Image Processing Method, Apparatus, and Storage Medium

Abstract

The present application provides an image processing method, apparatus and storage medium, relating to the technical field of image processing. The method includes: obtaining the brightness value of each pixel point in the image to be processed; determining a first region and a second region based on the brightness value of each pixel point; obtaining a first compression coefficient corresponding to the first region and a second compression coefficient corresponding to the second region; the proportion of the first compression coefficient in the compression coefficients is positively correlated with the average brightness value of the image to be processed; compressing the brightness value of the pixel points in the first region based on the first compression coefficient, and compressing the brightness value of the pixel points in the second region based on the second compression coefficient; sending a target brightness mapping table to the receiving card according to the relationship between the brightness values of each pixel point in the image to be processed before and after compression, so that the image display device displays the image to be processed according to the compressed brightness value. This method is applicable to the process of image display and is used to solve the problem of reduced image quality caused by brightness value compression.

Description

Technical Field

[0001] This application relates to the field of image processing technologies, and in particular, to an image processing method, apparatus, and storage medium. Background Art

[0002] With the continuous development of display technologies, light-emitting diode (LED) displays are widely used due to their advantages such as high brightness, wide viewing angle, and long lifespan.

[0003] However, the high brightness of LED displays also causes problems of high power consumption. To reduce the power consumption of LED displays, it is necessary to process the input image input to the LED display to reduce the brightness when the LED display shows the input image.

[0004] Current brightness control methods can determine the average gray level based on the gray-level data of the input image, and based on the mapping relationship between the average gray level and the brightness control coefficient, determine the target brightness control coefficient corresponding to the average gray level of the input image, and compress the input image according to the target brightness control coefficient.

[0005] However, the target brightness control coefficient determined according to the average gray level will affect the display quality of the image when compressing the input image. Summary of the Invention

[0006] Based on the above technical problems, this application provides an image processing method, apparatus, and storage medium, which can statistically analyze the brightness of the current frame image, and respectively compress the background low-brightness area and the foreground high-brightness area of the current frame image based on the statistical results, while reducing the overall brightness, it can also ensure the contrast of the current frame image, thereby optimizing the display quality after compression.

[0007] In a first aspect, the present application provides an image processing method, which is applied to a transmitting card in an image display device, and the image display device further includes a receiving card; the method includes: obtaining the brightness value of each pixel point in the image to be processed; determining a first region and a second region in the image to be processed based on the brightness value of each pixel point in the image to be processed; the first region is the region where the pixel points with brightness values greater than the brightness threshold in the image to be processed are located; the second region is the region where the pixel points with brightness values less than the brightness threshold in the image to be processed are located; obtaining a first compression coefficient corresponding to the first region and a second compression coefficient corresponding to the second region; the first compression coefficient is used to characterize the reduction amplitude of the brightness value of each pixel point in the first region; the second compression coefficient is used to characterize the reduction amplitude of the brightness value of each pixel point in the second region; the proportion of the first compression coefficient in the compression coefficients is positively correlated with the average brightness value of the image to be processed; the compression coefficient is the sum of the first compression coefficient and the second compression coefficient; compressing the brightness value of the pixel points in the first region based on the first compression coefficient; compressing the brightness value of the pixel points in the second region based on the second compression coefficient; determining a target brightness mapping table according to the relationship between the brightness values of each pixel point in the image to be processed before and after compression; sending the target brightness mapping table to the receiving card, so that the image display device displays the image to be processed according to the compressed brightness value.

[0008] The image processing method provided by the present application can divide the image into a first region with high brightness and a second region with low brightness based on the brightness value of each pixel point in the image to be processed. For the first region with high brightness, the brightness value of the pixel points in the first region is compressed by using the first compression coefficient with a larger reduction amplitude; for the second region with low brightness, the brightness value of the pixel points in the second region is compressed by using the second compression coefficient with a smaller reduction amplitude. The change amplitude of the brightness value of the pixel points in the low-brightness region is small, and the change amplitude of the brightness value of the pixel points in the high-brightness region is large, which can improve the contrast of the image to be processed and retain the detailed content in the low-brightness region, thereby optimizing the display quality after compression on the premise of reducing the overall power consumption of the LED screen.

[0009] Optionally, the method further includes: before determining a first region and a second region in the image to be processed based on the luminance values of each pixel point in the image to be processed, determining the luminance distribution of the image to be processed based on the luminance values of each pixel point in the image to be processed; the luminance distribution is the number of pixel points corresponding to each luminance value in the luminance interval of the image to be processed; the luminance interval is from 0 to the theoretical maximum luminance value of the image to be processed; the theoretical maximum luminance value is 2 to the power of N minus 1; N is the color depth bit number of the image to be processed; determining the cumulative distribution function of the image to be processed based on the luminance distribution of the image to be processed; the cumulative distribution function is used to represent the probability that the luminance value of a pixel point in the image to be processed is less than any luminance value in the luminance interval; selecting Q luminance values corresponding to Q preset probabilities from the cumulative distribution function; Q is an integer greater than or equal to 2; performing weighted average on the Q luminance values according to a preset weight to obtain a luminance threshold.

[0010] Optionally, the method further includes: before obtaining a first compression coefficient corresponding to the first region and a second compression coefficient corresponding to the second region, performing equalization stretching on the first region based on a first traction value, and performing equalization stretching on the second region based on a second traction value; the first traction value is positively correlated with the luminance threshold; the second traction value is less than the first traction value; equalization stretching means transforming the number of pixel points corresponding to each luminance value in the luminance distribution according to the first traction value or the second traction value; the first traction value is used to indicate the amplitude of transformation of the number of pixel points corresponding to the luminance values greater than the luminance threshold in the luminance distribution; the second traction value is used to indicate the amplitude of transformation of the number of pixel points corresponding to the luminance values less than the luminance threshold in the luminance distribution.

[0011] It should be understood that the luminance value of the second region is lower than that of the first region, and the second traction value is less than the first traction value, which means that the amplitude of transformation of the number of pixel points of the luminance value of the second region in the luminance distribution is smaller. In the embodiments of the present application, the image processing device performs equalization stretching on the low-luminance region (the second region) with a lower luminance value with a lower amplitude, which can avoid obvious noise or image distortion caused by excessive stretching of the low-luminance region while equalizing the overall image contrast.

[0012] In a possible implementation manner, performing equalization stretching on the first region based on the first traction value includes: performing equalization stretching on the first region based on the first traction value according to the following formula:

[0013]

[0014]

[0015] where k1 represents any luminance value between the luminance threshold and the theoretical maximum luminance value; h k1Indicates the number of pixels corresponding to k1 in the brightness distribution; h max Indicates the number of pixels corresponding to the brightness value with the largest number of corresponding pixels in the brightness distribution; u(H) represents the first traction value. k1 It indicates the number of pixels corresponding to k1 after the first area is balanced and stretched; initial indicates the minimum value of u(H); scope indicates the variable range of u(H); alpha indicates the brightness threshold; S indicates the value obtained by adding 1 to the theoretical maximum brightness value and dividing it by 2; gamma is a preset constant;

[0016] The second region is pulled up evenly based on the second pulling value, including: the second region is pulled up evenly based on the second pulling value according to the following formula:

[0017]

[0018] u(L)+1+log(1+u(H))

[0019] Where k2 represents any brightness value between 0 and the brightness threshold; h k2 represents the number of pixels corresponding to k2 in the brightness distribution; u(L) represents the second traction value; m k2 It indicates the number of pixels corresponding to k2 after the second area is balanced and stretched.

