Dynamic display method of sub-pixel LED display screen

Abstract

The invention discloses a dynamic display method for a sub-pixel LED display screen, and relates to the technical field of LED display control, a processing end reconstructs a virtual pixel grid in an image space by using an embedded sub-pixel mapping algorithm, maps color information of each virtual pixel to a plurality of adjacent physical sub-pixels, identifies a motion area in an image, and sends the motion area to the processing end; and performing adaptive adjustment on a virtual pixel reconstruction mode of the motion area, generating driving data for controlling each physical sub-pixel by a control end according to a virtual pixel mapping result and a dynamic enhancement strategy, and transmitting the driving data to an LED module so as to control the display of an LED display screen. According to the method, a virtual pixel grid is reconstructed through an embedded sub-pixel mapping algorithm, brightness and color independent control of a sub-pixel level is realized, an image with higher pixel density is constructed on the basis of the same hardware, fine display control of'dynamic and static separation 'is realized in the whole image, and the overall image perception quality is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of LED display control, and particularly relates to a dynamic display method for a sub-pixel LED display screen. Background Art

[0002] With the continuous development of information display technology, LED display screens have been widely used in many fields such as advertising, stage performances, traffic guidance, security monitoring, and intelligent terminals. Especially in scenarios with high requirements for display effects, people have put forward higher demands for aspects such as image display clarity, color restoration, and dynamic response capabilities.

[0003] The existing technology has the following deficiencies:

[0004] 1. Traditional LED display screens usually form a complete pixel point with each group of red, green, and blue sub-pixels, and the display content is based on whole-pixel control. The independent display capabilities of each sub-pixel cannot be fully utilized, resulting in the physical pixel count directly restricting the image resolution. Especially in scenarios with small pixel pitches or requiring fine display (such as high-definition demonstrations, vehicle instrument panels, remote conferences, etc.), problems such as blurred image edges and missing details are more obvious;

[0005] 2. And LED display screens usually only perform interpolation reconstruction or sub-pixel mapping on a single-frame image, without fully utilizing the pixel evolution law between consecutive frames, resulting in insufficient pixel time multiplexing. The system cannot achieve efficient precision enhancement when processing consecutive dynamic images, especially lacking performance in application scenarios requiring high refresh rates and high response displays.

[0006] Based on this, the present invention proposes a dynamic display method for a sub-pixel LED display screen. By reconstructing a virtual pixel grid through an embedded sub-pixel mapping algorithm, independent control of brightness and color at the sub-pixel level is achieved. The information of a virtual pixel is mapped to multiple physical sub-pixels, enabling the construction of an image with a higher pixel density on the same hardware basis, greatly improving the display clarity and the fineness of image edges, and achieving fine display control of "separating the static from the dynamic" in the entire image, thus enhancing the overall image perception quality. Summary of the Invention

[0007] The purpose of the present invention is to provide a dynamic display method for a sub-pixel LED display screen to solve the deficiencies in the background art.

[0008] To achieve the above purpose, the present invention provides the following technical solution: A dynamic display method for a sub-pixel LED display screen, the display method includes the following steps:

[0009] The acquisition end receives externally input image frame data and performs preprocessing operations on the image frame data;

[0010] The processing end uses an embedded sub-pixel mapping algorithm to reconstruct a virtual pixel grid in the image space, maps the color information of each virtual pixel to multiple adjacent physical sub-pixels, and adaptively adjusts the reconstruction method of the virtual pixels in the motion area after identifying the motion area in the image;

[0011] The control end generates drive data for controlling each physical sub-pixel according to the virtual pixel mapping result and the dynamic enhancement strategy, and transmits the drive data to the LED module to control the display of the LED display screen.

[0012] In a preferred embodiment, after identifying the motion area in the image, the reconstruction method of the virtual pixels in the motion area is adaptively adjusted, including the following steps:

[0013] In the current frame and the previous frame, calculate the difference in the brightness value or color value of the corresponding pixel points to obtain a pixel change amplitude matrix;

[0014] Mark the pixel points with a pixel change amplitude value greater than the motion threshold as motion pixels, and form a motion area mask:

[0015] Based on the motion area mask, dynamically adjust the virtual pixel reconstruction strategy within the motion area;

[0016] For the motion area, increase the priority of the refresh frequency, increase the refresh times of the sub-pixels in this area, and increase the sub-pixel brightness change rate.

