 162 can determine whether the pixel change frame 102 is equal to or greater than a second threshold. Here, the second threshold can be set as the ratio of the pixel change frame 102 to the area of ​​the entire (effective) screen, for example, 20%. Figure 10 As shown, the pixel change frame 102 is approximately 10% smaller than the second threshold 20%, and the current frame #12 can be identified as a still image. When the pixel change frame is equal to or greater than 20% (i.e., the second threshold) ("Yes" in operation S25), image feature recognition in operation S26 can be performed.

[0167] Figure 11 This is a diagram illustrating pixel changes in frame 202 of another frame #22 according to various embodiments. (See diagram) Figure 11 As shown, the feature recognition target identification module 162 can identify the pixel change frame 202 as a moving image, wherein the pixel change frame 202 has the same size as the pixel change region PA2, and the area ratio of the effective screen is about 30% larger than the second threshold 20%.

[0168] Figure 12 This is a diagram illustrating example pixel variation frame 302 of another frame #32 according to various embodiments. (See diagram for example.) Figure 12 As shown, the feature recognition target identification module 162 can identify the following pixel change frame 302 as a moving image: the pixel change frame 302 has the same size PA3 as the pixel change region PA2, and the area ratio of the effective screen is about 90% larger than the second threshold 20%.

[0169] Return to reference Figure 7 In operation S26, the image feature recognition module 164 can perform image feature recognition on the current frame where the pixel change frame 302 is greater than the second threshold 20%.

[0170] The image feature recognition module 164 can identify motion information between two frames. Motion recognition can detect the ratio of similar feature points between two frames and determine whether the ratio of similar feature points exceeds a third threshold. The image feature recognition module 164 can identify that a scene change has occurred when the ratio of similar feature points is equal to or less than the third threshold, and that no scene change has occurred when the ratio of similar feature points exceeds the third threshold. When the current frame does not represent a scene change, image feature recognition can be stopped, the current frame can be processed in the previous image quality mode, and the image feature recognition process for the next frame can be executed.

[0171] Figure 13 It shows another image from the previous frame #41, and Figure 14 , Figure 15 and Figure 16 These are diagrams showing the different current frames #52, #62, and #72.

[0172] refer to Figure 14 By amplification Figure 13 The current frame #52 is displayed by using a specific area of ​​the previous frame #41, and since the ratio of similar feature points is equal to or less than the third threshold, a scene change is identified, thus enabling image feature information recognition.

[0173] refer to Figure 15 The current frame #62 displays the same as Figure 13 The UI image of the previous frame #41 is unrelated to a specific region, and since the ratio of similar feature points is equal to or less than the third threshold, a scene change is identified, so image feature information recognition can be performed.

[0174] refer to Figure 16 In the current frame #72, with Figure 13 Compared to the previous frame #41, more subtle scene changes were detected, and since the ratio of similar feature points exceeded the third threshold, it was determined that no scene change had occurred. Therefore, the current frame #72 was processed in the previous image quality mode without performing image feature information recognition, and pixel changes in the next frame could be detected.

[0175] The image feature recognition module 164 can extract vector information from two consecutive frames and recognize motion information from the extracted vector information to identify screen state, such as the size of the motion and the ratio of each recognition direction.

[0176] Return to reference Figure 7In operation S27, the image feature recognition module 164 identifies screen information by performing image recognition on the current frame of two consecutive frames. In this case, image recognition can use a deep learning-based learning model to distinguish whether a frame is a content image or a UI image.

[0177] In operation S28, the image feature recognition module 164 can determine whether the current frame is a still image or a moving image based on the recognized screen state and screen information.

[0178] In operation S29, the image quality mode determination module 166 can determine an image quality mode suitable for the current frame #12 corresponding to the still image or moving image identified by the above method.

[0179] According to the embodiment, the electronic device 1 can reduce the false recognition rate and reduce CPU computing power and utilization by performing image feature recognition only on frames where scene changes are detected.

[0180] Figure 17 This is a flowchart illustrating an example method for processing an image in optimal quality mode according to a third embodiment of the present disclosure.

[0181] In operation S31, electronic device 1 can receive images through interface circuit 11. The received images may include multiple frames #11 to #1N.

[0182] In operation S32, the feature recognition target identification module 162 can detect pixel changes between consecutive previous frame #11 and current frame #12 in the plurality of frames #11 to #1N. That is, the feature recognition target identification module 162 can detect different pixels by comparing the pixels of the previous frame #11 with the pixels of the current frame #12.