[0020] In a possible implementation, obtaining a first compression coefficient corresponding to a first area and a second compression coefficient corresponding to a second area includes: determining a first predicted power consumption of the first area according to the sum of squares of brightness values of pixels in the first area; determining a total predicted power consumption of the image to be processed according to the sum of squares of brightness values of pixels in the image to be processed; obtaining an expected power consumption compression coefficient; the expected power consumption compression coefficient is used to indicate an expected decrease in power consumption required to display the image to be processed after reducing the brightness values of pixels in the image to be processed; determining a power consumption constraint relationship based on the first predicted power consumption, the total predicted power consumption, and the expected power consumption compression coefficient; the power consumption constraint relationship is: a first ratio and a second ratio. The result of the weighted summation of the proportions is the expected power consumption compression coefficient; the first proportion is the proportion of the first predicted power consumption in the total predicted power consumption; the weighted weight of the first proportion is the first compression coefficient; the second proportion is the proportion of the predicted power consumption of the second area in the total predicted power consumption; the sum of the second proportion and the first proportion is 1; the weighted weight of the second proportion is the second compression coefficient; the power consumption constraint relationship and the preset response curve are combined to determine the first compression coefficient and the second compression coefficient; the preset response curve is a relationship curve between the proportion of the first compression coefficient in the compression coefficient and the average brightness value of the image to be processed, and in the preset response curve, the proportion of the first compression coefficient in the compression coefficient is positively correlated with the average brightness value of the image to be processed.

[0021] In a possible implementation, obtaining the desired power consumption compression coefficient includes: obtaining the desired power consumption compression coefficient set by the user; determining a compression coefficient threshold based on the average brightness value and the theoretical maximum brightness value of the image to be processed; if the desired power consumption compression coefficient set by the user is greater than the compression coefficient threshold, determining the desired power consumption compression coefficient as the compression coefficient threshold; if the desired power consumption compression coefficient set by the user is less than or equal to the compression coefficient threshold, determining the desired power consumption compression coefficient set by the user as the desired power consumption compression coefficient.

[0022] In a second aspect, the present application provides an image processing apparatus, which includes various functional modules for implementing the method described in the first aspect above.

[0023] In a third aspect, the present application provides a computer program product. When the computer program product runs on an image processing apparatus, it causes the image processing apparatus to execute the steps of the related method described in the first aspect above, so as to implement the method described in the first aspect above.

[0024] In a fourth aspect, the present application provides an image processing apparatus, which includes a processor and a memory; the memory stores instructions executable by the processor; when the processor is configured to execute the instructions, the image processing apparatus is caused to implement the method described in the first aspect above.

[0025] In a fifth aspect, the present application provides a computer-readable storage medium, which includes: software instructions; when the software instructions run in an image processing apparatus, the image processing apparatus is caused to implement the method described in the first aspect above.

[0026] The beneficial effects of the second to fifth aspects above can be referred to those described in the first aspect and will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0028] Figure 1 It is a schematic diagram of the composition of the image processing system provided by the embodiment of the present application;

[0029] Figure 2 It is a schematic diagram of the composition of the image processing apparatus provided by the embodiment of the present application;

[0030] Figure 3 It is a schematic flowchart of the image processing method provided by the embodiment of the present application;

[0031] Figure 4 Another flowchart of the image processing method provided by the embodiment of the present application;

[0032] Figure 5 The brightness value histogram provided by the embodiment of the present application;

[0033] Figure 6 The process schematic diagram for determining the brightness threshold according to the cumulative distribution function provided by the embodiment of the present application;

[0034] Figure 7 Another flowchart of the image processing method provided by the embodiment of the present application;

[0035] Figure 8 The preset response curve taking the logarithmic function curve as an example provided by the embodiment of the present application;

[0036] Figure 9 The effect schematic diagram of the image processing method provided by the embodiment of the present application;

[0037] Figure 10 Another flowchart of the image processing method provided by the embodiment of the present application;

[0038] Figure 11 The architecture schematic diagram of another image processing system provided by the embodiment of the present application;

[0039] Figure 12 Another composition schematic diagram of the image processing device provided by the embodiment of the present application. Detailed implementation manners

[0040] First, the terms related to the embodiments of the present application are introduced.

[0041] 1. Color Depth: Also known as color bit depth, it is the unit representing the number of colors in a digital image in terms of the number of bits. An image is composed of pixels, and the three primary color channels (red, green, and blue) of each pixel are mixed together to enable the image to produce many different colors. Each of the three primary color channels in a digital image has a range of values that can be assigned, and this range is stored as a number. What determines how large or small this number can be is the number of bits used by the computer to store the number. A bit is just a binary unit of information, which is displayed numerically as 0 or 1. To store increasingly complex information, the computer needs to use more bits of 0 or 1. A 1-bit integer can only have two values (0 or 1), but a 2-bit integer can have 4 values (00, 01, 10, and 11), and a 3-bit integer can have 8 values (000, 001, 010, 011, 100, 101, 110, and 111), and so on. By increasing the number of bits for each primary color channel, the computer can store more complex color information.

[0042] 2. (Brightness Value) Histogram: For an image, the (brightness value) histogram can reflect the statistical situation of different brightness values appearing in the image. The horizontal axis of the (brightness value) histogram represents the various brightness values of the image, and the vertical axis represents the number of pixel points with each brightness value in the image.

[0043] 3. Histogram Equalization: Histogram equalization is a simple and effective image enhancement technique that changes the brightness values of each pixel in the image by changing the histogram, mainly used to enhance the contrast of the image. Due to its brightness distribution, the original image may be concentrated in a relatively narrow brightness value range, resulting in an unclear image. For example, the brightness values of the pixel points in an overexposed image are mainly concentrated in the high-brightness range of the histogram, while the brightness values of the pixel points in an underexposed image are mainly concentrated in the low-brightness range of the histogram. By using histogram equalization, the histogram of the original image can be transformed into a uniform distribution (equalized) form, which increases the dynamic range of the brightness difference between pixel points. In other words, the basic principle of histogram equalization is: to broaden the brightness values with a large number of pixel points in the histogram and merge the brightness values with a small number of pixel points in the histogram, thereby increasing the contrast, making the image clear, and achieving the purpose of image enhancement.

[0044] 4. Cumulative Distribution Function (CDF): In the embodiments of this application, the meaning of the CDF(x) of the image to be processed calculated based on the (brightness value) histogram is: the proportion of the probability that the brightness value in the entire frame of the image to be processed is less than the x value. For example, CDF(16) represents the probability (expressed as a percentage) that the brightness value in the entire frame of the image to be processed is less than 16.

[0045] 5. Other terms: Hereinafter, terms such as "first" and "second" are only for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" or "second", etc. may explicitly or implicitly include one or more of such features.

[0046] With the continuous development of display technology, light-emitting diode (LED) displays have been widely used due to their advantages such as high brightness, wide viewing angle, and long lifespan.

[0047] However, the high brightness of LED displays will cause problems of high power consumption. In order to reduce the power consumption of LED displays, it is necessary to process the input image input to the LED display to reduce the brightness when the LED display shows the input image.

[0048] The current brightness control method can determine the average gray level according to the gray level data of the input image, and based on the mapping relationship between the average gray level and the brightness control coefficient, determine the target brightness control coefficient corresponding to the average gray level of the input image, and compress the input image according to the target brightness control coefficient.

[0049] However, compressing the input image using the target brightness control coefficient will affect the display quality of the image.

[0050] On this basis, the embodiments of the present application provide an image processing method, which can divide the current frame image into two regions with different brightness based on the brightness of the current frame image, and compress the two regions respectively, which can avoid the loss of details in the low-brightness background area of the current frame image, thereby avoiding the reduction of the display quality after the current frame image is compressed.

[0051] The following will be introduced in conjunction with the accompanying drawings.

[0052] Figure 1 It is a schematic diagram of the composition of the image processing system provided by the embodiments of the present application. As Figure 1 shown, the image processing system includes: an image source 100 and an image display device 200.

[0053] The image source 100 may be a computing device with computing and processing capabilities such as a computer or a server. Among them, the server may be a single server, or may also be a server cluster composed of multiple servers. In some embodiments, the server cluster may also be a distributed cluster. Optionally, the server may also be implemented on a cloud platform. For example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, and a multi-cloud, etc., or any combination thereof.

[0054] The image source 100 is used to acquire images.

[0055] For example, the image source 100 may be connected to an image acquisition device, and the image source 100 may receive the image data sent by the image acquisition device.