[0017] In a preferred embodiment, obtain the pixel change amplitude matrix: D(x,y) = |F t (x,y) - F t-1 (x,y)|, where D(x,y) is the pixel change amplitude value, F t (x,y) is the current frame pixel brightness or color value, F t-1 is the previous frame pixel brightness or color value;

[0018] Form a motion area mask: where M(x,y) is the motion area mask, 1 represents the motion area, 0 represents the static area, and T m is the motion threshold.

[0019] In a preferred embodiment, the control end generates drive data for controlling each physical sub-pixel according to the virtual pixel mapping result and the dynamic enhancement strategy, and transmits the drive data to the LED module to control the display of the LED display screen, including the following steps:

[0020] The control end obtains the virtual pixel mapping result of the current frame, including the set of physical sub-pixels corresponding to each virtual pixel and the distance weight normalization value assigned to each sub-pixel;

[0021] Generate driving data for each physical sub-pixel, where the driving data includes sub-pixel coordinates, sub-pixel brightness values, light-emitting timings, and refresh frequencies;

[0022] Encapsulate all sub-pixel driving data in chronological order to form a control frame. The inter-frame synchronization, sub-pixel timing coordination, and refresh period of the control frame cover the display time. The control frame includes all sub-pixel brightness + timing + frequency and a synchronization signal header;

[0023] Transmit the driving data to the LED module in real time through the LED control interface. After receiving the driving data, the LED module sets the sub-pixel brightness according to the driving instructions, controls the on and off times of light emission, and cyclically updates the sub-pixel states at the specified refresh frequency.

[0024] In a preferred embodiment, mapping the color information of each virtual pixel to multiple adjacent physical sub-pixels includes the following steps:

[0025] For each sub-pixel in the set of physical sub-pixels corresponding to a pixel position within the virtual pixel grid, calculate its distance from the center position of the virtual pixel, determine the brightness weight based on the distance, normalize all weights, and obtain the normalized distance weight value;

[0026] Allocate the color brightness value of the virtual pixel to the corresponding color channels of the adjacent sub-pixels according to the normalized distance weight value. The expression is: In the formula, represents the brightness value of the sub-pixel, represents the brightness value of the virtual pixel, W norm (x s ,y s ) is the normalized distance weight value, and C ∈ {R, G, B}.

[0027] In a preferred embodiment, the processing end uses an embedded sub-pixel mapping algorithm to reconstruct the virtual pixel grid in the image space, including the following steps:

[0028] Obtain the relationship between the image pixel density and the sub-pixel distribution ratio of the display screen, and divide the virtual pixel grid in the image space according to the pixel mapping density ratio. The mapping formula is: In the formula, (x s ,y s ) is the position of the sub-pixel of the physical LED module, (x v ,y v ) is the position of a pixel point within the virtual pixel grid, and S x , S y are the ratio scaling coefficients of the virtual pixel to the sub-pixel;

[0029] For each virtual pixel, determine the set of adjacent physical sub-pixels it covers, and define the coverage range: C(xv , y v ) = {(x s , y s ) | |x s - x v S x | ≤ Δ x , |y s - y v S y | ≤ Δ y}}, where C(x v , y v ) is the set of physical sub - pixels corresponding to a pixel position (x v , y v ) in the virtual pixel grid, and Δ x , Δ y represent the tolerance of the coverage range.

[0030] In a preferred embodiment, the relationship between the image pixel density and the sub - pixel distribution ratio of the display screen is obtained, and the expression is: where R d is the pixel mapping density ratio, representing the spatial mapping ratio between the input image pixels and the actual sub - pixels, P r is the number of pixels per unit length of the input image, and P s is the number of sub - pixels per unit length of the LED module;

[0031] The calculation expression of the ratio scaling factor between the virtual pixel and the sub - pixel is: where W s , H s are the width and height of the sub - pixel matrix, and W v , H v are the width and height of the virtual pixel grid.

