Color enhancement method and electronic equipment

Abstract

The invention provides a color enhancement method and electronic equipment, and relates to the technical field of image processing. After an electronic device receives a video stream, scene detection can be performed on a current image in the video stream to determine whether the current image and a previous frame of image belong to the same scene. Under the condition of belonging to the same scene, the electronic equipment can directly take the color adjustment parameter corresponding to the previous frame of image as the color adjustment parameter corresponding to the current image, so that the color adjustment parameter matched with the scene of the current image can be quickly determined. And under the condition that the current image does not belong to the same scene, the electronic equipment determines a color adjustment parameter matched with the scene of the current image. Afterwards, the electronic device enhances the color of the current image by using the color adjustment parameter corresponding to the current image, thereby realizing real-time enhancement of the image color, realizing accurate and rapid determination of the color adjustment parameter, and ensuring the color enhancement effect, thereby ensuring the display effect of the video.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a color enhancement method and electronic device. Background Art

[0002] Video is one of the ways to record and store information, which can enable users to obtain information more intuitively and conveniently. With the development of technology, video is widely used in different fields such as life, work, education, etc. At the same time, people's requirements for video quality are getting higher and higher.

[0003] At present, in order to improve the video quality, after obtaining the image in the video stream, the electronic device needs to use the specified color adjustment parameters to adjust the pixel value of each pixel in the image to enhance the color of the image. However, different video images all use the same color adjustment parameters, which is not adaptive, and the color adjustment effect of the video image is poor, resulting in poor video color display effect. Summary of the invention

[0004] In view of this, the present application provides a color enhancement method and an electronic device for improving the color display effect of a video.

[0005] In a first aspect, the present application provides a color enhancement method. After acquiring a video stream, the electronic device obtains, for each first image frame in the video stream, a scene detection result corresponding to the first image based on the difference between the pixel value of a target channel of the first image and the pixel value of a target channel of a previous frame of the first image; wherein the scene detection result corresponding to the first image indicates whether the first image and the previous frame of the image belong to the same scene, that is, whether the scene corresponding to the first image has changed.

[0006] When the scene detection result indicates that the first image and the previous image belong to the same scene, it means that the scene corresponding to the first image has not changed, and the electronic device can use the color adjustment parameters corresponding to the previous image as the color adjustment parameters corresponding to the first image; the color adjustment parameters corresponding to the previous image are color adjustment parameters that match the scene of the previous image.

[0007] When the scene detection result indicates that the first image and the previous image do not belong to the same scene, it means that the scene corresponding to the first image has changed. The electronic device can re-determine the color adjustment parameters corresponding to the first image based on the first image, that is, determine that the color adjustment parameters corresponding to the first image are color adjustment parameters that match the scene of the first image.

[0008] Based on the color adjustment parameter corresponding to the first image, the color of the first image is adjusted to obtain a target first image; the color includes at least one of contrast, brightness and saturation. Afterwards, the electronic device can display the target first image.

[0009] The fact that the first image and the previous frame image belong to the same scene can be understood as the difference in contents (or objects) between the two frames of images is small, or the difference in display effects between the two frames of images is small.

[0010] In the present application, after receiving the current frame image (i.e., the first image), it is determined whether the first image and the previous frame image of the first image belong to the same scene. If they belong to the same scene, the electronic device can use the color adjustment parameters corresponding to the previous frame image as the color adjustment parameters corresponding to the first image, without re-predicting the color adjustment parameters, to achieve rapid determination of the color adjustment parameters and improve the processing efficiency of the video image. If they do not belong to the same scene, the electronic device can re-predict the color adjustment parameters corresponding to the first image to obtain color adjustment parameters that match the color scene of the first image, so that the electronic device can use the color adjustment parameters that match the color scene of the first image to adjust the color of the first image, achieve adaptive color adjustment, achieve real-time color enhancement of the video image, improve the quality of the video image, and thus improve the color display effect of the video. And because there are errors in the prediction of the color adjustment parameters, that is, for the same scene, there may be errors in the predicted color adjustment parameters. Therefore, when the current frame image and the previous frame image belong to the same scene, directly using the color adjustment parameters corresponding to the previous frame image to adjust the current frame image can avoid the different color display effects of two adjacent frames belonging to the same scene due to errors, thereby avoiding discontinuities between video frames (such as frame skipping, flickering, etc.).

[0011] In a possible design, the process of determining the difference between the pixel value of the target channel of the first image and the pixel value of the target channel of the previous frame image may include:

[0012] For each pixel in the first image, the electronic device can calculate the difference between the pixel value of the target channel of the pixel and the pixel value of the target channel of the pixel at the corresponding position in the previous frame image, and obtain the difference of the target channel corresponding to the pixel in the first image. In other words, for each first pixel in the first image, the electronic device can calculate the difference between the pixel value of the target channel of the first pixel and the pixel value of the target channel of the corresponding second pixel in the previous frame image, and obtain the difference of the target channel corresponding to the first pixel. Among them, the position of the first pixel on the first image is the same as the position of the second pixel corresponding to the first pixel on the previous frame image.

[0013] The electronic device may obtain the difference between the pixel value of the target channel of the first image and the pixel value of the target channel of the previous frame image based on the average value of the difference of the target channel corresponding to each first pixel point in the first image.

[0014] In the present application, the difference between the pixel value of the target channel of the first image and the pixel value of the target channel of the previous frame image is determined based on the difference in the pixel value of the target channel of the pixel point between the current frame and the previous frame image, that is, the scene detection result is determined, and the scene detection result is determined by the overall difference value of two adjacent frames of images, thereby ensuring the accuracy of the scene detection result.

[0015] In a possible design, the number of the target channels is one or more. Accordingly, the difference between the pixel value of the target channel of the first image and the pixel value of the target channel of the previous frame image is obtained by averaging the difference values ​​of the target channels corresponding to the pixels in the first image, including:

[0016] For each target channel, the electronic device may calculate the average value of the difference values ​​of the target channels corresponding to each pixel point to obtain the difference value of the target channel corresponding to the first image. Afterwards, the electronic device may obtain the difference between the pixel value of the target channel of the first image and the pixel value of the target channel of the previous frame image by taking the average value of the difference values ​​of each target channel corresponding to the first image.

[0017] In the present application, the electronic device can determine the difference between the pixel value of the target channel of the first image and the pixel value of the target channel of the previous frame image based on the difference between different target channels, that is, determine the scene detection result, and detect the degree of difference between the two frames of images from different dimensions to ensure the accuracy of the scene detection result.

[0018] In a possible design, the process of obtaining the scene detection result corresponding to the first image based on the difference between the pixel value of the target channel of the first image and the pixel value of the target channel of the previous frame of the first image may include:

[0019] When the difference in the target channel between the pixel value of the target channel of the first image and the pixel value of the target channel of the previous frame image is less than or equal to the first threshold, it indicates that the difference in the pixel values ​​between the two frames of images is small, that is, the difference in the displayed objects is small. The electronic device can determine that the scene detection result corresponding to the first image indicates that the first image and the previous frame image belong to the same scene, that is, the scene of the first image has not changed.

[0020] When the difference in the target channel between the pixel value of the target channel of the first image and the pixel value of the target channel of the previous frame image is greater than the first threshold, it indicates that the pixel value difference between the two frames of images is large, that is, the display objects are very different. The scene detection result corresponding to the first image is determined to indicate that the first image and the previous frame image do not belong to the same scene, that is, the scene of the first image has changed, thereby achieving accurate detection of the scene of the first image.

[0021] In a possible design, the target channel includes at least one of a hue H channel, a saturation S channel, and a brightness V channel.

[0022] In a possible design, the color may include brightness, and accordingly, the color adjustment parameter includes a brightness adjustment parameter. Since the brightness is related to the pixel value of the Y channel, the process of adjusting the brightness of the first image may include: for each pixel point in the first image (that is, the first pixel point), based on the brightness adjustment parameter, increasing the pixel value of the Y channel of the pixel point to achieve brightness enhancement, and avoiding excessive or insufficient brightness enhancement, so that the brightness of the first image after brightness enhancement is adapted to the scene of the first image.

[0023] Specifically, the electronic device may use the brightness adjustment parameter as an index of the pixel value of the Y channel of the pixel point, that is, for each pixel point in the first image, the electronic device uses Y L =Y0 L , calculate the pixel value of the Y channel of the pixel after brightness enhancement to enhance the brightness of the first image.

[0024] In a possible design, color includes contrast, and accordingly, the color adjustment parameter includes a contrast adjustment parameter and a brightness adjustment parameter. Since the contrast is related to the pixel value of the Y channel, the process of adjusting the contrast of the first image may include: first, for each pixel point in the first image, the electronic device may increase the pixel value of the Y channel of the pixel point based on the brightness adjustment parameter to obtain the pixel value of the Y channel of the pixel point after brightness enhancement.

[0025] Afterwards, the electronic device can determine the adjustment value of the Y channel of the pixel point based on the average value of the Y channel pixel value after brightness enhancement of the pixel point and the Y channel pixel value after brightness enhancement of each pixel point corresponding to the first image, combined with the contrast adjustment parameter. Based on the adjustment value and the average value of the Y channel of the pixel point, the pixel value of the Y channel of the pixel point after contrast enhancement is obtained to achieve contrast enhancement, and avoid excessive or insufficient contrast enhancement, so that the contrast of the first image after contrast enhancement is adapted to the scene of the first image.

[0026] Specifically, for each pixel in the first image, the electronic device may calculate the difference between the pixel in the Y channel after brightness enhancement and the average value. Then, the product of the difference between the pixel in the Y channel after brightness enhancement and the average value and the contrast adjustment parameter is calculated to obtain the adjustment value of the Y channel of the pixel. That is, for each pixel in the first image, the electronic device may use the Y C =(Y L-mean0)*C+mean0, calculate the pixel value of the Y channel of the pixel after contrast enhancement to achieve accurate contrast enhancement of the first image. C represents the pixel value of the Y channel after contrast enhancement of the pixel point, C represents the contrast adjustment parameter, and mean0 represents the average value of the pixel value of the Y channel after contrast enhancement corresponding to the first image.

[0027] In a possible design, color includes saturation, and accordingly, the color adjustment parameter includes a saturation adjustment parameter. Since saturation is related to the pixel value of the chromaticity channel (such as U and V channels), the process of adjusting the saturation of the first image may include: for each pixel point in the first image, based on the saturation adjustment parameter, increasing the pixel value of the chromaticity channel of the pixel point to achieve saturation enhancement, and avoiding excessive or insufficient saturation enhancement, so that the brightness of the first image after saturation enhancement is adapted to the scene of the first image.

[0028] Specifically, the electronic device calculates the product of the saturation adjustment parameter and the pixel value of the chromaticity channel of the pixel point, that is, for each pixel point in the first image, the electronic device can use U S =U0*S, calculate the pixel value of the U channel after saturation enhancement of the pixel point. S represents the pixel value of the U channel after saturation enhancement of the pixel point, and S represents the saturation adjustment parameter. U0 represents the pixel value of the U channel of the pixel point. And for each pixel point in the first image, the electronic device can use V S =V0*S, calculate the pixel value of the V channel after saturation enhancement. Where V S Represents the pixel value of the V channel after saturation enhancement of the pixel. V0 represents the pixel value of the V channel of the pixel.

[0029] In one possible design, the process of determining the color adjustment parameters corresponding to the first image may include: the electronic device may use the first image as an input parameter of an image processing model, run the image processing model, and obtain the color adjustment parameters corresponding to the first image; the image processing model is a lightweight type of model, which can achieve accurate prediction of the color adjustment parameters that match the scene of the first image, and because the image processing model is a lightweight model, it has high performance and can process images quickly, which can reduce power consumption.

[0030] In a possible design, in order to improve the processing speed of the image, the electronic device may first downsample the first image to obtain the downsampled first image. Afterwards, the electronic device may obtain the scene detection result corresponding to the first image based on the difference between the pixel value of the target channel of the downsampled first image and the pixel value of the target channel of the previous frame image after downsampling of the first image, thereby realizing rapid determination of the scene detection result.