[0056] For another example, after the image acquisition device acquires the image data, it may store the image data in a storage device, and the image source 100 may acquire the image data from the storage device. The storage device may include various local access data storage media, such as Blu-ray discs, high-density digital video discs (DVDs), compact discs read-only memory (CD-ROMs), flash memories, or other suitable digital storage media for storing encoded video data.

[0057] For yet another example, the storage device may correspond to a file server or another intermediate storage device that stores the image data acquired by the image acquisition device. The image source 100 may acquire the stored image data from the storage device via streaming or downloading. The file server may be any type of server capable of storing image data and transmitting the image data to the image source 100. For example, the file server may include a World Wide Web (Web) server (e.g., for a website), a File Transfer Protocol (FTP) server, a Network Attached Storage (NAS) device, and a local disk drive, etc., and the embodiments of the present application do not limit this.

[0058] In some embodiments, the image source 100 is further used to send image data to the image display device 200.

[0059] As described above, the image source 100 and the image display device 200 may be connected through a wired network or a wireless network. The wired network or wireless network may include one or more media or devices capable of transmitting images from the image source 100 to the image display device 200.

[0060] In some embodiments, a wired network or a wireless network may include one or more communication media that enable the image source 100 to directly transmit image data to the image display device 200 in real time. In this embodiment, the image source 100 may modulate the image data according to a communication standard (such as a wireless communication protocol) and transmit the modulated image data to the image display device 200. The one or more communication media may include wireless and / or wired communication media, such as the radio frequency (RF) spectrum or one or more physical transmission lines. Optionally, the one or more communication media may form part of a packet-based network, which may be, for example, a local area network, a wide area network, or a global network (such as the Internet). Optionally, the one or more communication media may include routers, switches, base stations, or other devices that facilitate communication from the image source 100 to the image display device 200.

[0061] The image display device 200 is used to display images.

[0062] The image display device 200 may be an LED / liquid crystal display (LCD) monitor, an LED / LCD TV, a mobile phone, a laptop computer, an all-in-one computer, or a tablet computer, etc., which are display devices with the function of displaying images.

[0063] The image display device 200 may include a transmitting card 210 and a receiving card 220.

[0064] The transmitting card 210 is an audio-video protocol conversion system. The transmitting card 210 can strip out the image data sent by the image source 100, and after internal processing, send it to the receiving card 220. The receiving card 220 can control the LED lamp beads to emit light to display images according to the image that has undergone internal processing and is sent by the transmitting card 210.

[0065] In some embodiments, the image display device 200 or the transmitting card 210 may also be used to process the image to be processed according to the following image processing method. The specific process may be referred to as described in the following image processing method and will not be elaborated here.

[0066] It should be noted that Figure 1 Taking the image source 100 and the image display device 200 as two independent devices as an example, the image source 100 may also be integrated with the image display device 200 into one device, that is, the image source 100 or its corresponding functions, as well as the image display device 200 or its corresponding functions, may be integrated on the same device. For example, the above-mentioned mobile phones, laptop computers, tablet computers, or all-in-one computers, etc. The embodiments of the present application do not limit this.

[0067] The execution entity of the image processing method provided in the embodiments of this application is an image processing device, which may be the above-mentioned image display device 200; alternatively, the image processing device may also be the above-mentioned sending card 210; or, the image processing device may further be the processor in the sending card 210; or, the image processing device may further be an application (APP) installed in the sending card 210 that provides image processing functions; or, the image processing device may further be a functional module in the sending card 210 for executing image processing functions, etc. The embodiments of this application do not limit this.

[0068] Figure 2 It is a schematic diagram of the composition of the image processing device provided in the embodiments of this application. As Figure 2 shown, the image processing device may include: a processor 10, a memory 20, a communication line 30, a communication interface 40, and an input / output interface 50.

[0069] Among them, the processor 10, the memory 20, the communication interface 40, and the input / output interface 50 may be connected through the communication line 30.

[0070] The processor 10 is used to execute the instructions stored in the memory 20 to implement the image processing method provided in the following embodiments of this application. The processor 10 may be a central processing unit (CPU), a general-purpose processor, a network processor (NP), a digital signal processor (DSP), a microprocessor, a micro control unit (MCU) / single chip microcomputer / single-chip microcomputer, a programmable logic device (PLD), or any combination thereof. The processor 10 may also be any other device with processing functions, such as a circuit, a device, or a software module. The embodiments of this application do not limit this. In one example, the processor 10 may include one or more CPUs, such as Figure 2 the CPU0 and CPU1 in Figure 2 shown by the dashed line). As an alternative implementation, the image display device may include multiple processors. For example, in addition to the processor 10, it may further include a processor 60 (

[0071] A memory 20 for storing instructions. For example, the instructions can be a computer program. Optionally, the memory 20 can be a read-only memory (ROM) or other types of static storage devices that can store static information and / or instructions, or it can be a random access memory (RAM) or other types of dynamic storage devices that can store information and / or instructions. It can also be an electrically erasable programmable read-only memory (EEPROM), a CD-ROM or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, etc. The embodiments of the present application do not limit this.

[0072] It should be noted that the memory 20 can exist independently of the processor 10 or can be integrated with the processor 10. The memory 20 can be located inside the image display device or outside the image display device. The embodiments of the present application do not limit this.

[0073] A communication line 30 for transmitting information between the components included in the image processing device.

[0074] A communication interface 40 for communicating with other devices (such as the above-mentioned image source 100) or other communication networks. The other communication network can be an Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc. The communication interface 40 can be a module, a circuit, a transceiver or any device capable of realizing communication.

[0075] Exemplarily, the communication interface 40 can include any one of the following: a video graphics array (VGA) / D-Sub interface, a digital visual interface (DVI, such as DVI-A, DVI-D, DVI-I, etc.), a high definition multimedia interface (HDMI), a display port (DP), a universal serial bus (USB) interface, or a thunderbolt interface, etc.

[0076] The input / output interface 50 is used to implement the human-computer interaction between the user and the image processing device. For example, it can implement action interaction, text interaction, or voice interaction between the user and the image processing device, etc.

[0077] Exemplarily, the input / output interface 50 can be a keyboard, a mouse, etc. Through a keyboard, a mouse, etc., action interaction or text interaction between the user and the image processing device can be achieved.

[0078] It should be noted that Figure 2 the structure shown in Figure 2 does not constitute a limitation on the image processing device. Except for the components shown, the image processing device may include more or fewer components than those shown in the figure, or a combination of certain components, or different component arrangements.

[0079] Next, the image processing method provided by the embodiments of the present application will be introduced with reference to the accompanying drawings.

[0080] Figure 3 is a schematic flowchart of the image processing method provided by the embodiments of the present application. Optionally, this method can be executed by an image processing device having the above Figure 2 shown hardware structure. As Figure 3 shown, this method may include S101 to S106.

[0081] S101. The image processing device obtains the brightness value of each pixel point in the image to be processed.

[0082] In a possible implementation manner, the image processing device can obtain the image to be processed, convert the input image from the red green blue (RGB) color gamut (format) to the YUV color gamut, and then obtain the brightness value (Y value) of each pixel point. The specific color gamut conversion process can refer to the related art and will not be elaborated here.

[0083] S102. The image processing device determines a first region and a second region in the image to be processed based on the brightness value of each pixel point in the processed image.

[0084] Among them, the first region is the region where the pixel points with brightness values greater than the brightness threshold in the image to be processed are located. The second region is the region where the pixel points with brightness values less than the brightness threshold in the image to be processed are located.

[0085] Optionally, before S102, the image processing device can also obtain the brightness threshold. In this case, Figure 4 is another schematic flowchart of the image processing method provided by the embodiments of the present application. As Figure 4 shown, before S102, this method may further include S201 to S204.

[0086] S201. The image processing device determines the brightness distribution of the image to be processed based on the brightness values of each pixel point in the image to be processed.

[0087] Among them, the brightness distribution of the image to be processed is the number of pixel points corresponding to each brightness value in the brightness interval of the image to be processed. The brightness interval is from 0 to the theoretical maximum brightness value of the image to be processed, and the theoretical maximum brightness value is 2 to the power of N minus 1, where N is the color depth bit number of the image to be processed.