[0032] In a preferred embodiment, the acquisition end receives the externally input image frame data and performs pre - processing operations on the image frame data, including the following steps:

[0033] Sample the received original image frame data according to the resolution requirements of the LED display screen, perform color separation operations on the sampled image frame data, decompose the color information of each pixel point into three channels: red, green, and blue, and save the brightness values of each channel separately;

[0034] Perform gray - level distribution analysis on the brightness values of the separated red, green, and blue channels, count the gray - level value distribution of each channel within the image frame, and extract the image brightness characteristics, contrast range, and brightness gradient distribution;

[0035] Based on the results of grayscale distribution analysis, extract the pixel information to be displayed in each frame of the image, and determine the color information of each pixel point in the current frame. The color information includes the target brightness value and the target color value;

[0036] Further split the color information of each pixel point extracted into three channels: red sub-pixel, green sub-pixel, and blue sub-pixel, to form a sub-pixel brightness matrix.

[0037] In a preferred embodiment, perform grayscale distribution analysis on the brightness values of the separated red, green, and blue channels, count the grayscale value distribution of each channel within the image frame, and extract the image brightness characteristics, contrast range, and brightness gradient distribution, including the following steps:

[0038] Perform grayscale value statistics on the brightness value matrices of the separated red, green, and blue channels respectively, calculate the distribution quantity of the brightness values within the range of 0 to 255 in each channel, and generate a grayscale histogram corresponding to each channel;

[0039] According to the grayscale histogram, calculate the average brightness value, maximum brightness value, minimum brightness value, and brightness variance of each channel, and extract the overall brightness level, brightness contrast range, and brightness fluctuation characteristics;

[0040] Combine the brightness contrast range and brightness fluctuation characteristics to judge the contrast uniformity and dynamic performance ability of the image;

[0041] Perform spatial gradient calculation on each channel brightness matrix, analyze the brightness change rate between adjacent pixel points, generate an image brightness gradient map, and identify the high-gradient regions and low-gradient regions in the image.

[0042] In a preferred embodiment, perform spatial gradient calculation on each channel brightness matrix, analyze the brightness change rate between adjacent pixel points, generate an image brightness gradient map, and identify the high-gradient regions and low-gradient regions in the image, including the following steps:

[0043] For the brightness matrix of each channel C, calculate the horizontal gradient and vertical gradient, and the expressions are: where I C (x,y) is the brightness value at the pixel point (x,y), I C (x + 1,y) is the brightness value at the pixel point (x + 1,y), I C (x,y + 1) is the brightness value at the pixel point (x,y + 1), G C,x (x,y) is the horizontal direction brightness gradient value at the pixel point (x,y), G C,y (x,y) is the vertical direction brightness gradient value at the pixel point (x,y);

[0044] The calculation expression for the brightness change rate is as follows: In the formula, G C (x,y) is the brightness change rate at the pixel point (x,y). After calculating the brightness change rates of all pixel points, an image brightness gradient map is constructed based on the brightness change rates of all pixel points;

[0045] The brightness change rate of the pixel points is compared with a preset change threshold. If the brightness change rate is greater than or equal to the change threshold, it is a high-gradient pixel point; if the brightness change rate is less than the change threshold, it is a low-gradient pixel point. All high-gradient pixel points form a high-gradient region, and all low-gradient pixel points form a low-gradient region.

[0046] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0047] In the present invention, the acquisition end receives externally input image frame data, performs preprocessing operations on the image frame data. The processing end uses an embedded sub-pixel mapping algorithm to reconstruct a virtual pixel grid in the image space, maps the color information of each virtual pixel to multiple adjacent physical sub-pixels. After identifying the moving area in the image, the reconstruction method of the virtual pixels in the moving area is adaptively adjusted. The control end generates drive data for controlling each physical sub-pixel according to the virtual pixel mapping result and the dynamic enhancement strategy, and transmits the drive data to the LED module to control the display of the LED display screen. This method reconstructs the virtual pixel grid through the embedded sub-pixel mapping algorithm, realizes the independent control of brightness and color at the sub-pixel level, maps the information of one virtual pixel to multiple physical sub-pixels, realizes the construction of an image with a higher pixel density on the same hardware basis, greatly improves the clarity of the display and the fineness of the image edge, and realizes the fine display control of "separating the moving and the static" in the entire image, improving the overall image perception quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0049] Figure 1 It is the flowchart of the display method of the present invention.

[0050] Figure 2 It is the mind map of the display method of the present invention.

[0051] Figure 3 It is the timing diagram of the display method of the present invention.