[0183] In operation S33, the feature recognition target identification module 162 can identify whether the pixel change in operation S32 is equal to or greater than a first threshold. When the pixel change is lower than the first threshold ("No" in operation S33), the feature recognition target identification module 162 can not perform image feature recognition in operation S34, apply the previous image quality mode, and detect pixel changes in the next two frames #12 and #13. When the pixel change is equal to or greater than the first threshold ("Yes" in operation S33), the feature recognition target identification module 162 can perform image feature recognition in operation S34. Here, the first threshold can be set to the number of pixels in the current frame #12 that are different from the pixels in the previous frame #1.

[0184] In operation S34, the feature recognition target identification module 162 can extract the pixel change region PA1 of the current frame #12. The pixel change region PA1 can be formed by connecting the outermost pixels among the changed pixels of the current frame #12. (See reference) Figure 9 The current frame #12 includes the quadrilateral pixel variation region PA1.

[0185] In operation S35, the feature recognition target identification module 162 can extract pixel change frames 102 based on pixel change regions PA1. (Reference) Figure 10 Pixel change frame 102 includes a pixel change region PA1 that is part of the current frame #12. In this case, pixel change frame 102 may be the same as pixel change region PA1 or the current frame #12.

[0186] In operation S36, the feature recognition target identification module 162 can determine whether the pixel change frame 102 is equal to or greater than a second threshold. Here, the second threshold can be set as the ratio of the area of ​​the pixel change frame 102 to the area of ​​the entire (effective) screen. When the pixel change frame 102 is less than the second threshold ("No" in operation S36), the current frame #12 can be identified as a still image. When the pixel change frame is equal to or greater than the second threshold ("Yes" in operation S36), image feature recognition in operation S37 can be performed.

[0187] In operation S37, the image feature recognition module 164 can perform image feature recognition on the current frame where the pixel change frame 302 is greater than the second threshold.

[0188] The image feature recognition module 164 can identify motion information between two frames. Motion recognition can detect the ratio of similar feature points between two frames and determine whether the ratio of similar feature points exceeds a third threshold. The image feature recognition module 164 can identify that a scene change has occurred when the ratio of similar feature points is equal to or less than the third threshold, and that no scene change has occurred when the ratio of similar feature points exceeds the third threshold. When the current frame is not a scene change ("No" in operation S37), image feature recognition can be stopped, the current frame can be processed in the previous image quality mode, and the image feature recognition process for the next frame can be executed.

[0189] When a scene change is detected ("Yes" in operation S37), the image feature recognition module 164 can extract vector information from two frames #11 and #12, and identify motion information from the extracted vector information to identify screen state, such as the size of the motion and the ratio of each recognition direction.

[0190] In operation S38, the image feature recognition module 164 identifies screen information by performing image recognition on the current frame #12 of two consecutive frames #11 and #12. The image feature recognition module 164 can use a deep learning-based learning model and can distinguish image information about whether a frame is a content image or a UI image.

[0191] In operation S39, the image feature recognition module 164 can determine whether the current frame is a still image or a moving image based on the recognized screen state and screen information.

[0192] In operation S40, the image quality mode determination module 166 can determine an image quality mode suitable for the current frame #12 corresponding to the still image or moving image identified by the above method.

[0193] As described above, the electronic device 1 according to the embodiment can primarily determine whether to perform image feature recognition based on the number of pixels that change between two frames, and secondly, perform image feature recognition based on whether scene changes occur between two frames, thus enabling more precise selection of image feature recognition targets.

[0194] Figure 18 This is a flowchart illustrating example methods for processing images in optimal quality mode according to various embodiments, and Figure 19 and Figure 20 The diagram shows two consecutive frames #81 and #82 according to various embodiments.

[0195] In operation S41, electronic device 1 can receive images through interface circuit 11.

[0196] In operation S42, the feature recognition target identification module 162 can detect pixel changes between consecutive previous frame #81 and current frame #82 in multiple frames #81 to #8N. That is, the feature recognition target identification module 162 can detect different pixels by comparing the pixels of the previous frame #81 with the pixels of the current frame #82.

[0197] In operation S43, the feature recognition target identification module 162 can extract the pixel change region of the current frame #82. The pixel change region can be formed by connecting the outermost pixels among the changed pixels of the current frame #82.

[0198] In operation S44, the feature recognition target identification module 162 can extract pixel change frames 102 based on pixel change regions.

[0199] In operation S45, the feature recognition target identification module 162 can determine whether the pixel change frame 102 is equal to or greater than a second threshold. Here, the second threshold can be set as the ratio of the area of ​​the pixel change frame 102 to the area of ​​the entire (effective) screen. When the pixel change frame 102 is less than the second threshold ("No" in operation S45), the current frame #82 can be identified as a still image. When the pixel change frame is equal to or greater than the second threshold ("Yes" in operation S45), image feature recognition in operation S46 can be performed.