[0031] In a second aspect, the present application provides an electronic device, comprising a display screen, a memory and one or more processors; the display screen, the memory and the processor are coupled; the display screen is used to display an image generated by the processor, and the memory is used to store computer program code, wherein the computer program code comprises computer instructions; when the processor executes the computer instructions, the electronic device executes the method described above.

[0032] In a third aspect, the present application provides a computer storage medium, comprising computer instructions, which, when executed on an electronic device, enable the electronic device to execute the method as described above.

[0033] In a fourth aspect, the present application provides a computer program product, which, when executed on an electronic device, enables the electronic device to execute the method described above.

[0034] It can be understood that the beneficial effects that can be achieved by the electronic device described in the second aspect, the computer storage medium described in the third aspect, and the computer program product described in the fourth aspect provided above can be referred to the beneficial effects in the first aspect and any possible design method thereof, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1A A video scene schematic diagram 1 provided in an embodiment of the present application;

[0036] Figure 1B A video scene diagram provided in an embodiment of the present application Figure 2 ;

[0037] Figure 1C A third schematic diagram of a video scene provided in an embodiment of the present application;

[0038] Figure 1D A video scene diagram provided in an embodiment of the present application Figure 4 ;

[0039] Figure 1E A video scene diagram provided in an embodiment of the present application Figure 5 ;

[0040] Figure 1F A video scene diagram provided in an embodiment of the present application Figure 6 ;

[0041] Figure 2 A structural block diagram of an electronic device provided in an embodiment of the present application;

[0042] Figure 3A A schematic diagram of a color enhancement process provided in an embodiment of the present application;

[0043] Figure 3B A schematic diagram of a model training process provided in an embodiment of the present application;

[0044] Figure 4 A schematic diagram of a model structure provided in an embodiment of the present application;

[0045] Figure 5 A schematic diagram of a model structure provided in the embodiment of the present application Figure 2 ;

[0046] Figure 6 A schematic diagram of a model structure provided in an embodiment of the present application is shown in FIG3;

[0047] Figure 7 A schematic diagram of a model structure provided in an embodiment of the present application Figure 4 ;

[0048] Figure 8 A schematic diagram of a color enhancement method provided in an embodiment of the present application;

[0049] Fig. 9 A schematic diagram of a scene detection process provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this embodiment, unless otherwise specified, "plurality" means two or more.

[0051] Video is composed of multiple frames of images. With the development of technology, video is widely used in different aspects, such as office, movies, conferences, games, collaboration, shooting, etc. Electronic devices can play videos, that is, display the images in the video. For example, when using electronic devices for work, electronic devices (such as user A's device) may share the desktop of other devices (such as user B's device). User B's device sends the desktop display data to user A's device, and user A's device displays the desktop of user B's device (such as user B's device). Figure 1A As shown), the desktop display data can be sent to the device of user A in the form of streaming media, that is, in the form of video stream. The desktop display data is equivalent to an image. It can be simply understood that the device of user B continuously sends an image including the desktop display content to the device of user A, so that the device of user A can display the image. Therefore, when the device of user B displays the content of the PPT file, the desktop display data includes the content of the displayed PPT file, that is, the image includes the content of the PPT file, and the content of the PPT file can be considered as an image. It should be understood that the PPT file can also be considered as an image in other cases, such as recording the content displayed by the electronic device, and the displayed content can include the content of the PPT file.

[0052] For example, in Figure 1B In the collaborative situation shown, the mobile phone sends screen projection data to the PC in real time. The screen projection data is sent in the form of streaming media. The screen projection data includes the screen data displayed by the mobile phone. The PC displays the screen of the mobile phone according to the received screen projection data. Here, the screen can refer to the image in the video.

[0053] For example, in Figure 1C As shown, the mobile phone displays the game screen. Since the game screen is displayed continuously, one frame of the game screen can be considered as one frame of image in the video.

[0054] For example, Figure 1D As shown, the mobile phone plays XX movie and displays the pictures in XX movie. Here, XX movie is equivalent to a video, and the pictures in the movie are equivalent to images.

[0055] For example, Figure 1E As shown, the mobile phone is in a video recording state, and the mobile phone generates a corresponding video stream, which may include recorded video images. When the user wants to end the recording, the user may click the stop recording button 10 as shown in 1D, and the mobile phone stops recording in response to the click operation of the stop recording button 10, and generates a corresponding video file, which may include multiple frames of images.

[0056] For example, Figure 1F As shown, the mobile phone plays a short video and displays the images in the short video, where the short video can also be called a video.

[0057] Above Figures 1A to 1F The type of video shown is only an example, and other aspects may also involve videos, that is, the video may also be of other types, such as TV series type, etc., which is not limited in this application.

[0058] In order to improve the quality of the video, the electronic device can enhance the video image to improve the quality of the video image, thereby improving the display effect of the video image. In one implementation, for each frame of the image in the video, the electronic device can directly use the specified color adjustment parameter to adjust the pixel value of each pixel in the image to enhance the color of the image. For example, the pixel value includes color channel data, and the color channel data of each pixel of the image is multiplied by the specified color adjustment parameter to enhance the color of the image. However, this implementation will make the images in different scenes in the video achieve the same color enhancement effect, but different scenes have different requirements for color enhancement effects. For example, the electronic device plays a movie, and the scenes of some pictures in the movie are darker scenes, such as night scenes, and the colors of the objects in the pictures in the movie are darker, and the scenes of some pictures are brighter scenes, such as daytime scenes, and the colors of the objects in the pictures in the movie are brighter. Using this implementation, after adjusting the picture of the movie, the color of the picture corresponding to the brighter scene will be enhanced, and the objects in the picture may become brighter, and the color of the picture corresponding to the darker scene will also be enhanced, and the objects in the picture will also become brighter, reducing the display effect of the picture. It can be seen that this implementation method is not adaptive and cannot be adaptively adjusted according to the scene of the image in the video, resulting in poor color display effect.

[0059] In another implementation, electronic devices can use deep learning algorithms to enhance video images. However, most models are highly complex and take a long time to process images, making it impossible for electronic devices with low computing power such as mobile phones, tablets, and smart screens to achieve real-time enhancement of video images.

[0060] Therefore, in view of the above problems, the present application proposes a color enhancement method. After obtaining a video stream, the electronic device can perform scene detection for the first image in the video stream, that is, compare the pixel value of the target channel of the first image with the pixel value of the target channel of the previous frame image of the first image, and obtain the scene detection result corresponding to the first image to determine whether the scene of the first image (or referred to as the color scene here) has changed. In the case where the scene detection result corresponding to the first image indicates that the scene has changed, it indicates that the scene of the first image is more different from the scene of the previous frame image, that is, the color display effect of the scene of the first image is more different from that of the previous frame image, so the electronic device needs to re-predict the color adjustment parameter corresponding to the first image. In the case where the scene detection result corresponding to the first image indicates that the scene has not changed, it indicates that the scene of the first image is less different from that of the previous frame image, that is, the color display effect of the scene of the first image is less different from that of the previous frame image, so the electronic device can directly use the color adjustment parameter corresponding to the previous frame image as the color adjustment parameter corresponding to the first image, avoiding unnecessary prediction of the color adjustment parameter. Wherein, the color adjustment parameter includes at least one of a brightness adjustment parameter, a contrast adjustment parameter, and a saturation adjustment parameter. Afterwards, the electronic device can use the color adjustment parameters corresponding to the first image to adjust the color of the first image to obtain the color-enhanced first image, that is, the target first image, to achieve real-time enhancement of the color of the video image, and determine whether the color adjustment parameters need to be re-predicted through scene detection, which can reduce the amount of data processing and thus reduce the power consumption of the electronic device. Afterwards, the electronic device can display the color-enhanced first image to ensure the color display effect of the first image, thereby improving the video quality and ensuring the user's visual experience.

[0061] Exemplarily, the electronic device in the embodiments of the present application may be a mobile phone, a tablet computer, a wearable device, a computer (such as a PC), a personal digital assistant (PDA), or other device capable of displaying images in a video. The embodiments of the present application do not impose any special restrictions on the specific form of the electronic device.

[0062] For example, Figure 2 FIG. 2 shows a schematic diagram of the structure of the electronic device 200. Figure 2As shown, the electronic device 200 may include a processor 210, an external memory interface 220, an internal memory 221, a universal serial bus (USB) interface 230, a charging management module 211, a power management module 212, a battery 213, an antenna 1, an antenna 2, a mobile communication module 240, a wireless communication module 250, an audio module 270, a speaker 270A, a receiver 270B, a microphone 270C, an earphone interface 270D, a sensor module 280, a button 290, a motor 291, an indicator 292, a camera 293, a display screen 294, and a subscriber identification module (SIM) card interface 295, etc.

[0063] It is to be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the electronic device 200. In other embodiments of the present application, the electronic device 200 may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0064] The processor 210 may include one or more processing units, for example, the processor 210 may include an application processor (AP), a modem processor, a graphics processor (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

[0065] The controller may be the nerve center and command center of the electronic device 200. The controller may generate an operation control signal according to the instruction operation code and the timing signal to complete the control of fetching and executing instructions.

[0066] The processor 210 may also be provided with a memory for storing instructions and data. In some embodiments, the memory in the processor 210 is a cache memory. The memory may store instructions or data that the processor 210 has just used or cyclically used. If the processor 210 needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processor 210, and thus improves the efficiency of the system.

[0067] In some embodiments, the processor 210 may include one or more interfaces. The interface may include an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.

[0068] It is understandable that the interface connection relationship between the modules illustrated in the embodiment of the present invention is only a schematic illustration and does not constitute a structural limitation on the electronic device 200. In other embodiments of the present application, the electronic device 200 may also adopt different interface connection methods in the above embodiments, or a combination of multiple interface connection methods.

[0069] The charging management module 211 is used to receive charging input from a charger. The charger may be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 211 may receive charging input from a wired charger through the USB interface 230. In some wireless charging embodiments, the charging management module 211 may receive wireless charging input through a wireless charging coil of the electronic device 200. While the charging management module 211 is charging the battery 213, it may also power the electronic device through the power management module 212.

[0070] The wireless communication function of the electronic device 200 can be implemented through the antenna 1, the antenna 2, the mobile communication module 240, the wireless communication module 250, the modem processor and the baseband processor.

[0071] Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in the electronic device 200 can be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve the utilization of the antennas. For example, antenna 1 can be reused as a diversity antenna for a wireless local area network. In some other embodiments, the antenna can be used in combination with a tuning switch.

[0072] The mobile communication module 240 can provide solutions for wireless communications including 2G / 3G / 4G / 5G, etc., applied to the electronic device 200. The mobile communication module 240 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), etc. The mobile communication module 240 can receive electromagnetic waves from the antenna 1, and filter, amplify, and process the received electromagnetic waves, and transmit them to the modulation and demodulation processor for demodulation. The mobile communication module 240 can also amplify the signal modulated by the modulation and demodulation processor, and convert it into electromagnetic waves for radiation through the antenna 1. In some embodiments, at least some of the functional modules of the mobile communication module 240 can be set in the processor 210. In some embodiments, at least some of the functional modules of the mobile communication module 240 can be set in the same device as at least some of the modules of the processor 210.

[0073] The modem processor may include a modulator and a demodulator. Among them, the modulator is used to modulate the low-frequency baseband signal to be sent into a medium-high frequency signal. The demodulator is used to demodulate the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After the low-frequency baseband signal is processed by the baseband processor, it is passed to the application processor. The application processor outputs a sound signal through an audio device (not limited to a speaker 270A, a receiver 270B, etc.), or displays an image or video through a display screen 294. In some embodiments, the modem processor may be an independent device. In other embodiments, the modem processor may be independent of the processor 210 and be set in the same device as the mobile communication module 240 or other functional modules.

[0074] The wireless communication module 250 can provide wireless communication solutions including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared (IR), etc., which are applied to the electronic device 200. The wireless communication module 250 can be one or more devices integrating at least one communication processing module. The wireless communication module 250 receives electromagnetic waves via the antenna 2, modulates the frequency of the electromagnetic wave signal and performs filtering, and sends the processed signal to the processor 210. The wireless communication module 250 can also receive the signal to be sent from the processor 210, modulate the frequency of the signal, amplify it, and convert it into electromagnetic waves for radiation through the antenna 2.