[0052] Figure 4 It is the brain map of the display method of the present invention. Detailed implementation manners

[0053] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0054] Embodiment 1: Please refer to Figures 1-4 As shown, this embodiment proposes a dynamic display method for a sub-pixel LED display screen. The display method includes the following steps:

[0055] The acquisition end receives externally input image frame data and performs preprocessing operations on the image frame data. The preprocessing operations include sampling, color separation, gray-scale distribution analysis, and after extracting the pixel information of each frame of the image (extracting the basic pixel information to be displayed in each frame), performing sub-pixel channel splitting (splitting according to the three sub-pixel channels of red, green, and blue). The processing end reconstructs a virtual pixel grid in the image space using an embedded sub-pixel mapping algorithm based on the pixel density of the input image and the arrangement structure of each sub-pixel in the physical LED module, maps the color information of each virtual pixel to multiple adjacent physical sub-pixels through a dynamic distribution mapping model, and assigns brightness weights to them. A multi-frame pixel fusion technology is introduced between consecutive image frames for visual compensation. To enhance the virtual pixel display effect, a multi-frame pixel fusion technology is introduced in the method: between consecutive image frames, the display content of the physical sub-pixels is time-division multiplexed. According to the human eye's visual persistence characteristic, the system smoothly transitions and superimposes the brightness of the sub-pixels in several adjacent frames to generate a dynamic fusion image, and establishes an inter-frame synchronization mechanism to ensure the complete restoration of the virtual pixels in the time domain. After identifying the moving area in the image, the reconstruction method of the virtual pixels in the moving area is adaptively adjusted (based on the pixel change situation between the current image frame and the previous frame, the moving area in the image is identified in real time, and the reconstruction method of the virtual pixels in the moving area is adaptively adjusted using a dynamic visual perception model. For high-speed moving areas, the refresh frequency and sub-pixel weights are preferentially allocated to reduce problems such as image ghosting and edge blurring). The control end generates drive data for controlling each physical sub-pixel according to the virtual pixel mapping result and the dynamic enhancement strategy. The drive data includes the brightness value, emission timing, and refresh frequency of the sub-pixel. This drive data is transmitted to the LED module to control the display of the LED display screen.

[0056] This application receives externally input image frame data through a collection end, performs preprocessing operations on the image frame data. The processing end uses an embedded sub-pixel mapping algorithm to reconstruct a virtual pixel grid in the image space, maps the color information of each virtual pixel to multiple adjacent physical sub-pixels. After identifying the moving regions in the image, it adaptively adjusts the reconstruction method of the virtual pixels in the moving regions. The control end generates drive data for controlling each physical sub-pixel according to the virtual pixel mapping result and the dynamic enhancement strategy, and transmits the drive data to the LED module to control the display of the LED display screen. This method reconstructs the virtual pixel grid through the embedded sub-pixel mapping algorithm, realizes independent control of brightness and color at the sub-pixel level, maps the information of one virtual pixel into multiple physical sub-pixels, realizes the construction of an image with a higher pixel density on the same hardware basis, greatly improves the clarity of the display and the fineness of the image edge, and realizes "dynamic-static separation" fine display control in the entire image, improving the overall image perception quality.

[0057] Embodiment 2: The collection end receives externally input image frame data and performs preprocessing operations on the image frame data. The preprocessing operations include sampling, color separation, grayscale distribution analysis, and after extracting the pixel information of each frame of the image (extracting the basic pixel information to be displayed in each frame), performing sub-pixel channel splitting (splitting according to the three sub-pixel channels of red, green, and blue), including the following steps:

[0058] Sample the received original image frame data according to the resolution requirements of the LED display screen to ensure that the image size matches the number of physical pixels of the display screen, or scale and crop according to a set ratio to retain the effective display area. Perform a color separation operation on the sampled image frame data, decompose the color information of each pixel point into three independent channels of red, green, and blue, and save the brightness value of each channel separately as the basis for subsequent sub-pixel mapping.

[0059] Specifically, performing a color separation operation on the sampled image frame data, decomposing the color information of each pixel point into three independent channels of red, green, and blue, and saving the brightness value of each channel separately as the basis for subsequent sub-pixel mapping, includes the following steps:

[0060] Read the sampled image frame data row by row and column by column, and extract the original color information of each pixel point, usually represented in the form of RGB color values (three components of R, G, and B).