[0200] In operation S46, the image feature recognition module 164 can perform image feature recognition on the current frame where the pixel change frame 302 is greater than the second threshold.

[0201] The image feature recognition module 164 can identify motion information between two frames. Motion recognition can detect the ratio of similar feature points between two frames and determine whether the ratio of similar feature points exceeds a third threshold. The image feature recognition module 164 can identify that a scene change has occurred when the ratio of similar feature points is equal to or less than the third threshold, and that no scene change has occurred when the ratio of similar feature points exceeds the third threshold. When the current frame does not represent a scene change, image feature recognition can be stopped, the current frame can be processed in the previous image quality mode, and the image feature recognition process for the next frame can be executed.

[0202] When a scene change is detected ("Yes" in operation S46), the image feature recognition module 164 can extract vector information from two frames #81 and #82, and identify motion information from the extracted vector information to identify screen state, such as the size of the motion and the ratio of each recognition direction.

[0203] In operation S47, the image feature recognition module 164 identifies screen information by performing image recognition on the current frame #82 of two consecutive frames #81 and #82. The image feature recognition module 164 can use a deep learning-based learning model and can distinguish image information about whether a frame is a content image or a UI image.

[0204] In operation S48, the image feature recognition module 164 can detect the rate of change of the black areas BA1 and BA2 compared to the effective screen displaying the two frames #81 and #82. In this case, when the rate of change of the black areas of the two frames #81 and #82 is equal to or greater than a predetermined fourth threshold, frames #81 and #82 can be determined as still images.

[0205] refer to Figure 19 and Figure 20Frames #81 and #82 are frames that are not transformed by the content itself, but simply moved. Therefore, even if the scene changes due to the movement of the content, the image feature recognition module 164 can process frames #81 and #82 as still images.

[0206] In operation S49, the image feature recognition module 164 can determine whether the current frame is a still image or a moving image based on the recognized screen state, screen information and black area information.

[0207] In operation S50, the image quality mode determination module 166 can determine an image quality mode suitable for the current frame #12 corresponding to the still image or moving image identified by the above method.

[0208] Figure 21 This is a flowchart illustrating an example method for selecting an image quality mode suitable for identification of still or moving images in various embodiments.

[0209] In operation S61, the image quality mode determination module 166 can check the frame size of the image corresponding to the identified still image (i.e., UI image, Web image, text image, gallery image, etc.).

[0210] In operation S62, the image quality mode determination module 166 can perform additional image feature recognition on images with a frame size equal to or greater than a fourth threshold. The image quality mode determination module 166 can detect target scenes or target objects in the current frame through additional image feature recognition, and classify the image into preset categories #1 to #N based on the detected target scenes or target objects.

[0211] In operation S63, the image quality mode determination module 166 can map images classified by its category to predefined image quality modes. Additionally, the image quality mode determination module 166 can map images identified as moving images to predefined image quality modes.

[0212] In operation S64, the image quality mode determination module 166 can store images mapped to predefined image quality modes in the image quality stack.

[0213] In operation S65, the image quality mode determination module 166 can select the maximum number of image quality modes stored in the image quality mode stack as the final image quality mode.

[0214] As described above, by detecting the frame size of the image identified as a still image and identifying only subdivided images with a predetermined size or larger by additional image characteristic information, the optimal image quality mode can be applied to actual still images.

[0215] Figure 22 This is a flowchart illustrating an example method for selecting an image quality mode suitable for a still image or a moving image, according to various embodiments.

[0216] In operation S71, the image quality mode determination module 166 can check the frame size of the image corresponding to the identified still image (i.e., UI image, Web image, text image, gallery image, etc.).

[0217] In operation S72, the image quality mode determination module 166 can perform motion information recognition on images with a frame size equal to or greater than a fourth threshold. The image quality mode determination module 166 can use motion information recognition to distinguish whether an image identified as a still image is entirely still or whether a moving image is included in a specific region. When an image is identified as an image including a moving image in a specific region, the image quality mode determination module 166 can classify the image into one of the separate categories. As a result, some moving image categories are processed to an overall image quality suitable for still images, but only specific regions are processed to an image quality suitable for moving images; therefore, some moving image categories can be processed using an image quality mode suitable for the image characteristics.

[0218] In operation S73, when the image is generally still, the image quality mode determination module 166 can perform additional image feature recognition. The image quality mode determination module 166 can detect target scenes or target objects in the current frame through additional image feature recognition, and classify the image into preset categories #1 to #N based on the detected target scenes or target objects.

[0219] In operation S74, the image quality mode determination module 166 can map images classified by its category to predefined image quality modes. Additionally, the image quality mode determination module 166 can map images identified as moving images to predefined image quality modes.

[0220] In operation S75, the image quality mode determination module 166 can store images mapped to predefined image quality modes in the image quality stack.