[0075] The electronic device 200 implements the display function through a GPU, a display screen 294, and an application processor. The GPU is a microprocessor for image processing, which connects the display screen 294 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor 210 may include one or more GPUs, which execute program instructions to generate or change display information.

[0076] The display screen 294 is used to display images, videos, etc. The display screen 294 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode or an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the electronic device 200 may include 1 or N display screens 294, where N is a positive integer greater than 1.

[0077] The electronic device 200 can realize the shooting function through ISP, camera 293, video codec, GPU, display screen 294 and application processor.

[0078] The external memory interface 220 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device 200.

[0079] The internal memory 221 can be used to store computer executable program codes, which include instructions. The processor 210 executes various functional applications and data processing of the electronic device 200 by running the instructions stored in the internal memory 221. The internal memory 221 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc. The data storage area may store data created during the use of the electronic device 200 (such as audio data, a phone book, etc.), etc. In addition, the internal memory 221 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0080] The electronic device 200 can implement audio functions such as music playing and recording through the audio module 270, the speaker 270A, the receiver 270B, the microphone 270C, the headphone jack 270D, and the application processor.

[0081] The buttons 290 include a power button, a volume button, etc. The buttons 290 may be mechanical buttons or touch buttons.

[0082] Indicator 292 may be an indicator light, which may be used to indicate charging status, power changes, messages, missed calls, notifications, etc.

[0083] The sensor module 280 may include a pressure sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a distance sensor, a proximity light sensor, a fingerprint sensor, a temperature sensor, a touch sensor, an ambient light sensor, a bone conduction sensor, and the like.

[0084] The embodiment of the present application provides a color enhancement method. After receiving a video stream, the electronic device, such as Figure 3AAs shown, for each first image in the video stream, the electronic device performs scene detection on the first image, such as comparing the scene of the first image with the scene of the previous frame of the first image to determine whether the scene of the first image has changed. If the scene of the first image has changed, it indicates that the color difference between the first image and the previous frame is large, and the color adjustment parameters corresponding to the first image need to be determined. The electronic device can input the first image into the trained first MobileNetV3 model, so as to use the trained first MobileNetV3 model to determine the color adjustment parameters corresponding to the first image, and accurately determine the color adjustment parameters corresponding to the first image. If the scene of the first image has not changed, it indicates that the color difference between the first image and the previous frame is small. Therefore, the color adjustment parameters corresponding to the previous frame can be directly used as the color adjustment parameters corresponding to the first image, so as to quickly determine the color adjustment parameters corresponding to the first image. Afterwards, the electronic device can adjust the first image using the color adjustment parameters corresponding to the first image to enhance the color of the first image, thereby improving the quality of the first image, that is, improving the video quality.

[0085] The first MobileNetV3 model after training is obtained by training with sample images, and the first MobileNetV3 model after training can predict the color adjustment parameters corresponding to the image. The process of obtaining the first MobileNetV3 model after training with sample images will be described in detail below.

[0086] In an embodiment of the present application, the first device can obtain a first sample image set, which can include multiple first sample original images. The first device can perform color degradation processing on the first sample original image to reduce the color quality of the first sample image, and obtain a sample degraded image corresponding to the first sample original image. Afterwards, the first device can use the first sample original image and its corresponding sample degraded image to train the MobileNetV3 model. The trained MobileNetV3 model can predict the color adjustment parameters corresponding to the image in the video, thereby using the color adjustment parameters to improve the color quality of the image, that is, to improve the color display effect of the image. Specifically, if Figure 3B As shown, the process of training the MobileNetV3 model using the first sample image set may include S301-S306.

[0087] S301: A first device obtains a first sample image set, wherein the first sample image set includes a plurality of first sample original images.

[0088] The first sample original image has a relatively high quality, such as an image with a relatively high color quality, that is, an image with a relatively good color display effect (or referred to as a standard image).

[0089] In some embodiments, the first sample original image may be an image obtained from MIT-Adobe FiveK. MIT-Adobe FiveK includes multiple (e.g., 5,000) original images in DNG format (hereinafter referred to as original image 1), and a higher quality image corresponding to each original image 1. Accordingly, the first sample original image may be a higher quality image corresponding to the original image 1.

[0090] Among them, the higher quality image corresponding to the original image 1 may be an image manually modified by five (A, B, C, D, E) professional photo retouchers, namely professional photo retoucher A, professional photo retoucher B, professional photo retoucher C, professional photo retoucher D and professional photo retoucher E. Correspondingly, the first sample original image may include an image manually modified by a professional photo retoucher (such as professional photo retoucher C). Or the first sample original image may include images manually modified by multiple professional photo retouchers, such as images manually modified by professional photo retoucher A and professional photo retoucher C. This application is not limited thereto.

[0091] In some embodiments, the above-mentioned first sample image set can be an image set of multiple types. The types may include collaboration, movies, games, office (such as PPT), meetings, short videos, recordings and the like. Different types of image sets are produced in different ways. For example, a movie-type image set may include: multiple (such as 100) domestic and foreign 1080P / 2K high-definition movies, thereby forming a large data set that is evenly distributed in four aspects: brightness, texture complexity, motion complexity, and color complexity. In other words, it includes movie pictures with different display effects (such as color display effects), that is, images, to ensure the richness of the movie-type image set and improve the generalization of the model.

[0092] For another example, the above-mentioned office PPT type image set may include: the PPT content in the image includes common content such as text, pictures, tables, statistical charts, etc.

[0093] For another example, the image set of the above-mentioned conference type may include different facial images, such as facial images of people of different genders, facial images of people of different ages, facial images of people of different regions, etc.

[0094] For another example, the image set of the above-mentioned game type may include game screens, that is, images, in different game applications.

[0095] In some embodiments, the model training process can be simply understood as the process of how to restore from a lower quality image to a higher quality image for two images with the same content. Therefore, after obtaining the first sample original image with higher quality, for each first sample original image, the first device can perform a degradation process on the color of the first sample original image to obtain an image with poor color corresponding to the first sample original image, that is, obtain a sample degraded image corresponding to the first sample original image, so that the first device can use each first sample original image and each sample degraded image corresponding to the first sample original image to train the model. It should be understood that the content of the first sample original image and the sample degraded image corresponding to the first sample original image, that is, the object is the same, but compared with the first sample original image, the color display effect of the object in the sample degraded image corresponding to the first sample original image is poor, such as the originally bright object becomes dark. The following will introduce a possible process of degrading the color of the first sample original image in combination with S302-S305.

[0096] S302: The first device obtains a brightness degradation value within a preset brightness degradation range.

[0097] S303: The first device obtains a contrast degradation value from a preset contrast degradation range.

[0098] S304: The first device obtains a saturation degradation value from a preset saturation degradation range.

[0099] S305. For each first sample original image among the multiple first sample original images, the first device performs degradation processing on the brightness of the first sample original image based on the brightness degradation value; performs degradation processing on the contrast of the first sample original image based on the contrast degradation value; and performs degradation processing on the saturation of the first sample original image based on the saturation degradation value, to obtain a sample degraded image corresponding to the first sample original image.

[0100] In the embodiment of the present application, since the brightness, saturation and contrast of the image are related to the color of the image, degrading the color means degrading the brightness, saturation and contrast. In other words, for each first sample original image, the first device needs to degrade the first sample original image from the dimensions of brightness, merging degree and contrast. Exemplarily, the first device can randomly obtain a brightness degradation value from a preset brightness degradation range, randomly obtain a contrast degradation value from a preset contrast degradation range, and obtain a saturation degradation value from a preset saturation degradation range.

[0101] Afterwards, since the Y (brightness, or brightness) channel data of the image (i.e., the pixel value of the Y channel) affects the brightness of the image, the first device can use the brightness degradation value to adjust the pixel value of the Y channel of the pixel point in the first sample original image, such as reducing the pixel value of the Y channel of the pixel point in the first sample original image, thereby achieving brightness degradation of the first sample original image.

[0102] Since the Y channel data of the image also affects the contrast of the image, the first device can use the contrast degradation value and the pixel value of the Y channel of the pixel after the brightness is reduced to adjust the pixel value of the Y channel of the pixel in the first sample original image to reduce the brightness of the brighter pixel and increase the brightness of the darker pixel, thereby reducing the contrast of the first sample original image, that is, achieving degradation of the contrast of the first sample original image.

[0103] Since the chrominance U and V channel data of the image (i.e., the pixel value of the U channel and the pixel value of the V channel) affect the saturation of the image, the first device can use the saturation degradation value to adjust the pixel value of the U channel and the pixel value of the V channel of the pixel point in the first sample original image, such as reducing the pixel value of the U channel of the pixel point in the first sample original image and reducing the pixel value of the V channel of the pixel point in the first sample original image, thereby achieving the degradation of the saturation of the first sample original image. Among them, U can represent the difference between the blue component and the brightness. V represents the difference between the red component and the brightness.

[0104] In the embodiment of the present application, the first device degrades the brightness, saturation and contrast of the first sample original image to obtain a sample degraded image corresponding to the first sample original image. Compared with the first sample original image, the overall brightness of the sample degraded image corresponding to the first sample original image is lower. And compared with the first sample original image, the contrast of the sample degraded image corresponding to the first sample original image is lower, the light and dark changes of the first sample original image are more obvious, the details of the image are clearer and more vivid, and the visibility is higher, while the light and dark changes of the sample degraded image corresponding to the first sample original image are relatively gentle.

[0105] And compared with the first sample original image, the sample degraded image corresponding to the first sample original image has a lower saturation, the color of the first sample original image is more vivid and full, while the color of the sample degraded image corresponding to the first sample original image is relatively dim.

[0106] In some embodiments, the process of performing brightness degradation processing on the first sample original image may include: for each pixel in the first sample original image, the first device uses the reciprocal of the brightness degradation value as an index of the pixel value of the Y channel of the pixel (or the original Y channel pixel value) to obtain the pixel value of the Y channel after brightness degradation of the pixel. Specifically, the first device may use the following formula 1 to calculate the pixel value of the Y channel after brightness degradation of the pixel.

[0107]

[0108] Among them, Y represents the pixel value of the original Y channel of the pixel point, Y l It represents the pixel value of the Y channel after brightness degradation of the pixel point, and l represents the brightness degradation value.

[0109] The above-mentioned process of degrading the contrast of the first sample original image may include: for each pixel in the first sample original image, the first device may calculate the difference (or first difference) between the pixel value of the Y channel of the pixel after brightness degradation and the average value of the pixel value of the Y channel corresponding to the first sample original image after brightness degradation. After that, the first device determines the adjustment value of the Y channel of the pixel based on the first difference and the contrast degradation value. After that, the first device may determine the pixel value of the Y channel of the pixel after contrast degradation by using the adjustment value of the Y channel of the pixel and the average value of the pixel value of the Y channel corresponding to the first sample original image after brightness degradation, so as to reduce the brightness of the pixel when the brightness of the pixel exceeds the average brightness of the first sample original image after brightness degradation. When the brightness of the pixel is lower than the average brightness of the first sample original image after brightness degradation, the brightness of the pixel is increased to achieve brightness degradation.

[0110] It should be understood that although contrast degradation is also for the pixel value of the Y channel, for the same pixel point, the pixel value of the Y channel after contrast degradation may change compared to the pixel value of the Y channel after brightness degradation, such as being too large or too small. In other words, the pixel value of the Y channel after contrast degradation may be larger than the pixel value of the original Y channel. In other words, the brightness of the pixel point will become brighter, but it can be determined through experimental tests that, speaking of the image as a whole, the overall brightness of the image is still reduced, that is, the brightness has been degraded.

[0111] The average value of the pixel values ​​of the Y channel after brightness degradation corresponding to the first sample original image represents the average value of the pixel values ​​of the Y channel after brightness degradation of all the pixel points in the first sample original image.