[0088] Exemplarily, taking the color depth of the image to be processed as 8 bits as an example, the theoretical maximum value of the image to be processed is 2 to the power of 8 minus 1, and the brightness interval of the image to be processed is [0, 255].

[0089] Optionally, the brightness distribution can specifically be a brightness value histogram.

[0090] Exemplarily, Figure 5 is the brightness value histogram provided by the embodiment of the present application. As Figure 5 shown, the abscissa of the brightness value histogram is the brightness value, and the value range is the brightness interval of the image to be processed. The ordinate of the brightness value histogram is the number of pixel points, that is, the number of pixel points with the brightness value of the abscissa of the histogram.

[0091] S202. The image processing device determines the cumulative distribution function of the image to be processed based on the distribution of the image to be processed.

[0092] Among them, the cumulative distribution function is used to characterize the probability that the brightness value of the pixel points in the image to be processed is less than any brightness value in the brightness interval.

[0093] S203. The image processing device selects Q brightness values corresponding to Q preset probabilities from the cumulative distribution function.

[0094] Among them, Q is an integer greater than or equal to 2. The preset probability can take any probability in the cumulative distribution function. For example, 10%, 20%, or 30%, etc. The embodiment of the present application does not limit the specific value of the preset probability.

[0095] S204. The image processing device performs weighted average on the Q brightness values according to the preset weight to obtain a brightness threshold.

[0096] Among them, the preset weight can be preset in the image processing device by the administrator through the above input / output interface. For example, the preset weight can be 1 / 3, 1 / 4, or 1 / 5, etc. The embodiment of the present application does not limit the specific value of the preset weight.

[0097] Exemplarily, Figure 6Schematic diagram of the process for determining the brightness threshold according to the cumulative distribution function provided by the embodiments of the present application. As Figure 6 shown, taking Q = 2 and the preset weight = 1 / 3 as an example, assuming that the two preset probabilities selected from the cumulative distribution function are 20% and 80% respectively, the image processing device can determine the brightness value L20 corresponding to 20% and the brightness value L80 corresponding to 80% according to the cumulative distribution function respectively, and perform weighted average on L20 and L80 according to 1 / 3 to obtain the brightness threshold alpha = (L20 + L80) / 3.

[0098] It should be noted that when the brightness value of a certain pixel point is equal to the brightness threshold, the image processing device can divide the pixel point into the first region, or divide it into the second region, etc. The embodiments of the present application do not limit this.

[0099] In some possible embodiments, after determining the first region and the second region, the image processing device can also perform equalization processing on the pixel points in the first region and the pixel points in the second region in the brightness distribution respectively. In this case, after S204 and before S103, the method may further include: the image processing device performs equalization stretching on the first region based on the first traction value; the image processing device performs equalization stretching on the second region based on the second traction value.

[0100] Among them, the first traction value is positively correlated with the brightness threshold. The second traction value is less than the first traction value. Equalization stretching means changing the number of pixel points corresponding to each brightness in the brightness distribution according to the first traction value or the second traction value, so that the number of pixel points in the brightness distribution is evenly distributed at each brightness value. For example, as described above, the brightness distribution can be specifically a brightness value histogram, then the equalization stretching is also histogram equalization. The first traction value is used to indicate the amplitude of changing the number of pixel points corresponding to the brightness values greater than the brightness threshold in the brightness distribution. The second traction value is used to indicate the amplitude of changing the number of pixel points corresponding to the brightness values less than the brightness threshold in the brightness distribution.

[0101] In a possible implementation manner, the image processing device can perform equalization stretching on the first region based on the first traction value according to the following formulas (1) and (2):

[0102]

[0103]

[0104] Among them, k1 represents any brightness value between the brightness threshold and the theoretical maximum brightness value. h k1 represents the number of pixel points corresponding to k1 in the brightness distribution. h maxRepresents the number of pixels corresponding to the brightness value with the largest number of corresponding pixels in the brightness distribution. u(H) represents the first traction value. m k1 Represents the number of pixels corresponding to k1 after equalization stretching of the first region. initial represents the minimum value of u(H). For example, initial can be set to 2 or 3, etc., and this value can be adjusted according to the actual situation. scope represents the variable range of u(H). For example, scope can be set to 3 or 4, etc., and this value can also be adjusted according to the actual situation. alpha represents the brightness threshold. S represents the value obtained by dividing the theoretical maximum brightness value plus 1 by 2. For example, taking the theoretical maximum brightness value of the to-be-processed image as 255 above, then this value can be taken as (255 + 1) / 2 = 128. gamma is a preset constant. For example, this value can be set to 2.2.

[0105] In a possible implementation, the image processing device can perform equalization stretching on the second region based on the second traction value according to the following formulas (3) and (4):

[0106]

[0107] u(L)+1+log(1+u(H)) Formula (4)

[0108] Among them, k2 represents any brightness value between 0 and the brightness threshold. h k2 Represents the number of pixels corresponding to k2 in the brightness distribution. u(L) represents the second traction value. m k2 Represents the number of pixels corresponding to k2 after equalization stretching of the second region.

[0109] It should be understood that the brightness value of the second region is lower than that of the first region, and the second traction value is less than the first traction value, which means that the amplitude of transforming the number of pixels with the brightness value of the second region in the brightness distribution is smaller. In the embodiments of the present application, the image processing device performs equalization stretching on the low-brightness region (the second region) with a lower brightness value with a lower amplitude, which can avoid obvious noise or image distortion caused by excessive stretching of the low-brightness region while equalizing the overall image contrast.

[0110] Optionally, after performing equalization stretching on the brightness distribution of the to-be-processed image, the image processing device can obtain a brightness mapping table Y1. Y1 includes the mapping coefficients corresponding to each pixel point in the to-be-processed image, and this mapping coefficient is used to represent the relationship between the original brightness value of the pixel point in the to-be-processed image and the brightness value of the pixel point in the to-be-processed image after equalization stretching. Then the brightness threshold alpha can obtain alpha_new after being mapped by Y1.

[0111] S103. The image processing device obtains a first compression coefficient corresponding to a first region and a second compression coefficient corresponding to a second region.

[0112] Among them, the first compression coefficient is used to characterize the reduction amplitude of the luminance value of each pixel point in the first region. The second compression coefficient is used to characterize the reduction amplitude of the luminance value of each pixel point in the second region. The proportion of the first compression coefficient in the compression coefficients is positively correlated with the average luminance value of the image to be processed. The compression coefficient is the sum of the first compression coefficient and the second compression coefficient, and the compression coefficient is a fixed value.

[0113] In a possible implementation, the image processing device can determine the first compression coefficient and the second compression coefficient respectively based on the predicted power consumption of the first region and the second region. In this case, Figure 7 is another flowchart of the image processing method provided by the embodiments of the present application. As Figure 7 shown, S103 may specifically include S301 to S305.

[0114] S301. The image processing device determines a first predicted power consumption of the first region according to the sum of the squares of the luminance values of the pixel points in the first region.

[0115] Optionally, for a self-luminous LED display screen, since each LED lamp bead is self-luminous, its display power consumption can be approximately regarded as the square of the luminance value. In this case, the image processing device can calculate the first predicted power consumption of the first region according to the following formula (5):

[0116]

[0117] In formula (5), P high represents the first predicted power consumption. M represents the number of pixel points in the first region. Y h(j) represents the luminance value of the pixel points in the first region.

[0118] S302. The image processing device determines the total predicted power consumption of the image to be processed according to the sum of the squares of the luminance values of the pixel points in the image to be processed.

[0119] Optionally, based on a principle similar to the above formula (5), the image processing device can calculate the total predicted power consumption according to the following formula (6):

[0120]

[0121] In formula (6), P represents the total predicted power consumption. width represents the width of the image to be processed, in pixel points. high represents the height of the image to be processed, in pixel points. Y (i) represents the luminance value of the pixel points in the image to be processed.

[0122] It should be noted that the above approximate determination of the predicted power consumption based on the sum of the squares of the luminance values is for the convenience of calculation. In actual calculation, in order to improve the accuracy, the predicted power consumption can also be calculated based on the sum of the 2.2 powers of the luminance values. The embodiments of the present application do not limit this.