[0221] In operation S76, the image quality mode determination module 166 can select the maximum number of image quality modes stored in the image quality mode stack as the final image quality mode.

[0222] As described above, even when a moving image is included in a specific area of ​​an image that has been processed as a still image, a more optimized image quality mode can be provided to the user by applying an image quality mode that takes into account both the still image and the moving image.

[0223] The electronic device 1 according to the embodiments of the present disclosure can provide not only images transmitted from the mobile device 2 in a screen mirror manner as the optimal image quality mode, but also images transmitted via the interface circuit 11 in a streaming manner and images transmitted via a download method as the optimal image quality mode.

[0224] The optimal image quality mode service module according to embodiments of this disclosure can be implemented as a computer program product stored in a first memory 13, which is a computer-readable storage medium, or as a computer program product transmitted and received via network communication. Furthermore, the image quality mode service module described above can be implemented as a computer program individually or integratedly.

[0225] A computer program according to embodiments of the present disclosure can perform the detection of pixel changes between two consecutive frames in an image based on multiple frames, and perform the identification of predefined characteristics based on frames in which the pixel changes are equal to or greater than a threshold.

[0226] While this disclosure has been illustrated and described with reference to various exemplary embodiments, it should be understood that these exemplary embodiments are intended to be illustrative and not restrictive. Those skilled in the art will further understand that various changes in form and detail may be made without departing from the full scope of this disclosure, including the appended claims and their equivalents.

Claims

1. An electronic device, comprising: monitor; Interface circuit; and The processor is configured as follows: The interface circuit processes signals received from an external display device, the signals corresponding to an image displayed on the external display device. The display is controlled to show a mirrored image based on the processed signal. The mirrored image comprises multiple frames. Identify regions of pixel change where the pixel change between two consecutive frames in the plurality of frames is greater than or equal to a first threshold. Based on the ratio of the size of the pixel variation region to the screen size of the display being greater than or equal to a second threshold, the characteristics of the mirrored image are identified, and the mirrored image is displayed based on a predetermined image quality mode for still or moving images according to the characteristics of the mirrored image. The mirror image is displayed based on a predetermined image quality mode of the still image, provided that the ratio of the size of the pixel variation region to the screen size of the display is less than a second threshold, without performing the identification of the characteristics of the mirror image.

2. The electronic device of claim 1, wherein the processor is configured to detect the pixel change region.

3. The electronic device according to claim 1, The pixel variation region is defined by connecting a plurality of outer pixels among the pixels that vary between the two consecutive frames.

4. The electronic device according to claim 1, The processor is configured to process images in a previous image quality mode where the ratio of the pixel variation region is less than the second threshold.

5. The electronic device according to claim 1, The processor is configured to process an image in a previous image quality mode where the pixel variation is less than the first threshold.

6. The electronic device according to claim 1, The processor is configured to compare the ratio of similar feature points to identify the feature.

7. The electronic device according to claim 6, The identification of the aforementioned characteristics includes at least one of scene change information identification and motion information identification.

8. The electronic device according to claim 1, The processor is configured to detect black areas of a valid screen displaying two consecutive frames.

9. The electronic device according to claim 8, The processor is configured to identify two consecutive frames as still images or moving images based on the difference in black regions detected between them.

10. The electronic device according to claim 1, The processor is configured to identify whether the second of the two consecutive frames is a content image or a UI image.

11. The electronic device according to claim 1, The processor is configured to identify whether the image is a still image or a moving image based on feature recognition results.

12. The electronic device of claim 11, wherein the processor is configured to: Detect the frame size of a still image. Detecting the target scene or target object in the current frame by performing additional feature recognition on frames with a predetermined size or larger, and The image is classified into a preset category based on the detected target scene or target object.

13. A method for controlling an electronic device as claimed in any one of claims 1 to 12, comprising: Receive a signal, the signal corresponding to an image displayed on an external display device; A mirrored image is displayed on the screen of the electronic device based on the received signal, the mirrored image comprising multiple frames; Identify the pixel change region in which the pixel change between two consecutive frames in the plurality of frames is greater than or equal to a first threshold; Based on the ratio of the size of the pixel change region to the screen size of the display being greater than or equal to a second threshold, the characteristics of the mirrored image are identified, and the mirrored image is displayed based on a predetermined image quality mode of a still image or moving image according to the characteristics of the mirrored image. as well as The mirror image is displayed based on a predetermined image quality mode of the still image, provided that the ratio of the size of the pixel variation region to the screen size of the display is less than a second threshold, without performing the identification of the characteristics of the mirror image.

14. A non-transitory computer-readable storage medium having an executable computer program stored thereon, wherein the computer program, when executed, performs the method of claim 13.

Images