[0112] Specifically, the first device may use the following formula 2 to calculate the pixel value of the Y channel of the pixel after contrast degradation.

[0113]

[0114] Wherein, mean represents the average value of the pixel values ​​of the Y channel after brightness degradation corresponding to the first sample original image. c represents the contrast degradation value. c Indicates the pixel value of the Y channel after contrast degradation.

[0115] The above-mentioned process of degrading the saturation of the first sample original image may include: for each pixel point in the first sample original image, the first device calculates the ratio between the original U channel pixel value of the pixel point and the saturation degradation value to reduce the chroma of the pixel point, and obtains the pixel value of the U channel of the pixel point after saturation degradation. Specifically, the first device may use the following formula 3 to calculate the pixel value of the U channel of the above-mentioned pixel point after saturation degradation.

[0116] U s =U / sFormula 3

[0117] Among them, U s Represents the pixel value of the U channel after saturation degradation of the pixel. U represents the original pixel value of the U channel of the pixel. s represents the saturation degradation value.

[0118] Furthermore, the first device calculates the ratio between the original pixel value of the V channel of the pixel and the saturation degradation value to reduce the pixel value of the V channel of the pixel to obtain the pixel value of the V channel after saturation degradation of the pixel. Specifically, the first device can use the following formula 4 to calculate the pixel value of the U channel after saturation degradation of the above pixel.

[0119] V s =V / s Formula 4

[0120] Among them, V s Represents the pixel value of the V channel after saturation degradation of the pixel. V represents the original pixel value of the V channel of the pixel.

[0121] It should be noted that before the color of the first sample original image is degraded, if the format of the first sample original image is not in YUV format, the first device needs to convert the format of the first sample original image into YUV format. After that, the first device performs degradation processing on the color of the first sample original image in YUV format. If the format of the first sample original image is in YUV format, the first device can perform degradation processing on the color of the first sample original image in YUV format.

[0122] In some embodiments, the brightness degradation value is randomly selected by the first device from a preset brightness degradation range, and the random selection process obeys a uniform distribution. Therefore, when the number of first sample original images is large enough and the number of training iterations is large enough, each value in the preset brightness degradation range can be obtained, and degraded sample images with brightness degradation to different degrees are obtained, thereby ensuring the richness of training samples, improving the accuracy of the model's prediction of brightness adjustment parameters, being able to accurately predict images of different brightnesses, and improving the generalization of the model.

[0123] Similarly, the above contrast degradation value is randomly selected by the first device from the preset contrast degradation range, and the random selection process obeys uniform distribution. Therefore, when the number of first sample original images is large enough and the number of training iterations is large enough, each value in the preset contrast degradation range can be obtained, and degraded sample images with different degrees of contrast degradation are obtained, thereby ensuring the richness of training samples, improving the model's prediction accuracy for contrast adjustment parameters, and being able to accurately predict images with different contrasts, thereby improving the generalization of the model.

[0124] The saturation degradation value is randomly selected by the first device from a preset contrast degradation range, and the random selection process obeys a uniform distribution. Therefore, when the number of first sample original images is large enough and the number of training iterations is large enough, each value in the preset saturation degradation range can be obtained, and degraded sample images with saturation degraded to different degrees are obtained, thereby ensuring the richness of training samples, improving the model's prediction accuracy for saturation adjustment parameters, being able to accurately predict images with different saturations, and improving the generalization of the model.

[0125] In some embodiments, for each first sample original image, the first device may obtain a target preset brightness degradation range corresponding to the type of the first sample original image to select a brightness degradation value from the target preset brightness degradation range. Furthermore, the first device may obtain a target preset contrast degradation range corresponding to the type of the first sample original image to select a contrast degradation value from the target preset contrast degradation range. The first device may obtain a target preset saturation degradation range corresponding to the type of the first sample original image to select a saturation degradation value from the target preset saturation degradation range. Exemplarily, the type may include a video recording type (or recording type, shooting type), a movie type, a conference type, a game type, a collaborative type, and the like.

[0126] However, it is found through experiments that the prediction result of the color adjustment parameters of the image by the model is independent of the type of the first sample image set. Therefore, when training the model, it is not necessary to distinguish the preset brightness degradation range. In other words, the preset brightness degradation range corresponding to all types of images is the same. Accordingly, the above-mentioned preset brightness degradation range can be [1, 1.15], the preset contrast degradation range can be [1, 1.1], and the preset saturation degradation range can be [1, 1.3].

[0127] It should be noted that the above-mentioned preset brightness degradation range, preset saturation degradation range and preset contrast degradation range are obtained through experimental testing. The first device performs degradation processing on the first sample original image based on the degradation values ​​in the preset brightness degradation range, preset saturation degradation range and preset contrast degradation range to obtain a sample degraded image corresponding to the first sample original image, so that the first MobileNetV3 model described below can learn how to restore the sample degraded image corresponding to the first sample original image to the first sample original image, and implement the training of the first MobileNetV3 model. If the degradation value used does not belong to the above-mentioned degradation range (such as the above-mentioned preset brightness degradation range, preset saturation degradation range, preset contrast degradation range), it may make it impossible to restore the sample degraded image corresponding to the first sample original image to the first sample original image during model training, resulting in poor model training effect.

[0128] In some embodiments, in order to ensure the training effect of the model, the first sample original images may include first sample original images of various contrasts, first sample original images of various saturations, and first sample original images of various brightnesses. That is to say, the contrasts between the first sample original images are different, the brightness between the first sample original images are different, the saturation between the first sample original images is different, the contrasts between the first sample original images are the same but the brightness is different, the contrasts and saturations between the first sample original images are different, the saturations and brightness between the first sample original images are different, and the contrasts, brightnesses, and saturations between the first sample original images are different, so as to ensure the comprehensiveness of the training samples.

[0129] S306. The first device trains the first MobileNetV3 model based on each first sample original image and the sample degraded image corresponding to each first sample original image to obtain a trained first MobileNetV3 model. The trained first MobileNetV3 model can predict color adjustment parameters corresponding to images in videos under different scenes, and the color adjustment parameters include brightness adjustment parameters, contrast adjustment parameters, and saturation adjustment parameters.

[0130] In the embodiment of the present application, the first device degrades the saturation, contrast and brightness of the first sample original image respectively to obtain a sample degraded image with reduced saturation, contrast and brightness corresponding to the first sample original image. Afterwards, the first device can input the first sample original image and its corresponding sample degraded image into the first MobileNetV3 model to train the first MobileNetV3 model, so that the first MobileNetV3 model learns how to restore the sample degraded image corresponding to the first sample original image to the first sample original image. In layman's terms, the first MobileNetV3 model learns how to restore an image with poor color display effect to a corresponding image with good color display effect, so that the trained first MobileNetV3 model has the ability to predict the saturation adjustment parameters, contrast adjustment parameters and brightness adjustment parameters of the image.

[0131] The brightness adjustment parameter is used to increase the brightness of the image, the contrast adjustment parameter is used to increase the contrast of the image, and the saturation adjustment parameter is used to increase the saturation of the image.

[0132] In some embodiments, the above is a process of degrading the saturation, contrast and brightness of the first sample original image in the first sample image set, taking the color including saturation, contrast and brightness as an example, to obtain a sample degraded image corresponding to the first sample original image, so as to train the first MobileNetV3 model using the first sample original image and its corresponding sample degraded image, and obtain the trained first MobileNetV3 model capable of predicting the saturation adjustment parameter, the contrast adjustment parameter and the brightness adjustment parameter. Of course, the above color may also include only one of saturation, contrast and brightness, and obtain the trained first MobileNetV3 model capable of predicting the color adjustment parameter corresponding to the color type. For example, the color may include saturation, and the first device may degrade the saturation of the first sample original image to obtain a sample degraded image corresponding to the first sample original image, so as to determine the trained first MobileNetV3 model capable of predicting the saturation adjustment parameter using the first sample original image and its corresponding sample degraded image, where the saturation parameter refers to the color adjustment parameter. For another example, color may include contrast, and the first device may perform degradation processing on the contrast of the first sample original image to obtain a sample degraded image corresponding to the first sample original image, so as to use the first sample original image and its corresponding sample degraded image to determine a trained first MobileNetV3 model capable of predicting a contrast adjustment parameter. Here, the contrast adjustment parameter refers to a color adjustment parameter.

[0133] For another example, color may include brightness, and the first device may perform degradation processing on the brightness of the first sample original image to obtain a sample degraded image corresponding to the first sample original image, so as to use the first sample original image and its corresponding sample degraded image to determine a trained first MobileNetV3 model capable of predicting a brightness adjustment parameter. Here, the brightness adjustment parameter refers to a color adjustment parameter.

[0134] Alternatively, the above-mentioned color may also include only two of saturation, contrast and brightness. For example, the color may include saturation and contrast, and the first device may perform degradation processing on the saturation and contrast of the first sample original image respectively to obtain a sample degraded image corresponding to the first sample original image, so as to use the first sample original image and its corresponding sample degraded image to determine the trained first MobileNetV3 model capable of predicting the saturation adjustment parameter and the contrast adjustment parameter. Here, the contrast adjustment parameter and the saturation adjustment parameter refer to the color adjustment parameter.

[0135] For another example, color may include saturation and brightness, and the first device may perform degradation processing on the saturation and brightness of the first sample original image respectively to obtain a sample degraded image corresponding to the first sample original image, so as to use the first sample original image and its corresponding sample degraded image to determine a trained first MobileNetV3 model capable of predicting a saturation adjustment parameter and a brightness adjustment parameter. Here, the brightness adjustment parameter and the saturation adjustment parameter refer to the color adjustment parameter.

[0136] For another example, color may include contrast and brightness, and the first device may perform degradation processing on the contrast and brightness of the first sample original image respectively to obtain a sample degraded image corresponding to the first sample original image, so as to use the first sample original image and its corresponding sample degraded image to determine a trained first MobileNetV3 model capable of predicting contrast adjustment parameters and brightness adjustment parameters. Here, brightness adjustment parameters and contrast adjustment parameters refer to color adjustment parameters.

[0137] In the embodiment of the present application, the color of the first sample original image can be degraded as required. For example, if the brightness, saturation and contrast of the image are to be improved, the brightness, saturation and contrast of the first sample original image can be degraded respectively to determine the relevant model that can predict the brightness adjustment parameter, saturation adjustment parameter and contrast adjustment data. For another example, if the brightness of the image is to be improved quickly, the brightness of the first sample original image can be degraded to determine the relevant model that can predict the brightness adjustment parameter, etc.

[0138] In some embodiments, the performance and speed of MobileNetV3 are greatly improved compared to MobileNetV1 and MobileNetV2, using a network architecture search (NAS) method. Figure 4 The structure of the above-mentioned first MobileNetV3 model may include a first convolutional layer, a batch normalization (BN) layer and an activation function layer, 11 coding blocks, an average pooling layer and a fully connected layer.

[0139] Correspondingly, after the first device inputs the image (such as the first sample original image and its corresponding sample degraded image) into the first MobileNetV3 model, the image first passes through the first convolution layer, and the first device performs a 3*3 convolution operation on the image to obtain a convolved image. Afterwards, the convolved image passes through a batch normalization layer, and the first device can perform a batch normalization operation on the convolved image, such as normalizing the pixel values ​​of the Y channel, the pixel values ​​of the U channel, and the pixel values ​​of the V channel of the pixels in the convolved image to a smaller preset range, thereby improving the generalization ability of the model.

[0140] Afterwards, the normalized image can continue to pass through the activation function layer. The first device can use a nonlinear activation function (hswish) to perform activation processing on the normalized image. The activation processing process can be understood as a gating mechanism. By limiting the range and relative weights of the input values, it can avoid excessive enhancement or weakening of features, so that the model can learn and express features more effectively, thereby improving the prediction accuracy of the model.

[0141] After that, the activated image passes through 11 encoding blocks in sequence. Among them, the encoding block mainly implements channel separable convolution, SE channel attention mechanism and residual connection. The structure of the encoding block can be as follows Figure 5 shown.