[0123] S303. The image processing device obtains the expected power consumption compression coefficient.

[0124] Among them, the expected power consumption compression coefficient is used to represent the expected reduction amplitude of the power consumption required to display the image to be processed after reducing the luminance value of the pixel points in the image to be processed. For example, taking the expected power consumption compression coefficient as 30% as an example, this coefficient means that the predicted power consumption of the processed image is reduced by 30%, that is, 70% of the predicted power consumption of directly displaying the image to be processed.

[0125] In a possible implementation manner, the image processing device can obtain the expected power consumption compression coefficient set by the user and modulate the expected power consumption compression coefficient set by the user. In this case, the above S303 can specifically include the following steps:

[0126] Step 1. The image processing device obtains the expected power consumption compression coefficient set by the user.

[0127] For example, the image processing device can receive the expected power consumption compression coefficient input by the user through the above input / output interface.

[0128] Step 2. The image processing device determines the compression coefficient threshold based on the average luminance value and the theoretical maximum luminance value of the image to be processed.

[0129] Optionally, the image processing device can calculate the average luminance value of the image to be processed according to the following formula (7):

[0130]

[0131] In formula (7), mean y represents the average luminance value of the image to be processed. total_num represents the number of pixel points in the image to be processed.

[0132] Optionally, the image processing device can calculate the compression coefficient threshold according to the following formula (8):

[0133]

[0134] In formula (8), K temp represents the compression coefficient threshold. 255 is the theoretical maximum luminance value of the image to be processed. Here, it is exemplified by the color depth of the image to be processed being 8 bits. In actual calculation, this value can change with the change of the color depth of the image to be processed.

[0135] Step 3: If the expected power consumption compression factor set by the user is greater than the compression factor threshold, the expected power consumption threshold is determined as the compression factor threshold.

[0136] Step 4: If the expected power consumption compression coefficient set by the user is less than or equal to the compression coefficient threshold, the expected power consumption compression coefficient set by the user is determined as the expected power consumption compression coefficient.

[0137] S304: The image processing apparatus determines a power consumption constraint relationship based on the first predicted power consumption, the total predicted power consumption, and the expected power consumption compression factor.

[0138] The power consumption constraint relationship is: the result of the weighted sum of the first ratio and the second ratio is the expected power consumption compression factor. The first ratio is the proportion of the first predicted power consumption in the total predicted power consumption, that is, P1 (first ratio) = P high / P. The second ratio is the proportion of the predicted power consumption of the second area in the total predicted power consumption, that is, P2 (second ratio) = 1-P1. The weighted weight of the first ratio is the first compression coefficient. The weighted weight of the second ratio is the second compression coefficient.

[0139] Exemplarily, the power consumption constraint relationship may be shown as the following formula (9):

[0140] P1×K1+P2×K2=K X Formula (9)

[0141] In formula (9), P1 represents the first ratio. K1 represents the first compression coefficient. P2 represents the second ratio. K2 represents the second compression coefficient. X Represents the expected power consumption compression factor.

[0142] S305: The image processing apparatus combines the power consumption constraint relationship and the preset response curve to determine a first compression coefficient and a second compression coefficient.

[0143] Among them, the preset response curve is a relationship curve between the proportion of the first compression coefficient in the compression coefficient and the average brightness value of the image to be processed. In the preset response curve, the proportion of the first compression coefficient in the compression coefficient is positively correlated with the average brightness value of the image to be processed.

[0144] Optionally, the preset response curve may be a piecewise function, a linear function curve, or a logarithmic function curve with a decreasing slope, etc. This embodiment of the present application does not limit this.

[0145] Optionally, the image processing device may adaptively distribute the expected power consumption compression coefficient to the first compression coefficient and the second compression coefficient in proportion according to the content of the image to be processed (for example, the average brightness value), and the adaptive strategy is shown in the following Table 1:

[0146] Table 1

[0147]

[0148] Exemplarily, Figure 8 It is a preset response curve provided by an embodiment of the present application, taking a logarithmic function curve as an example. As Figure 8 shown, the preset response curve is a logarithmic function curve. The ordinate of the logarithmic function curve is the proportion of the first compression coefficient in the compression coefficient, and the abscissa of the logarithmic function curve is the average brightness value of the image to be processed. The value range of the logarithmic function is from a preset lowest threshold low_coef to the highest threshold up_coef. As Figure 8 shown, as the average brightness value of the image to be processed increases, the proportion of the expected power consumption compression coefficient borne by the highlighted first region increases.

[0149] Optionally, the image processing device may first calculate the average brightness value of the image to be processed, and then use the average brightness value as the abscissa to query the above preset response curve to obtain the proportion of the first compression coefficient corresponding to the average brightness value in the compression coefficient. Based on the fixed expected power consumption compression coefficient, the obtained first ratio, second ratio, and the ratio of the first compression coefficient in the compression coefficient, the first compression coefficient and the second compression coefficient are determined respectively.

[0150] S104. The image processing device compresses the brightness values of the pixel points in the first region based on the first compression coefficient, and compresses the brightness values of the pixel points in the second region based on the second compression coefficient.

[0151] Optionally, as described above, after performing equalization stretching on the brightness distribution of the image to be processed, the image processing device may obtain a brightness mapping table Y1. After the brightness threshold alpha is mapped by Y1, alpha_new can be obtained. Here, being mapped by Y1 means multiplying the brightness value by the relationship between the brightness values before and after compression in Y1. The brightness threshold here may be multiplied by the relationship corresponding to the first region in Y1, or the relationship corresponding to the second region, or the average value of the relationships corresponding to the first region and the second region, etc. The embodiments of the present application do not limit this. Then the image processing device may specifically compress the brightness values of the pixel points in the first region based on the first compression coefficient according to the following formula (10):

[0152] Y NEW_H = alpha new +(Y1 - alpha new )×(1 - K1) Formula (10)

[0153] In formula (10), Y NEW_HRepresents the first luminance mapping table corresponding to the first region. The first luminance mapping table can be referred to as described in the above luminance mapping table. The first luminance mapping table can specifically include the mapping coefficients corresponding to each pixel point in the first region, and the mapping coefficients are used to characterize the correspondence between the luminance values of each pixel point in the first region after the above-mentioned equalization stretching and the target luminance values (to be displayed) after compression processing. Y1-alpha new Represents the relationship (coefficient) between the luminance values before and after compression in Y1 minus alpha new A resulting intermediate mapping table after that.

[0154] Optionally, the image processing device can specifically compress the luminance values of the pixel points in the second region based on the second compression coefficient according to the following formula (11):

[0155] Y NEW_L = Y1 × (1 - K2) Formula (11)

[0156] In Formula (11), Y NEW_L Represents the second luminance mapping table corresponding to the second region. The second luminance mapping table can specifically include the mapping coefficients corresponding to each pixel point in the second region, and the mapping coefficients are used to characterize the correspondence between the luminance values of each pixel point in the second region after the above-mentioned equalization stretching and the target luminance values (to be displayed) after being compressed by the second compression coefficient.

[0157] S105. The image processing device determines the target luminance mapping table according to the relationship between the luminance values before and after compression of each pixel point in the image to be processed.

[0158] For example, the image processing device can combine the above first luminance mapping table and the second luminance mapping table to obtain the target luminance mapping table.

[0159] S106. The image processing device sends the target luminance mapping table to the sending card so that the image display device displays the image to be processed according to the compressed luminance values.

[0160] S106 can be referred to as described in the related art and will not be elaborated here. It should be noted that the above introduces the image processing method provided by the embodiments of the present application by taking one frame of the image to be processed as an example. This one frame of the image to be processed can also be understood as the current frame image, that is, for each frame of the image sent by the image source 100, the image processing device can process it according to the above image processing method.

[0161] Exemplarily, Figure 9 Is a schematic diagram of the effect of the image processing method provided by the embodiments of the present application. As Figure 9As shown, the area greater than the brightness threshold can be determined as the highlighted first area, and the amplitude of the equalization and stretching of the first area is relatively large, and the first compression coefficient corresponding to the first area is relatively large; the area less than the brightness threshold can be determined as the low-brightness second area, and the amplitude of the equalization and stretching of the second area is relatively small, and the second compression coefficient corresponding to the second area is relatively small. After such processing, the contrast of the image can be improved and the display effect can be optimized.

[0162] In the image processing method provided by the embodiment of the present application, the image processing device can divide the image into a highlighted first area and a low-brightness second area based on the brightness value of each pixel point in the image to be processed. For the highlighted first area, the image processing device can compress the brightness value of the pixel points in the first area by using the first compression coefficient with a relatively large reduction amplitude; for the low-brightness second area, the image processing device can compress the brightness value of the pixel points in the second area by using the second compression coefficient with a relatively small reduction amplitude. The change amplitude of the brightness value of the pixel points in the low-brightness area is relatively small, and the change amplitude of the brightness value of the pixel points in the highlighted area is relatively large, which can improve the contrast of the image to be processed and retain the detailed content in the low-brightness area, so as to optimize the display quality after compression on the premise of reducing the overall power consumption of the LED screen.

[0163] Based on the understanding of the above embodiments, Figure 10 is another flow schematic diagram of the image processing method provided by the embodiment of the present application. As Figure 10 shown, the image processing method mainly includes S401 to S406.

[0164] S401. The image processing device extracts the brightness value of the image to be processed.

[0165] S401 can refer to S101 described above and will not be elaborated here.

[0166] S402. The image processing device statistically analyzes the brightness value histogram.

[0167] S402 can refer to S201 described above and will not be elaborated here.

[0168] S403. The image processing device calculates the brightness threshold.

[0169] S403 can refer to S201 to S204 described above and will not be elaborated here.

[0170] S404. The image processing device performs piecewise equalization on the brightness value histogram.

[0171] S404 can refer to that described in formulas (1) to (4) above and will not be elaborated here.

[0172] S405. The image processing device performs piecewise optimal solution on the power consumption.

[0173] S405 can be referred to the above S301 to S305, and will not be elaborated here.

[0174] S406. The image processing device outputs a target brightness mapping table.

[0175] S406 can be referred to the above where the first brightness mapping table and the second brightness mapping table are combined to obtain the target brightness mapping table, and will not be elaborated here.

[0176] Based on the understanding of the above embodiments, Figure 11 is a schematic structural diagram of another image processing system provided by the embodiments of the present application. As Figure 11 shown, the image processing system may include an LED sending card and an LED receiving card.

[0177] The LED sending card can receive the image to be processed input by the image source, perform color gamut conversion on the image to be processed, convert the RGB color gamut to the YUV color gamut, statistically calculate the brightness value of each pixel point of the image to be processed according to the histogram, perform adaptive parameter calculation according to the obtained histogram to obtain a brightness threshold and an expected power consumption compression coefficient, perform piecewise histogram equalization to enhance the contrast according to the brightness threshold, perform piecewise optimal solution according to the expected power consumption compression coefficient to obtain a target brightness mapping table, and send the target brightness mapping table to the LED receiving card through the transmission channel.

[0178] The LED receiving card can receive the target brightness mapping table sent by the LED sending card, map the original brightness value of each pixel point in the image to be processed according to the target brightness mapping table to obtain the target brightness value of each pixel point, and then perform color gamut conversion to convert the YUV color gamut to the RGB color gamut to display the image.

[0179] The above mainly introduces the solution provided by the embodiments of the present application from the perspective of methods. To implement the above functions, it includes the corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should easily realize that, combining the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0180] In an exemplary embodiment, the embodiments of the present application further provide an image processing device, Figure 12 is another schematic composition diagram of the image processing device provided by the embodiments of the present application. As Figure 12As described above, the device includes an acquisition module 1201, a processing module 1202, and a transmission module 1203.

[0181] The acquisition module 1201 is configured to acquire the luminance value of each pixel point in the image to be processed.

[0182] The processing module 1202 is configured to determine a first region and a second region in the image to be processed based on the luminance value of each pixel point in the image to be processed; the luminance value of any pixel point in the first region is greater than the luminance value of the pixel point with the highest luminance value in the second region; acquire a first compression coefficient corresponding to the first region and a second compression coefficient corresponding to the second region; the first compression coefficient is used to characterize the reduction amplitude of the luminance value of each pixel point in the first region; the second compression coefficient is used to characterize the reduction amplitude of the luminance value of each pixel point in the second region; the proportion of the first compression coefficient in the compression coefficient is positively correlated with the average luminance value of the image to be processed; the compression coefficient is the sum of the first compression coefficient and the second compression coefficient; the compression coefficient is a fixed value; compress the luminance value of the pixel points in the first region based on the first compression coefficient, and compress the luminance value of the pixel points in the second region based on the second compression coefficient; determine the target luminance mapping table according to the relationship between the luminance values of each pixel point in the image to be processed before and after compression.

[0183] The transmission module 1203 is configured to send the target luminance mapping table to the receiving card, so that the image display device displays the image to be processed according to the compressed luminance value.

[0184] In some possible embodiments, the processing module 1202 is further configured to determine the luminance distribution of the image to be processed based on the luminance value of each pixel point in the image to be processed before determining the first region and the second region in the image to be processed based on the luminance value of each pixel point in the image to be processed; the luminance distribution is the number of pixel points corresponding to each luminance value in the luminance interval of the image to be processed; the luminance interval is from 0 to the theoretical maximum luminance value of the image to be processed; the theoretical maximum luminance value is 2 to the power of N minus 1; N is the color depth bit number of the image to be processed; determine the cumulative distribution function of the image to be processed based on the luminance distribution of the image to be processed; the cumulative distribution function is used to characterize the probability that the luminance value of the pixel points in the image to be processed is less than any luminance value in the luminance interval; select Q luminance values corresponding to Q preset probabilities from the cumulative distribution function; Q is an integer greater than or equal to 2; perform weighted average on the Q luminance values according to the preset weight to obtain the luminance threshold.

[0185] In some other possible embodiments, the processing module 1202 is further configured to, before obtaining the first compression coefficient corresponding to the first region and the second compression coefficient corresponding to the second region, perform equalization stretching on the first region based on the first traction value and perform equalization stretching on the second region based on the second traction value; the first traction value is positively correlated with the brightness threshold; the second traction value is less than the first traction value; equalization stretching means transforming the number of pixel points corresponding to each brightness value in the brightness distribution according to the first traction value or the second traction value; the first traction value is used to indicate the amplitude of the transformation of the number of pixel points corresponding to the brightness values greater than the brightness threshold in the brightness distribution; the second traction value is used to indicate the amplitude of the transformation of the number of pixel points corresponding to the brightness values less than the brightness threshold in the brightness distribution.

[0186] In some other possible embodiments, the processing module 1202 is specifically configured to perform equalization stretching on the first region based on the first traction value according to the following formula:

[0187]

[0188]

[0189] where k1 represents any brightness value between the brightness threshold and the theoretical maximum brightness value; h k1 represents the number of pixel points corresponding to k1 in the brightness distribution; h max represents the number of pixel points corresponding to the brightness value with the largest number of corresponding pixel points in the brightness distribution; u(H) represents the first traction value. m k1 represents the number of pixel points corresponding to k1 after performing equalization stretching on the first region; initial represents the minimum value of u(H); scope represents the variable range of u(H); alpha represents the brightness threshold; S represents the value obtained by dividing the theoretical maximum brightness value plus 1 by 2; gamma is a preset constant;

[0190] Perform equalization stretching on the second region based on the second traction value according to the following formula:

[0191]

[0192] u(L)+1+log(1+u(H))

[0193] where k2 represents any brightness value between 0 and the brightness threshold; h k2 represents the number of pixel points corresponding to k2 in the brightness distribution; u(L) represents the second traction value; m k2 represents the number of pixel points corresponding to k2 after performing equalization stretching on the second region.