[0142] Among them, channel separation convolution is the main factor in achieving the lightweight of the first MobileNetV3 model. Figure 6 As shown, channel-separable convolution can include depthwise convolution, or depthwise convolution and pointwise convolution. Depthwise convolution uses a 3*3 convolution kernel, and pointwise convolution uses a 1*1 convolution kernel. In other words, channel-separable convolution is divided into two processes: 1) channel-wise channel-separable convolution; 2) normal 1X1 convolution outputs the specified number of channels.

[0143] SE can be understood as an attention module. SE is an attention mechanism in the first MobileNetV3 model, which is used to enhance the network's modeling ability for specific channels. The main function of SE is to improve the model's discriminative and expressive capabilities while maintaining a lightweight structure.

[0144] The basic structure of SE is divided into two steps: squeeze and excitation. In the squeeze step, the input feature map is globally averaged pooled to reduce its dimension to a lower dimension. Then, in the excitation step, two fully connected layers (FC1 and FC2) are used to learn the channel relationship of the features and generate a channel attention vector. Specifically, the application of SE in the first MobileNetV3 model can be as follows: Figure 7 As shown. Among them, Figure 7 The number of FC1 nodes is 1 / 4 of the number of Conv1 channels, and the activation function after FC1 uses Relu. The number of FC2 nodes is the same as the number of Conv1 channels, and the activation function after FC2 uses hardsigmoid. The 4 numbers obtained by FC2 are equivalent to the weight coefficients of each channel of Conv1.

[0145] It should be understood that the number of coding blocks (i.e., 11) is obtained through experimental testing. When the number of coding blocks in the first MobileNetV3 model is 11, the prediction accuracy of the first MobileNetV3 model is high and the prediction efficiency is high. Of course, the number of coding blocks in the first MobileNetV3 model can also be other values, and this application does not limit the number of coding blocks.

[0146] The above describes the process of an image passing through the first convolutional layer, batch normalization layer, activation function layer, and 11 coding blocks. The following will continue to introduce the process of the image (or feature map) after 11 coding blocks passing through the average pooling layer and the fully connected layer.

[0147] The feature map passes through an average pooling layer (or called a global average pooling layer), and the first device performs an average pooling operation on the feature map to obtain an average pooling result.

[0148] After that, the average pooling result is input to the fully connected layer. The fully connected layer is a layer with a fixed number of neurons (nodes), which takes the output of the global average pooling layer as input and maps it to the final output regression result. The regression result here can refer to color adjustment parameters, such as brightness adjustment value, contrast adjustment value and saturation adjustment value.

[0149] In some embodiments, the fully connected layer may include one or more fully connected layers, such as two fully connected layers, namely fully connected layer 1 and fully connected layer 2. Among them, the fully connected layer 1 uses the ReLU activation function to process the average pooling result, and the fully connected layer 2 uses the hard_sigmoid activation function to process the output result of the fully connected layer 1.

[0150] In some embodiments, the backbone of the MobilenetV3 model is trained using a large-scale first sample image set, and the backbone is used to extract image features. The number of iterations (epochs) of the last fully connected layer is set to 300, the learning rate of the first 30 epochs is 0.001, and the learning rate of the remaining epochs is 0.00015. And the Adam optimizer is used for training.

[0151] In some embodiments, the first MobileNetV3 model is only an example, and other lightweight models may also be used, which are not limited in this application, such as MobileNetV1, MobileNetV2, etc.

[0152] The training process of the first MobileNetV3 model is introduced above. After obtaining the trained first MobileNetV3 model, the electronic device using the trained first MobileNetV3 model can use the trained first MobileNetV3 model to predict the color adjustment parameters of the image in the video stream, so as to adjust the image using the color adjustment parameters, realize real-time adjustment of the video image, enhance the color of the video image, and improve the quality of the video image, thereby improving the video quality and ensuring the user's visual experience. Figure 8 , details the process of enhancing the color of video images. Figure 8 As shown, the process is as follows:

[0153] S401: The electronic device obtains a video stream.

[0154] The video stream includes at least one image. The electronic device acquires the video stream in real time, and the video stream may be generated by the electronic device itself, such as when the electronic device is in a recording state, generating a corresponding video stream. Alternatively, the video stream may be a video stream sent by other devices, such as when the electronic device is in a collaborative state, displaying the display content of other devices, and the image in the video stream includes the display content of other devices.

[0155] S402: For each first image in the video stream, the electronic device downsamples the first image to obtain a downsampled first image.

[0156] In some embodiments, each time the electronic device obtains an image (or referred to as a first image), it can downsample the first image, such as downsampling the first image to 256*256. Of course, the first image can also be downsampled to other sizes, such as 512*512. However, compared to other sizes, 256*256 can require lower computing power for the electronic device while ensuring the accuracy of scene detection.

[0157] S403: The electronic device performs scene detection on the first image using the previous image after downsampling of the first image, and obtains a scene detection result corresponding to the first image, wherein the scene detection result indicates whether the scene of the first image has changed.

[0158] In the embodiment of the present application, the electronic device compares the downsampled first image with the previous downsampled image (or the previous frame image) of the first image to determine whether the first image and the previous frame image belong to the same scene. When the scene detection result indicates that the scene (or the color scene) of the first image has changed, it indicates that the display content between the first image and the previous frame image is quite different, the color difference is quite large, and the color adjustment parameters corresponding to the previous frame image cannot be used to adjust the color of the current image (i.e., the first image). The electronic device needs to determine the new color adjustment parameters corresponding to the first image, and can execute S404.

[0159] When the scene detection result indicates that the scene of the first image has not changed, it means that the display content between the first image and the previous image (or called the previous frame image) is slightly different, and the color difference is relatively small. The color adjustment parameters corresponding to the previous frame image can be used to adjust the color of the current image (i.e., the first image), and the electronic device can execute S405.

[0160] In some embodiments, the electronic device may use the difference between the pixel value of the target channel of the first image and the pixel value of the target channel of the previous frame of the first image to determine whether the first image and the previous frame of the image belong to the same scene, so as to obtain the scene detection result corresponding to the first image. The following is an introduction to possible implementation methods for determining the scene detection result.

[0161] In one implementation, since the HSV format corresponds to different dimensions, such as hue, saturation, and brightness, it is possible to determine whether the scene of the first image has changed in different dimensions, thereby ensuring that the obtained scene detection result is more accurate. The following will take the above-mentioned target channels including the H channel, the S channel, and the V channel as an example to describe in detail how to use HSV to obtain the scene detection result corresponding to the first image.

[0162] First, for the hue (H) dimension, the electronic device can calculate the difference between the pixel value of the H channel of the downsampled first image and the pixel value of the H channel of the previous frame image after downsampling of the first image, and obtain the difference of the H channel corresponding to the first image, and the difference of the H channel indicates the degree of hue difference between the first image and the previous frame image. The larger the difference of the H channel, the greater the degree of hue difference, the less the first image and the previous frame image include the same display content, and the less likely the first image and the previous frame image belong to the same scene. Exemplarily, for each pixel point (or first pixel point) in the downsampled first image, the electronic device can calculate the difference between the pixel value of the H channel of the first pixel point and the pixel value of the H channel of the pixel point (or second pixel point) at the corresponding position in the previous frame image, and obtain the difference of the H channel corresponding to the first pixel point. Among them, the position of the second pixel point at the corresponding position in the previous frame image in the previous frame image is the same as the position of the first pixel point in the first image. Afterwards, the electronic device may calculate the difference of the H channel corresponding to all the first pixels in the first image to obtain the difference of the H channel corresponding to the first image. Specifically, the electronic device may use the following formula 5 to calculate the difference of the H channel corresponding to the first image.

[0163]

[0164] Among them, score h represents the difference of the H channel corresponding to the first image, num_piexls represents the total number of pixels in the first image, current_H i represents the i-th pixel in the first image, last_H i Represents the i-th pixel in the previous frame of the first image.

[0165] For the saturation (S) dimension, the electronic device can calculate the difference between the pixel value of the S channel of the downsampled first image and the pixel value of the S channel of the previous frame image after downsampling of the first image, and obtain the difference of the S channel corresponding to the first image, and the difference of the S channel indicates the degree of saturation difference between the first image and the previous frame image. The larger the difference of the S channel, the greater the degree of saturation difference, the less the first image and the previous frame image include the same display content, and the less likely the first image and the previous frame image belong to the same scene. Exemplarily, for each first pixel point in the downsampled first image, the electronic device can calculate the difference between the pixel value of the S channel of the first pixel point and the pixel value of the H channel of the second pixel point at the corresponding position in the previous frame image, and obtain the difference of the S channel corresponding to the first pixel point. Afterwards, the electronic device can calculate the difference of the S channel corresponding to all the first pixels in the first image, and obtain the difference of the S channel corresponding to the first image. Specifically, the electronic device can use the following formula 6 to calculate the difference of the S channel corresponding to the first image.

[0166]

[0167] Among them, score s Indicates the difference of the S channel corresponding to the first image, current_S i Represents the pixel value of the S channel of the i-th pixel in the first image, last_S i Represents the pixel value of the S channel of the i-th pixel in the previous frame of the first image.

[0168] For the brightness (V) dimension, the electronic device can calculate the difference between the pixel value of the V channel of the downsampled first image and the pixel value of the V channel of the previous frame image after downsampling of the first image, and obtain the difference of the V channel corresponding to the first image, and the difference of the V channel indicates the degree of brightness difference between the first image and the previous frame image. The larger the difference of the V channel, the greater the degree of brightness difference, the less the first image and the previous frame image include the same display content, and the less likely the first image and the previous frame image belong to the same scene. Exemplarily, for each first pixel point in the downsampled first image, the electronic device can calculate the difference between the pixel value of the V channel of the first pixel point and the pixel value of the V channel of the second pixel point at the corresponding position in the previous frame image, and obtain the difference of the V channel corresponding to the first pixel point. Afterwards, the electronic device can calculate the difference of the V channel corresponding to all the first pixels in the first image, and obtain the difference of the V channel corresponding to the first image. Specifically, the electronic device can use the following formula 7 to calculate the difference of the V channel corresponding to the first image.

[0169]

[0170] Among them, score v represents the difference of the V channel corresponding to the first image, current_V represents the pixel value of the V channel of the i-th pixel in the first image, and last_V i Indicates the pixel value of the V channel of the i-th pixel in the previous frame of the first image.

[0171] It should be noted that the above-described process of determining the difference of the H channel, the difference of the S channel, and the difference of the V channel corresponding to the first image is only an example. The electronic device may not use each pixel point in the first image to determine it, but may use several pixel points in the first image to determine it. The present application does not limit the method of determining the difference of the H channel, the difference of the S channel, and the difference of the V channel corresponding to the first image.

[0172] Afterwards, the electronic device can obtain the scene detection result corresponding to the first image according to the difference of the H channel, the difference of the S channel, and the difference of the V channel corresponding to the first image. Exemplarily, the electronic device can calculate the average value of the difference of the H channel, the difference of the S channel, and the difference of the V channel corresponding to the first image to obtain the color difference corresponding to the first image. Specifically, the electronic device can use the following formula 8 to calculate the color difference corresponding to the first image.

[0173]

[0174] Here, score represents the color difference corresponding to the first image.

[0175] When the average value, that is, the color difference corresponding to the first image is greater than the threshold value 1, it indicates that the display content of the first image and the previous frame image is quite different, that is, the color difference between the first image and the previous frame image is quite different, and the scene difference between the first image and the previous frame image is quite different. The first image and the previous frame image do not belong to the same scene. The electronic device can determine that the scene detection result corresponding to the first image indicates that the scene has changed.

[0176] When the average value is less than or equal to threshold 1 (such as 27), it indicates that the display content of the first image and the previous frame image is slightly different, that is, the color difference between the first image and the previous frame image is small, the scene difference between the first image and the previous frame image is small, and the first image and the previous frame image belong to the same scene. The electronic device can determine that the scene detection result corresponding to the first image indicates that the scene has not changed.