[0194] In some other possible embodiments, the processing module 1202 is specifically configured to determine a first predicted power consumption of the first region according to the sum of the squares of the luminance values of the pixel points in the first region; determine the total predicted power consumption of the image to be processed according to the sum of the squares of the luminance values of the pixel points in the image to be processed; obtain an expected power consumption compression coefficient, where the expected power consumption compression coefficient is used to represent the expected reduction amplitude of the power consumption required to display the image to be processed after reducing the luminance values of the pixel points in the image to be processed; determine a power consumption constraint relationship based on the first predicted power consumption, the total predicted power consumption, and the expected power consumption compression coefficient. The power consumption constraint relationship is that the weighted sum of the first ratio and the second ratio is the expected power consumption compression coefficient. The first ratio is the proportion of the first predicted power consumption in the total predicted power consumption, and the weighted weight of the first ratio is the first compression coefficient. The second ratio is the proportion of the predicted power consumption of the second region in the total predicted power consumption, and the sum of the second ratio and the first ratio is 1. The weighted weight of the second ratio is the second compression coefficient. By combining the power consumption constraint relationship and the preset response curve, the first compression coefficient and the second compression coefficient are determined. The preset response curve is a relationship curve between the proportion of the first compression coefficient in the compression coefficients and the average luminance value of the image to be processed. In the preset response curve, the proportion of the first compression coefficient in the compression coefficients is positively correlated with the average luminance value of the image to be processed.

[0195] In some other possible embodiments, the processing module 1202 is specifically configured to obtain the expected power consumption compression coefficient set by the user; determine a compression coefficient threshold based on the average luminance value and the theoretical maximum luminance value of the image to be processed; if the expected power consumption compression coefficient set by the user is greater than the compression coefficient threshold, determine that the expected power consumption compression coefficient is the compression coefficient threshold; if the expected power consumption compression coefficient set by the user is less than or equal to the compression coefficient threshold, determine that the expected power consumption compression coefficient set by the user is the expected power consumption compression coefficient.

[0196] It should be noted that Figure 12 The division of the modules in [the relevant content] is illustrative. It is only a logical function division, and there may be other division methods in actual implementation. For example, two or more functions can also be integrated into one processing module. The embodiments of the present application do not limit this. The above integrated module can be implemented in the form of hardware or in the form of a software function module.

[0197] In an exemplary embodiment, the embodiments of the present application further provide a readable storage medium, including: execution instructions, which when running on an image processing device, cause the image processing device to execute any one of the methods provided in the above embodiments.

[0198] In an exemplary embodiment, the embodiments of the present application further provide a computer program product including computer execution instructions, which when running on an image processing device, cause the image processing device to execute any one of the methods provided in the above embodiments.

[0199] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using a software program, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer-executable instructions. When the computer-executable instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer-executable instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer-executable instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more integrated media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0200] Although the present application has been described in conjunction with various embodiments, however, in the process of implementing the claimed present application, those skilled in the art can understand and realize other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "one" does not exclude a plurality. A single processor or other unit can implement several functions recited in the claims. Certain measures are recited in mutually different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0201] Although the present application has been described in conjunction with specific features and their embodiments, it is obvious that various modifications and combinations can be made without departing from the spirit and scope of the present application. Accordingly, the present specification and the drawings are merely exemplary illustrations of the present application defined by the appended claims and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these changes and modifications.

[0202] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An image processing method, characterized in that, The method is applied to a transmitting card in an image display device; the image display device further includes a receiving card; the method includes: Obtain the brightness value of each pixel point in the image to be processed; Based on the brightness value of each pixel point in the image to be processed, determine a first region and a second region in the image to be processed; the first region is the region where the pixel points with brightness values greater than the brightness threshold are located in the image to be processed; the second region is the region where the pixel points with brightness values less than the brightness threshold are located in the image to be processed; Obtain a first compression coefficient corresponding to the first region and a second compression coefficient corresponding to the second region; the first compression coefficient is used to characterize the reduction amplitude of the brightness value of each pixel point in the first region; the second compression coefficient is used to characterize the reduction amplitude of the brightness value of each pixel point in the second region; the proportion of the first compression coefficient in the compression coefficients is positively correlated with the average brightness value of the image to be processed; the compression coefficient is the sum of the first compression coefficient and the second compression coefficient; compress the brightness value of the pixel points in the first region based on the first compression coefficient, and compress the brightness value of the pixel points in the second region based on the second compression coefficient; the reduction amplitude of the first compression coefficient for the brightness value is greater than the reduction amplitude of the second compression coefficient for the brightness value; Determine a target brightness mapping table according to the relationship between the brightness values of each pixel point in the image to be processed before and after compression; Send the target brightness mapping table to the receiving card, so that the image display device displays the image to be processed according to the compressed brightness value.

2. The method according to claim 1, wherein The method further includes: Before determining the first region and the second region in the image to be processed based on the brightness value of each pixel point in the image to be processed, determine the brightness distribution of the image to be processed based on the brightness value of each pixel point in the image to be processed; the brightness distribution is the number of pixel points corresponding to each brightness value in the brightness interval of the image to be processed; the brightness interval is from 0 to the theoretical maximum brightness value of the image to be processed; the theoretical maximum brightness value is 2 to the power of N minus 1; N is the color depth bit number of the image to be processed; Based on the brightness distribution of the image to be processed, determine the cumulative distribution function of the image to be processed; the cumulative distribution function is used to characterize the probability that the brightness value of the pixel points in the image to be processed is less than any brightness value in the brightness interval; Select Q brightness values corresponding to Q preset probabilities from the cumulative distribution function; Q is an integer greater than or equal to 2; Perform weighted average on the Q brightness values according to a preset weight to obtain the brightness threshold.

3. The method according to claim 2, characterized in that, The method further includes: Before obtaining the first compression coefficient corresponding to the first area and the second compression coefficient corresponding to the second area, the first area is evenly pulled up based on the first pulling value, and the second area is evenly pulled up based on the second pulling value; the first pulling value is positively correlated with the brightness threshold; the second pulling value is less than the first pulling value; the even pulling refers to transforming the number of pixel points corresponding to each brightness value in the brightness distribution according to the first pulling value or the second pulling value; the first pulling value is used to indicate the amplitude of the transformation of the number of pixel points corresponding to the brightness values greater than the brightness threshold in the brightness distribution; the second pulling value is used to indicate the amplitude of the transformation of the number of pixel points corresponding to the brightness values less than the brightness threshold in the brightness distribution.

4. The method according to claim 3, characterized in that The performing balanced pulling of the first region based on the first pulling value includes: performing balanced pulling of the first region based on the first pulling value according to the following formula: Among them, k1 represents any luminance value between the luminance threshold and the theoretical maximum luminance value; h k1 represents the number of pixel points corresponding to k1 in the luminance distribution; h max represents the number of pixel points corresponding to the luminance value with the largest number of corresponding pixel points in the luminance distribution; u(H) represents the first traction value; m k1 represents the number of pixel points corresponding to k1 after equalizing and stretching the first region; initial represents the minimum value of u(H); scope represents the variable range of u(H); alpha represents the luminance threshold; S represents the value obtained by dividing the theoretical maximum luminance value plus 1 by 2; gamma is a preset constant; The step of evenly pulling up the second region based on the second pulling value includes evenly pulling up the second region based on the second pulling value according to the following formula: u(L)+1+log(1+u(H)) Among them, k2 represents any luminance value between 0 and the luminance threshold; h k2 represents the number of pixel points corresponding to k2 in the luminance distribution; u(L) represents the second traction value; m k2 represents the number of pixel points corresponding to k2 after equalizing and stretching the second region.