[0177] It is worth mentioning that the average value of the difference of the H channel, the difference of the S channel, and the difference of the V channel is only an example, and the scene detection result corresponding to the first image can also be determined according to other methods. For example, when the difference of the H channel corresponding to the first image is greater than threshold 2, the difference of the S channel is greater than threshold 3, and the difference of the V channel is greater than threshold 4, the electronic device determines that the scene detection result corresponding to the first image indicates that the scene has changed. When the difference of the H channel corresponding to the first image is less than or equal to threshold 2, the difference of the S channel is less than or equal to threshold 3, and the difference of the V channel is less than or equal to threshold 4, the electronic device determines that the scene detection result corresponding to the first image indicates that the scene has not changed.

[0178] In the present application embodiment, Fig. 9 As shown, the electronic device downsamples (resizes) the first image in the video stream to improve the image processing speed and obtain the downsampled first image. Afterwards, the electronic device can convert the format of the downsampled first image into the HSV format to obtain the first image in the HSV format. Afterwards, the electronic device can respectively calculate the difference between the pixel value of the target channel of each first pixel point in the first image in the HSV format and the pixel value of the target channel of the second pixel point at the corresponding position of the previous frame image in the HSV format, and obtain the target channel pixel difference corresponding to each first pixel point. Afterwards, the electronic device can calculate the average value of the target channel pixel difference corresponding to each first pixel point to obtain the target channel pixel difference corresponding to the first image. Afterwards, the electronic device can calculate the average value of the target channel pixel difference corresponding to the first image to obtain the scene detection result corresponding to the first image, and the scene detection result indicates whether the scene has changed, so as to achieve accurate determination of the scene detection result. In addition, by downsampling the first image, the number of pixels to be calculated can be reduced, thereby reducing the amount of data processing, thereby improving the determination efficiency of the scene detection result.

[0179] The process of determining the previous frame image in the HSV format is similar to the process of determining the first image in the HSV format.

[0180] In some embodiments, generally speaking, the image format in the video stream is in YUV format, and the electronic device may first convert the YUV format of the downsampled first image into RGB format, and then convert the first image in RGB format into a first image in HSV format.

[0181] It should be understood that the above is an example of how to use HSV to obtain the scene detection result corresponding to the first image, taking the target channel including the H channel, the S channel and the V channel as an example. Of course, the target channel may include one or two channels of the H channel, the S channel and the V channel. The electronic device may calculate the difference of the target channel corresponding to the first image, so as to determine the detection result corresponding to the first image using the difference of the target channel corresponding to the first image. For example, the target channel includes the H channel and the S channel, and the electronic device calculates the difference of the H channel corresponding to the first image and the difference of the S channel corresponding to the first image. Afterwards, the electronic device may calculate the average of the difference of the H channel corresponding to the first image and the difference of the S channel corresponding to the first image. When the average is greater than a threshold value, the electronic device may determine that the scene detection result corresponding to the first image indicates that the scene has changed.

[0182] S404: The electronic device inputs the downsampled first image into the trained first MobileNetV3 model to obtain color adjustment parameters corresponding to the first image, wherein the color adjustment parameters include brightness adjustment parameters, contrast adjustment parameters, and saturation adjustment parameters.

[0183] In the embodiment of the present application, since the first image and the previous frame of the first image do not belong to the same scene, different scenes correspond to different color adjustment parameters. Therefore, the electronic device can use the trained first MobileNetV3 model to process the downsampled first image, obtain color adjustment parameters that match the scene of the first image, and achieve accurate prediction of the color adjustment parameters. Afterwards, the electronic device can use the color adjustment parameters that match the scene of the first image to adjust the color of the first image to avoid excessive or insufficient color enhancement, such as excessive or insufficient brightness increase, excessive or insufficient contrast increase, excessive or insufficient saturation increase, to ensure the color display effect of the first image.

[0184] In some embodiments, since the model generally processes images in RGB format, the electronic device can convert the downsampled first image into RGB format. Afterwards, the electronic device can input the downsampled first image in RGB format into the trained first MobileNetV3 model.

[0185] In some embodiments, the above-mentioned use of the trained first MobileNetV3 model to determine the color adjustment parameters corresponding to the first image is only an example. The present application does not limit the method of predicting the color adjustment parameters corresponding to the image. For example, the electronic device can use the pixel values ​​of the pixels in the first image (such as YUV pixel values, grayscale values, etc.) to adjust the preset color adjustment parameters to obtain color adjustment parameters that match the first image.

[0186] It should be noted that the process of downsampling the first image in S402 is optional, and the electronic device may not downsample the first image, but directly use the first image to perform scene detection to determine whether the scene of the first image has changed. In the case of a change, the electronic device can input the first image into the trained first MobileNetV3 model to determine the color adjustment parameters corresponding to the first image, but the data processing volume is large and the image processing efficiency is low.

[0187] It should be understood that the first MobileNetV3 model after the above training is only an example of an image processing model, and the image processing model can also be other lightweight types of models obtained through training.

[0188] S405: The electronic device uses the color adjustment parameters corresponding to the previous image of the first image as the color adjustment parameters corresponding to the first image.

[0189] In the embodiment of the present application, since the first image and the previous frame image of the first image belong to the same scene, it indicates that the color difference between the first image and the previous frame image is small, that is, the difference in color display effect is small. Therefore, the electronic device can use the color adjustment parameters corresponding to the previous frame image as the color adjustment parameters corresponding to the first image.

[0190] In some embodiments, the electronic device uses a brightness adjustment parameter to increase the brightness of the first image. Also, the electronic device uses a contrast adjustment parameter to increase the contrast of the first image. And the electronic device uses a saturation adjustment parameter to increase the contrast of the first image. Since the brightness is related to the pixel value of the Y channel of the image, the brightness of the first image can be increased by adjusting the pixel value of the Y channel of the pixel point in the first image using the brightness adjustment parameter.

[0191] A possible implementation method of improving the brightness of the first image by using the brightness adjustment parameter will be introduced below in conjunction with S406.

[0192] S406: For each pixel in the first image, the electronic device uses the brightness adjustment parameter as an index of the Y channel pixel of the pixel to obtain a pixel value of the Y channel after brightness enhancement of the pixel.

[0193] In an embodiment of the present application, for each pixel in the first image, the electronic device can use Formula 9 to calculate the pixel value of the Y channel of the pixel after brightness enhancement to achieve brightness enhancement of the first image.

[0194] Y L =Y0 L Formula 9

[0195] Among them, YL represents the pixel value of the Y channel after brightness enhancement of the pixel point, L represents the brightness adjustment parameter, and Y0 represents the original pixel value of the Y channel of the pixel point, that is, the pixel value of the Y channel of the pixel point in the first image.

[0196] In some embodiments, since contrast refers to the degree of difference between brightness values ​​in an image. It reflects the difference in brightness between different areas in an image. It can be seen that the contrast of an image is also related to the pixel value of the Y channel of the image. Therefore, the electronic device can also adjust the pixel value of the Y channel of the pixel point in the first image by using the contrast adjustment parameter to improve the contrast of the first image. Exemplarily, for each pixel point in the first image, the electronic device can obtain the adjustment value of the Y channel of the pixel point based on the average value of the pixel value of the Y channel after brightness enhancement of the pixel point and the pixel value of the Y channel after brightness enhancement corresponding to the first image, combined with the contrast adjustment parameter. Among them, the average value of the pixel value of the Y channel after brightness enhancement corresponding to the first image represents the average value of the pixel value of the Y channel after brightness enhancement of each pixel point in the first image. Exemplarily, the electronic device can calculate the difference (or second difference) between the pixel value of the Y channel after brightness enhancement of the pixel point and the average value of the pixel value of the Y channel after brightness enhancement corresponding to the first image. Then, the electronic device can determine the adjustment value of the Y channel of the pixel point based on the second difference and the contrast adjustment parameter.

[0197] Afterwards, the electronic device obtains the pixel value of the Y channel after contrast enhancement according to the adjustment value of the Y channel of the pixel point and the average value of the pixel value of the Y channel after brightness enhancement corresponding to the first image, and improves the contrast of the first image by reducing the brightness value of the darker pixel points in the first image and increasing the brightness value of the brighter pixel points in the first image, thereby enhancing the contrast of the first image. A possible implementation method of improving the brightness of the first image by using the brightness adjustment parameter will be introduced below in combination with S407-S409.

[0198] S407. For each pixel in the first image, the electronic device calculates a second difference between the pixel value of the Y channel after brightness enhancement and the average value of the pixel value of the Y channel after brightness enhancement corresponding to the first image.

[0199] S408: The electronic device calculates the product of the second difference and the contrast adjustment parameter to obtain an adjustment value of the Y channel of the pixel.

[0200] S409: The electronic device calculates the sum of the adjustment value of the Y channel of the pixel point and the average value of the Y channel pixel value after brightness enhancement corresponding to the first image to obtain the Y channel pixel value after contrast enhancement of the pixel point.

[0201] In an embodiment of the present application, for each pixel in the first image, the electronic device may use the following formula 10 to calculate the pixel value of the Y channel of the pixel after contrast enhancement to achieve contrast enhancement of the first image.

[0202] Y C =(Y L -mean0)*C+mean0 Formula 10

[0203] Among them, Y C represents the pixel value of the Y channel after contrast enhancement of the pixel point, C represents the contrast adjustment parameter, mean0 represents the average value of the pixel value of the Y channel after contrast enhancement corresponding to the first image, so as to achieve contrast enhancement of the first image.

[0204] It is understandable that, although both contrast and brightness enhancement are for the pixel value of the Y channel, for the same pixel point, the pixel value of the Y channel after contrast enhancement may change compared to the pixel value of the Y channel after brightness enhancement, such as being too large or too small, that is, the pixel value of the Y channel after contrast enhancement may be smaller than the pixel value of the original Y channel. In other words, the brightness of the pixel point will become darker, but it can be determined through experimental tests that, from the perspective of the first image as a whole, the overall brightness of the first image is still enhanced, that is, the brightness has been enhanced. In addition, the pixel value of the Y channel after contrast enhancement may be larger than the pixel value of the Y channel after brightness enhancement. In other words, the brightness of the pixel point will become brighter, but it can be determined through experimental tests that, from the perspective of the first image as a whole, the overall brightness of the first image will not be too bright.

[0205] In some embodiments, since saturation indicates the purity and vividness of a color, and in the YUV color space, the UV channel indicates the chromaticity information of an image, that is, the color in the image, that is, the saturation is related to the pixel values ​​of the U and V channels of the image, the electronic device can adjust the pixel values ​​of the U channel and the pixel values ​​of the V channel of the pixel points in the first image by using the saturation adjustment parameter to improve the saturation of the first image. A possible implementation method of using the saturation adjustment parameter to improve the saturation of the first image will be introduced below in conjunction with S410 and S411.

[0206] S410: The electronic device calculates the product of the pixel value of the U channel of the pixel point and the saturation adjustment parameter to obtain the pixel value of the U channel of the pixel point after the saturation is enhanced.

[0207] In an embodiment of the present application, for each pixel in the first image, the electronic device may use the following formula 11 to calculate the pixel value of the U channel of the pixel after saturation enhancement.

[0208] U S =U0*S Formula 11

[0209] Among them, U S represents the pixel value of the U channel after saturation enhancement of the pixel point, and S represents the saturation adjustment parameter. U0 represents the original pixel value of the U channel of the pixel point, that is, the pixel value of the U channel of the pixel point in the first image.

[0210] S411. The electronic device calculates the product of the pixel value of the V channel of the pixel point and the saturation adjustment parameter to obtain the pixel value of the V channel of the pixel point after the saturation is enhanced.

[0211] In an embodiment of the present application, for each pixel in the first image, the electronic device may use the following formula 12 to calculate the pixel value of the V channel of the pixel after saturation enhancement.

[0212] V S =V0*S Formula 12

[0213] Among them, V S V0 represents the pixel value of the V channel after saturation enhancement of the pixel point. V1 represents the original pixel value of the V channel of the pixel point, that is, the pixel value of the V channel of the pixel point in the first image.