5. The method according to any one of claims 1-4, characterized in that, The obtaining a first compression coefficient corresponding to the first region and a second compression coefficient corresponding to the second region includes: determining a first predicted power consumption of the first area according to a sum of squares of brightness values of pixels in the first area; Determining the total predicted power consumption of the image to be processed according to the sum of squares of brightness values of pixels in the image to be processed; Obtaining an expected power consumption compression factor; the expected power consumption compression factor is used to indicate an expected reduction in power consumption required to display the image to be processed after reducing the brightness value of a pixel in the image to be processed; Based on the first predicted power consumption, the total predicted power consumption, and the expected power consumption compression coefficient, a power consumption constraint relationship is determined; the power consumption constraint relationship is: a result of a weighted sum of a first ratio and a second ratio is the expected power consumption compression coefficient; the first ratio is a proportion of the first predicted power consumption in the total predicted power consumption; the weighted weight of the first ratio is the first compression coefficient; the second ratio is a proportion of the predicted power consumption of the second region in the total predicted power consumption; the sum of the second ratio and the first ratio is 1; the weighted weight of the second ratio is the second compression coefficient; The first compression coefficient and the second compression coefficient are determined by combining the power consumption constraint relationship and the preset response curve; the preset response curve is a relationship curve between the proportion of the first compression coefficient in the compression coefficients and the average brightness value of the image to be processed, and in the preset response curve, the proportion of the first compression coefficient in the compression coefficients is positively correlated with the average brightness value of the image to be processed.

6. The method according to claim 5, wherein The obtaining of the expected power consumption compression factor includes: Obtain the expected power consumption compression factor set by the user; Determining a compression coefficient threshold based on an average brightness value and a theoretical maximum brightness value of the image to be processed; If the expected power consumption compression coefficient set by the user is greater than the compression coefficient threshold, determine the expected power consumption compression coefficient as the compression coefficient threshold; If the expected power consumption compression coefficient set by the user is less than or equal to the compression coefficient threshold, determine the expected power consumption compression coefficient set by the user as the expected power consumption compression coefficient.

7. An image processing apparatus, characterized in that, The device is applied to a transmitting card in an image display device; the image display device further includes a receiving card; the device includes: an acquisition module, a processing module, and a transmitting module; The acquisition module is used to acquire the luminance value of each pixel point in the image to be processed; The processing module is used to determine a first region and a second region in the image to be processed based on the luminance value of each pixel point in the image to be processed; the first region is the region where the pixel points with luminance values greater than the luminance threshold in the image to be processed are located; the second region is the region where the pixel points with luminance values less than the luminance threshold in the image to be processed are located; obtain a first compression coefficient corresponding to the first region and a second compression coefficient corresponding to the second region; the first compression coefficient is used to characterize the reduction amplitude of the luminance value of each pixel point in the first region; the second compression coefficient is used to characterize the reduction amplitude of the luminance value of each pixel point in the second region; the proportion of the first compression coefficient in the compression coefficient is positively correlated with the average luminance value of the image to be processed; the compression coefficient is the sum of the first compression coefficient and the second compression coefficient; compress the luminance value of the pixel points in the first region based on the first compression coefficient; reduce the luminance value of the pixel points in the second region based on the second compression coefficient; the reduction amplitude of the luminance value by the first compression coefficient is greater than the reduction amplitude of the luminance value by the second compression coefficient; determine a target luminance mapping table according to the relationship between the luminance values of each pixel point in the image to be processed before and after compression; The transmitting module is used to send the target luminance mapping table to the receiving card, so that the image display device displays the image to be processed according to the compressed luminance value.

8. The device according to claim 7, wherein The processing module is used to determine the luminance distribution of the image to be processed based on the luminance value of each pixel point in the image to be processed before determining the first region and the second region in the image to be processed based on the luminance value of each pixel point in the image to be processed; the luminance distribution is the number of pixel points corresponding to each luminance value in the luminance interval of the image to be processed; The luminance interval is from 0 to the theoretical maximum luminance value of the image to be processed; the theoretical maximum luminance value is 2 to the power of N minus 1; N is the color depth bit number of the image to be processed; Determine the cumulative distribution function of the image to be processed based on the luminance distribution of the image to be processed; the cumulative distribution function is used to characterize the probability that the luminance value of the pixel points in the image to be processed is less than any luminance value in the luminance interval; select Q luminance values corresponding to Q preset probabilities from the cumulative distribution function; The Q is an integer greater than or equal to 2; the Q brightness values are weighted averaged according to a preset weight to obtain a brightness threshold; and / or, The processing module is further configured to, before acquiring the first compression coefficient corresponding to the first region and the second compression coefficient corresponding to the second region, perform balanced pulling on the first region based on the first pulling value and perform balanced pulling on the second region based on the second pulling value; The first traction value is positively correlated with the brightness threshold; The second traction value is less than the first traction value; The balanced pull-up refers to changing the number of pixel points corresponding to each brightness value in the brightness distribution according to the first pulling value or the second pulling value; the first pulling value is used to indicate the amplitude of changing the number of pixel points corresponding to the brightness values greater than the brightness threshold in the brightness distribution; the second pulling value is used to indicate the amplitude of changing the number of pixel points corresponding to the brightness values less than the brightness threshold in the brightness distribution; and / or, The processing module is specifically configured to perform balanced pulling on the first region based on the first pulling value according to the following formula: Among them, k1 represents any luminance value between the luminance threshold and the theoretical maximum luminance value; h k1 represents the number of pixel points corresponding to k1 in the luminance distribution; h max represents the number of pixel points corresponding to the luminance value with the largest number of corresponding pixel points in the luminance distribution; u(H) represents the first traction value; m k1 represents the number of pixel points corresponding to k1 after equalizing and stretching the first region; initial represents the minimum value of u(H); scope represents the variable range of u(H); alpha represents the luminance threshold; S represents the value obtained by dividing the theoretical maximum luminance value plus 1 by 2; gamma is a preset constant; The second area is pulled up evenly based on the second pulling value according to the following formula: u(L)+1+log(1+u(H)) Among them, k2 represents any luminance value between 0 and the luminance threshold; h k2 represents the number of pixel points corresponding to k2 in the luminance distribution; u(L) represents the second traction value; m k2 represents the number of pixel points corresponding to k2 after equalization stretching of the second region; and / or, The processing module is specifically used to determine the first predicted power consumption of the first area according to the sum of the squares of the brightness values of the pixels in the first area; determine the total predicted power consumption of the image to be processed according to the sum of the squares of the brightness values of the pixels in the image to be processed; obtain an expected power consumption compression coefficient; the expected power consumption compression coefficient is used to indicate the expected reduction in power consumption required to display the image to be processed after reducing the brightness values of the pixels in the image to be processed; determine a power consumption constraint relationship based on the first predicted power consumption, the total predicted power consumption, and the expected power consumption compression coefficient; the power consumption constraint relationship is: the result of the weighted sum of the first ratio and the second ratio is the expected power consumption compression coefficient; the first ratio is The first predicted power consumption accounts for the total predicted power consumption; the weighted weight of the first ratio is the first compression coefficient; the second ratio is the ratio of the predicted power consumption of the second area to the total predicted power consumption; the sum of the second ratio and the first ratio is 1; the weighted weight of the second ratio is the second compression coefficient; the power consumption constraint relationship and the preset response curve are combined to determine the first compression coefficient and the second compression coefficient; the preset response curve is a relationship curve between the proportion of the first compression coefficient in the compression coefficient and the average brightness value of the image to be processed, and in the preset response curve, the proportion of the first compression coefficient in the compression coefficient is positively correlated with the average brightness value of the image to be processed; and / or, The processing module is specifically configured to obtain an expected power consumption compression coefficient set by a user; determine a compression coefficient threshold based on an average luminance value and a theoretical maximum luminance value of the image to be processed; if the expected power consumption compression coefficient set by the user is greater than the compression coefficient threshold, determine the expected power consumption compression coefficient as the compression coefficient threshold; if the expected power consumption compression coefficient set by the user is less than or equal to the compression coefficient threshold, determine the expected power consumption compression coefficient set by the user as the expected power consumption compression coefficient.

9. An image processing apparatus, characterized in that, The image processing apparatus includes: a processor and a memory; The memory stores instructions executable by the processor; When the processor is configured to execute the instructions, the image processing apparatus implements the method according to any one of claims 1-6.

10. A readable storage medium, characterized in that, The readable storage medium includes: software instructions; When the software instructions run in the image processing apparatus, the image processing apparatus implements the method according to any one of claims 1-6.

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