[0214] The above S410 and S411 are only an example of improving the saturation of the first image. The electronic device may also improve the saturation of the first image in other ways. For example, for each pixel in the first image, the electronic device uses the saturation as the index of the U channel of the pixel to obtain the pixel value of the U channel after the saturation is enhanced. And the saturation is used as the index of the V channel of the pixel to obtain the pixel value of the V channel after the saturation is enhanced, so as to achieve the saturation enhancement of the first image. However, the saturation enhancement effect of the first image introduced in S410 and S411 is better, and the color display effect of the first image is better.

[0215] S412: The electronic device displays the target first image based on the pixel values ​​of the Y channel after contrast enhancement, the pixel values ​​of the U channel after saturation enhancement, and the pixel values ​​of the U channel after saturation enhancement of the pixel points in the first image.

[0216] In the embodiment of the present application, the electronic device uses the brightness adjustment parameter to enhance the brightness of the first image, uses the saturation adjustment parameter to enhance the saturation of the first image, and uses the contrast adjustment parameter to enhance the contrast of the first image, thereby obtaining a color-enhanced first image. Afterwards, the electronic device can display the color-enhanced first image. Compared with the first image, the color-enhanced first image has a brighter overall color, a larger difference in brightness and darkness, and a more vivid color, and a better color display effect, thereby improving the color quality of the image, thereby improving the video quality and ensuring the user's visual experience.

[0217] In an embodiment of the present application, when the electronic device performs color enhancement on a video stream, it first performs scene detection on the first image in the video stream to determine whether the current frame image (i.e., the first image) and the previous frame image of the first image belong to the same scene. If they belong to the same scene, the electronic device can use the color adjustment parameters corresponding to the previous frame image as the color adjustment parameters corresponding to the first image, without re-predicting the color adjustment parameters, to achieve rapid determination of the color adjustment parameters and improve the processing efficiency of the video image. If they do not belong to the same scene, the electronic device can re-predict the color adjustment parameters corresponding to the first image to obtain color adjustment parameters that match the color scene of the first image, so that the electronic device can use the color adjustment parameters that match the color scene of the first image to adjust the color of the first image and achieve adaptive color adjustment. And because there are errors in the prediction of the color adjustment parameters, that is, for the same scene, the color adjustment parameters that may be predicted may have errors. Therefore, when the current frame image and the previous frame image belong to the same scene, directly using the color adjustment parameters corresponding to the previous frame image to adjust the current frame image can avoid the different color display effects of two adjacent frames belonging to the same scene due to errors, thereby avoiding discontinuities between video frames (such as frame skipping, flickering, etc.).

[0218] It should be noted that the above is an example of a color adjustment parameter including a saturation adjustment parameter, a contrast adjustment parameter, and a brightness adjustment parameter to introduce a process of enhancing the saturation, contrast, and brightness of a first image to obtain a color-enhanced first image. Of course, the color adjustment parameter may also include only one of the saturation adjustment parameter, the contrast adjustment parameter, and the brightness adjustment parameter, or the color adjustment parameter may also include only two of the saturation adjustment parameter, the contrast adjustment parameter, and the brightness adjustment parameter. In other words, the color adjustment parameter may include at least one of the saturation adjustment parameter, the contrast adjustment parameter, and the brightness adjustment parameter. Correspondingly, the electronic device may use the color adjustment parameter to enhance the color corresponding to the first image to obtain a color-enhanced first image. For example, the color adjustment parameter includes a brightness adjustment parameter, and the electronic device may use the brightness adjustment parameter to enhance the brightness corresponding to the first image to obtain a color-enhanced first image. For another example, the color adjustment parameter includes a brightness adjustment parameter and a saturation adjustment parameter, and the electronic device may use the brightness adjustment parameter to enhance the brightness corresponding to the first image to obtain a color-enhanced first image, and use the saturation adjustment parameter to enhance the saturation corresponding to the first image.

[0219] In some embodiments, the electronic device can not only perform color enhancement on images in the video in real time, such as performing color enhancement on the first image after obtaining the first image in the video stream as mentioned above, but the electronic device can also perform color enhancement on images in video files that have been stored locally, as long as it can obtain pixel points in the image in the video (such as pixel values ​​of the YUV channel).

[0220] It should be noted that when the first image, that is, the first frame of the first image, is received for the first time, the electronic device can directly input it into the trained first MobileNetV3 model to obtain the color adjustment parameters corresponding to the first frame of the first image, thereby realizing scene detection of the first frame of the first image.

[0221] In some embodiments, the trained first MobileNetV3 model can not only predict the color adjustment parameters corresponding to the first image, but also predict the texture adjustment parameters corresponding to the image. Exemplarily, when the first image and the previous frame image do not belong to the same scene, the electronic device inputs the downsampled first image into the trained first MobileNetV3 model, and can not only obtain the color adjustment parameters corresponding to the first image, but also obtain the texture adjustment parameters corresponding to the first image. In addition, when the first image and the previous frame image belong to the same scene, the electronic device can also directly use the texture adjustment parameters corresponding to the previous frame image as the texture adjustment parameters corresponding to the first image. Afterwards, the electronic device can use the texture adjustment parameters matching the scene of the first image to adjust the texture information of the first image, implement sharpening processing of the first image, enhance the texture clarity of the first image, and avoid the situation where the texture information is enhanced too much or too little, such as the distortion of the first image due to excessive sharpening or the insufficient sharpening degree, resulting in the texture clarity of the first image not being effectively improved, thereby ensuring the clarity of the first image.

[0222] Exemplarily, the trained first MobileNetV3 model can calculate the texture complexity corresponding to the downsampled first image. Afterwards, the trained first MobileNetV3 model can use the mapping curve between texture complexity and texture clarity intensity to determine the downsampled first image, that is, the texture clarity intensity corresponding to the first image, and output the texture clarity intensity corresponding to the first image (that is, the texture adjustment parameter corresponding to the first image). Among them, the texture clarity intensity corresponding to the first image represents the texture adjustment parameter that matches the texture information of the first image. When the texture clarity intensity is used to adjust the first image, there will be no over-adjustment or under-adjustment.

[0223] It is understandable that the first sample image set may also include images with higher definition and images with lower definition. The first device trains the first MobileNetV3 model using the images with higher definition and the images with lower definition, so that the trained first MobileNetV3 model can predict the texture adjustment parameters.

[0224] The above has introduced the detailed process of adjusting the color of the first image. The following will introduce the process of adjusting the texture clarity of the first image in detail.

[0225] First, the electronic device may determine the high frequency information of the first image. Then, for each pixel point in the high frequency information of the first image, the electronic device multiplies the pixel value of the Y channel of the pixel point by the texture adjustment parameter to obtain the target first image.

[0226] In some embodiments, the process of determining the high-frequency area of ​​the first image may include: first, the electronic device performs mean filtering on the pixel values ​​of the Y channel of the pixel points in the first image to obtain the blurred first image (or the first filtered image). Afterwards, the pixel value of the Y channel of each pixel point in the first image is subtracted from the pixel value of the Y channel of the pixel point at the corresponding position in the blurred first image to obtain the high-frequency information of the first image (or high-frequency edge information, edge information), so as to blur the non-high-frequency information of the first image, such as low-frequency information, that is, the part with less texture information. The blurred first image is the high-frequency information of the first image, that is, the part where the texture needs to be enhanced. For example, the pixel value of the Y channel of the first pixel point in the first image is subtracted from the pixel value of the Y channel of the corresponding position in the blurred first image, that is, the first pixel point, to obtain the pixel difference corresponding to the first pixel point in the first image. By analogy, the pixel difference corresponding to each pixel point in the first image can be obtained, and the pixel difference corresponding to each pixel point in the first image constitutes the high-frequency information of the first image.

[0227] In some embodiments, the electronic device calculates the product of the high-frequency information of the first image and the texture adjustment parameter λ to obtain the texture adjustment value of the Y channel of each pixel in the first image. Exemplarily, the electronic device may multiply the pixel value of the Y channel of each pixel in the high-frequency information of the first image by the texture adjustment parameter, that is, multiply the pixel difference corresponding to each pixel in the first image by the texture adjustment parameter to obtain the texture adjustment value of the Y channel corresponding to each pixel in the first image.

[0228] Afterwards, for each pixel in the first image, the electronic device adds the pixel value of the Y channel of the pixel (i.e., the original pixel value of the Y channel) to the texture adjustment value at the corresponding position to obtain the pixel value of the Y channel of the pixel after texture enhancement. For example, the pixel value of the Y channel of the first pixel in the first image is added to the texture adjustment value at the corresponding position, i.e., the texture adjustment value of the Y channel of the first pixel, to obtain the pixel value of the Y channel of the first pixel in the first image after texture enhancement.

[0229] Afterwards, the electronic device synthesizes the pixel values ​​of the Y channel after texture enhancement and the pixel values ​​of the UV channel of the pixel points of the first image to obtain the first image after texture enhancement, that is, to obtain the target first image, thereby obtaining a texture enhanced video stream. It can be understood that the color of the target first image is also enhanced.

[0230] In some embodiments, after obtaining the high-frequency information of the first image, a threshold may be used to determine whether to retain part of the high-frequency information of the first image to suppress the noise generated by texture enhancement. Exemplarily, for the pixel value of the Y channel of each pixel point in the high-frequency information of the first image, the electronic device may determine whether the pixel value of the Y channel of the pixel point is greater than or equal to a threshold value of 5. If it is greater than or equal to the threshold value of 5, the electronic device may retain the pixel point, that is, the high-frequency information of the first image still includes the pixel value of the pixel point.

[0231] When the value is less than the threshold value 5, the electronic device may discard the pixel point, that is, the high-frequency information of the first image does not include the pixel value of the pixel point.

[0232] In an embodiment of the present application, in order to reduce computing power and increase the speed of adjusting the texture clarity of the image, the electronic device can perform real-time texture clarity enhancement processing on the first image in the video stream in the YUV control. And since Y corresponds to brightness, UV corresponds to the chromaticity of color, and UV has little effect on texture clarity, the electronic device can only enhance the pixel value of the Y channel of the first image to ensure the texture enhancement effect of the first image and reduce the amount of data processing. In addition, the electronic device only performs texture enhancement on the high-frequency information of the first image, and does not perform texture enhancement on non-high-frequency information, that is, areas with less texture information, to avoid unnecessary texture adjustment. For example, the first image includes a puppy image portion and a background image portion, and the background image portion refers to the blue sky, which includes less texture information, so it can be blurred. The puppy image portion includes the puppy's hair, which is high-frequency information, so it is necessary to enhance the texture clarity of the puppy's hair to make the detail display effect of the puppy image portion clearer. It can be simply understood that determining the high-frequency information of the first image is to determine the lines of the puppy's hair, then increase the brightness of these lines, and then combine them with the pixel values ​​of the UV channel of the original first image to obtain a first image with higher definition.

[0233] In some embodiments, the high-frequency information may include noise. For example, if the video stream is a video stream generated by recording a video, the image in the captured video stream may include white spots, which are noise. Therefore, the electronic device may perform mean filtering on the high-frequency information of the first image again to obtain the high-frequency information of the filtered first image. Afterwards, for the pixel value of the Y channel of each pixel point in the high-frequency information of the filtered first image, the electronic device may determine whether the pixel value of the Y channel of the pixel point is less than the threshold value 6. If it is less than the threshold value 6, it indicates that the pixel point is likely to be noise, and the electronic device may discard the pixel point, that is, discard the pixel value of the Y channel of the pixel point. If it is greater than or equal to the threshold value 6, the pixel point may be retained. Afterwards, for each pixel point in the high-frequency information of the filtered first image, the electronic device multiplies the pixel value of the Y channel of the pixel point by the texture adjustment parameter to obtain the target first image.

[0234] In some embodiments, the mean filtering method used in the above blur processing is only an example, and the electronic device may also use other methods to blur the first image, such as Gaussian blur (or Gaussian filtering), Laplace operation, median filtering, etc. The difference in high-frequency information of the first image determined by different blur processing methods is small, but compared with other blur processing methods, the performance of mean filtering is higher and the speed of determining the high-frequency information of the first image is faster, so that the texture enhancement rate of the first image can be effectively improved, and the real-time texture enhancement of the video stream can be ensured.

[0235] In the embodiment of the present application, after obtaining the high-frequency information of the first image, the electronic device uses the texture adjustment parameters that match the scene of the first image to adjust the high-frequency information of the first image, thereby achieving adaptive texture adjustment and avoiding excessive or under-enhancement of the texture of the first image. In addition, in order to reduce the influence of noise, after obtaining the high-frequency information of the first image, a mean filter is performed again, and some pixels in the high-frequency information of the first image are discarded according to the threshold value. After two noise reduction processes, the noise generated by texture enhancement is effectively suppressed.

[0236] In some embodiments, although the pixel values ​​of the Y channel of the first image are processed based on brightness and contrast, and the texture adjustment parameters are also processed based on the pixel values ​​of the Y channel of the first image, the texture adjustment parameters mainly adjust the pixel values ​​of the Y channel in the high-frequency area of ​​the first image, while the brightness and contrast adjustment parameters adjust the pixel values ​​of the Y channel of the entire pixel points of the first image. Therefore, after adjusting the pixel values ​​of the Y channel of the pixel points based on the color adjustment parameters and the contrast adjustment parameters, the color quality (such as brightness, contrast) and clarity of the first image as a whole can still be improved.

[0237] Exemplarily, when the pixel values ​​of the channels of the pixel points in the first image need to be processed using the color adjustment parameters and the texture adjustment parameters, for each pixel point in the image, the color adjustment parameters can be used to determine the color-enhanced pixel value of the corresponding channel of the pixel point, and the texture adjustment parameters can be used to determine the clarity-enhanced pixel value of the Y channel of the pixel point. If the channel corresponding to the pixel point also includes the Y channel, the pixel value of the target Y channel of the pixel point can be determined based on the pixel value of the Y channel after color enhancement and the pixel value of the Y channel after clarity enhancement, such as calculating the average value between the two as the pixel value of the target Y channel, or using the pixel value of the Y channel after color enhancement or the pixel value of the Y channel after clarity enhancement as the pixel value of the target Y channel.

[0238] For example, after adjusting the pixel value of the Y channel of each pixel point in the first image based on the brightness adjustment parameter, the pixel value of the Y channel of each pixel point after brightness enhancement is obtained. The pixel value of the Y channel of the pixel point in the high-frequency part of the first image is adjusted by using the texture adjustment parameter to obtain the pixel value of the Y channel of the pixel point in the high-frequency part after clarity enhancement. For the same pixel point, the average value between the pixel value of the Y channel of the pixel point after brightness enhancement and the pixel value of the Y channel of the pixel point after clarity enhancement can be calculated to obtain the pixel value of the target Y channel of the pixel point. Although the pixel value of the target Y channel of the pixel point may be smaller than the pixel value of the Y channel of the pixel point after brightness enhancement, the texture adjustment is for the high-frequency part of the first image. Overall, the brightness of the first image is still enhanced. And although the pixel value of the target Y channel of the pixel point may be smaller than the pixel value of the Y channel of the pixel point after clarity enhancement, the clarity of the first image can still be improved. In summary, although adjusting the image using both texture adjustment parameters and color adjustment parameters may change the pixel value of the Y channel of the high-frequency part of the pixels, that is, the brightness of the high-frequency part of the pixels changes, it will not affect the overall brightness display effect of the first image. Overall, the brightness of the first image is still enhanced, and the clarity is also enhanced.

[0239] In some embodiments, the present application provides a computer storage medium including computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the method as described above.

[0240] In some embodiments, the present application provides a computer program product, which, when executed on an electronic device, enables the electronic device to execute the method described above.

[0241] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated.

[0242] It should be understood that the "embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, the various embodiments in the entire specification do not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0243] It should also be understood that in the present application, "when", "if" and "if" all mean that the UE or base station will take corresponding actions under certain objective circumstances. It is not a time limit, and it does not require the UE or base station to make judgments when implementing it, nor does it mean that there are other limitations.

[0244] A person skilled in the art may understand that the various numerical numbers such as first and second involved in the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application, and also indicate the order of precedence.

[0245] In this application, elements expressed in the singular are intended to mean "one or more" rather than "one and only one", unless otherwise specified. In this application, "at least one" is intended to mean "one or more", and "more than one" is intended to mean "two or more", unless otherwise specified.

[0246] The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there may be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. A can be singular or plural, and B can be singular or plural.

[0247] In this document, the term "at least one of..." or "at least one of..." means all or any combination of the listed items. For example, "at least one of A, B, and C" may mean: A exists alone, B exists alone, C exists alone, A and B exist at the same time, B and C exist at the same time, and A, B, and C exist at the same time, where A may be singular or plural, B may be singular or plural, and C may be singular or plural.

[0248] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0249] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0250] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0251] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0252] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0253] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0254] The same or similar parts between the various embodiments in this application can refer to each other. In the various embodiments in this application, and the various implementation methods / implementation methods / implementation methods in each embodiment, if there is no special explanation and logical conflict, the terms and / or descriptions between different embodiments and the various implementation methods / implementation methods / implementation methods in each embodiment are consistent and can be referenced to each other. The technical features in different embodiments and the various implementation methods / implementation methods / implementation methods in each embodiment can be combined to form new embodiments, implementation methods, implementation methods, or implementation methods according to their inherent logical relationships. The above-described implementation methods of this application do not constitute a limitation on the scope of protection of this application.

[0255] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims. In short, the above is only a preferred embodiment of the technical solution of the present application, and is not used to limit the protection scope of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A color enhancement method, characterized in that: include: Acquire a video stream; wherein the video stream includes at least one frame of a first image; For each first image in the at least one first image, based on a difference between a pixel value of a target channel of the first image and a pixel value of a target channel of a previous image of the first image, obtaining a scene detection result corresponding to the first image; wherein the scene detection result corresponding to the first image indicates whether the first image and the previous image belong to the same scene; When the scene detection result indicates that the first image and the previous image belong to the same scene, the color adjustment parameters corresponding to the previous image are used as the color adjustment parameters corresponding to the first image; the color adjustment parameters corresponding to the previous image are color adjustment parameters that match the scene of the previous image; When the scene detection result indicates that the first image and the previous frame image do not belong to the same scene, based on the first image, determining a color adjustment parameter corresponding to the first image; the color adjustment parameter corresponding to the first image is a color adjustment parameter that matches the scene of the first image; Based on the color adjustment parameter corresponding to the first image, adjust the color of the first image to obtain a target first image; the color includes at least one of contrast, brightness and saturation; The target first image is displayed.

2. The method according to claim 1, characterized in that The method further comprises: For each pixel point in the first image, calculate the difference between the pixel value of the target channel of the pixel point and the pixel value of the target channel of the pixel point at the corresponding position in the previous frame image, to obtain the difference value of the target channel corresponding to the pixel point in the first image; An average value of the difference of the target channel corresponding to each pixel point in the first image is calculated to obtain the difference between the pixel value of the target channel of the first image and the pixel value of the target channel of the previous frame image.

3. The method according to claim 2, characterized in that The number of the target channels is one or more; The calculating an average value of the difference of the target channel corresponding to each pixel point in the first image to obtain the difference between the pixel value of the target channel of the first image and the pixel value of the target channel of the previous frame image includes: For each of the target channels, calculating an average value of the difference values ​​of the target channels corresponding to the respective pixel points, to obtain the difference value of the target channel corresponding to the first image; An average value of the difference values ​​of each target channel corresponding to the first image is calculated to obtain a difference value between a pixel value of the target channel of the first image and a pixel value of the target channel of the previous frame image.

4. The method according to any one of claims 1 to 3, characterized in that The obtaining a scene detection result corresponding to the first image based on a difference between a pixel value of a target channel of the first image and a pixel value of a target channel of a previous frame of the first image comprises: When a difference in the target channel between a pixel value of the target channel of the first image and a pixel value of the target channel of the previous frame image is less than or equal to a first threshold, determining that the scene detection result corresponding to the first image indicates that the first image and the previous frame image belong to the same scene; When a target channel difference between a pixel value of a target channel of the first image and a pixel value of a target channel of the previous frame image is greater than the first threshold, it is determined that a scene detection result corresponding to the first image indicates that the first image and the previous frame image do not belong to the same scene.

5. The method according to any one of claims 1 to 4, characterized in that The target channel includes at least one of a hue H channel, a saturation S channel, and a brightness V channel.

6. The method according to any one of claims 1 to 5, characterized in that The color includes brightness, and the color adjustment parameter includes a brightness adjustment parameter; The adjusting the color of the first image based on the color adjustment parameter corresponding to the first image includes: For each pixel in the first image, based on the brightness adjustment parameter, the pixel value of the Y channel of the pixel is increased.

7. The method according to claim 6, characterized in that The step of increasing the pixel value of the Y channel of the pixel point based on the color adjustment parameter includes: The brightness adjustment parameter is used as an index of the pixel value of the Y channel of the pixel point.

8. The method according to any one of claims 1 to 6, characterized in that The color includes contrast, and the color adjustment parameter includes a contrast adjustment parameter and a brightness adjustment parameter; The adjusting the color of the first image based on the color adjustment parameter corresponding to the first image includes: For each pixel point in the first image, based on the brightness adjustment parameter, the pixel value of the Y channel of the pixel point is increased to obtain the pixel value of the Y channel of the pixel point after brightness enhancement; Determine an adjustment value of the Y channel of the pixel point based on an average value of the Y channel pixel values ​​of the pixel point after brightness enhancement and the Y channel pixel values ​​of each pixel point corresponding to the first image after brightness enhancement, in combination with the contrast adjustment parameter; Based on the adjustment value of the Y channel of the pixel point and the average value, a pixel value of the Y channel of the pixel point after contrast enhancement is obtained.

9. The method according to claim 8, characterized in that The step of determining the adjustment value of the Y channel of the pixel point based on the average value of the pixel value of the Y channel of the pixel point after brightness enhancement and the pixel value of the Y channel of each pixel point corresponding to the first image after brightness enhancement, in combination with the contrast adjustment parameter, comprises: Calculate the difference between the pixel point of the Y channel after brightness enhancement and the average value; The product of the difference between the pixel point of the Y channel after brightness enhancement and the average value and the contrast adjustment parameter is calculated to obtain the adjustment value of the Y channel of the pixel point.

10. The method according to any one of claims 1 to 9, characterized in that The color includes saturation, and the color adjustment parameter includes a saturation adjustment parameter; The adjusting the color of the first image based on the color adjustment parameter corresponding to the first image includes: For each pixel in the first image, based on the saturation adjustment parameter, increasing the pixel value of the chromaticity channel of the pixel; The chrominance channels include a U channel and a V channel.

11. The method according to claim 10, characterized in that The step of increasing the pixel value of the chromaticity channel of the pixel point based on the saturation adjustment parameter includes: The product of the saturation adjustment parameter and the pixel value of the chroma channel of the pixel point is calculated.

12. The method according to any one of claims 1 to 11, characterized in that The determining, based on the first image, a color adjustment parameter corresponding to the first image includes: The first image is used as an input parameter of an image processing model, and the image processing model is run to obtain color adjustment parameters corresponding to the first image; the image processing model is a lightweight model.

13. The method according to any one of claims 1 to 12, characterized in that The obtaining a scene detection result corresponding to the first image based on a difference in the target channel between a pixel value of the target channel of the first image and a pixel value of the target channel of an image of a previous frame of the first image comprises: Downsampling the first image to obtain a downsampled first image; Based on the difference in the target channel between the pixel value of the target channel of the downsampled first image and the pixel value of the target channel of the previous frame image after downsampling of the first image, a scene detection result corresponding to the first image is obtained.

14. An electronic device, characterized in that: The electronic device includes a display screen, a memory and one or more processors; the display screen, the memory and the processor are coupled; the display screen is used to display an image generated by the processor, and the memory is used to store computer program code, and the computer program code includes computer instructions; when the processor executes the computer instructions, the electronic device executes the method described in any one of claims 1 to 13.

15. A computer storage medium, characterized in that: The method comprises computer instructions, which, when executed on an electronic device, cause the electronic device to execute the method according to any one of claims 1 to 13.

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