[0061] Extract the red components of all pixels and construct a red channel matrix in the order of pixel positions. Each element in the matrix corresponds to the red brightness value of the corresponding pixel in the original image. Extract the green components of all pixels and construct a green channel matrix in the order of pixel positions. Each element in the matrix corresponds to the green brightness value of the corresponding pixel in the original image. Extract the blue components of all pixels and construct a blue channel matrix in the order of pixel positions. Each element in the matrix corresponds to the blue brightness value of the corresponding pixel in the original image. The implementation code is as follows:

[0062] from PIL import Image

[0063] import numpy as np

[0064] # Read the image

[0065] image_path = 'input_image.jpg' # Replace with your image path

[0066] image = Image.open(image_path).convert('RGB')

[0067] # Convert the image to a NumPy array

[0068] image_array = np.array(image)

[0069] # Extract the red, green, and blue channel matrices

[0070] red_channel = image_array[:, :, 0] # Red channel matrix

[0071] green_channel = image_array[:, :, 1] # Green channel matrix

[0072] blue_channel = image_array[:, :, 2] # Blue channel matrix

[0073] # Output the matrix shape (number of rows, number of columns)

[0074] print("Red Channel Matrix Shape:", red_channel.shape)

[0075] print("Green Channel Matrix Shape:", green_channel.shape)

[0076] print("Blue ChannelMatrix Shape:",blue_channel.shape)

[0077] # If you need to save as a separate grayscale image:

[0078] Image.fromarray(red_channel).save('red_channel.png')

[0079] Image.fromarray(green_channel).save('green_channel.png')

[0080] Image.fromarray(blue_channel).save('blue_channel.png')

[0081] In the above code, image_array[:,:,0] extracts the red components of all pixels to form the red channel matrix. image_array[:,:,1] is the green channel matrix. image_array[:,:,2] is the blue channel matrix. PIL is used to open and save images, and NumPy efficiently processes pixel matrices.

[0082] Save the red, green, and blue channel matrices separately as the input basic data for subsequent sub-pixel mapping, brightness adjustment, and virtual pixel reconstruction, ensuring that the brightness values of each sub-pixel can independently participate in subsequent image processing.

[0083] Perform grayscale distribution analysis on the brightness values of the separated red, green, and blue channels, count the grayscale value distribution of each channel within the image frame, extract the image brightness characteristics, contrast range, and brightness gradient distribution, which is convenient for subsequent brightness weight assignment and virtual pixel mapping optimization. Based on the grayscale distribution analysis results, extract the basic pixel information to be displayed in each frame of the image, and determine the target brightness value and target color value of each pixel point in the current frame, providing complete pixel input data for sub-pixel reconstruction and virtual pixel mapping.

[0084] Specifically, performing grayscale distribution analysis on the brightness values of the separated red, green, and blue channels, counting the grayscale value distribution of each channel within the image frame, extracting the image brightness characteristics, contrast range, and brightness gradient distribution, and based on the grayscale distribution analysis results, extracting the basic pixel information to be displayed in each frame of the image, and determining the target brightness value and target color value of each pixel point in the current frame, includes the following steps:

[0085] For any color channel C (which can be R, G, or B), for the luminance value matrices of the separated red, green, and blue channels, perform grayscale value statistics respectively, calculate the distribution quantity of luminance values within the range of 0 to 255 in each channel, generate the grayscale histogram corresponding to each channel, and reflect the distribution characteristics of the current frame image at different luminance levels.

[0086] For the luminance value i ∈ [0, 255], the number of pixels with this luminance value, grayscale histogram H C (i) = the number of pixels whose pixel point luminance value is equal to i, where i is the luminance value and its value range is from 0 to 255.

[0087] According to the grayscale histogram H C (i), calculate the average luminance value, maximum luminance value, minimum luminance value, and luminance variance of each channel. The expression for the average luminance value is:

[0088] where μ C is the average luminance value of channel C, reflecting the overall luminance level, N is the total number of pixels in channel C, that is The expression for the maximum luminance value is: I C,max = max{i | H C (i) > 0}, where I C,max is the maximum luminance value of channel C. The expression for the minimum luminance value is: I C,min = min{i | H C (i) > 0}, where I C,min is the minimum luminance value of channel C. Obtain the luminance difference by subtracting the minimum luminance value from the maximum luminance value, reflecting the luminance contrast range. The expression for the luminance variance is: where is the luminance variance of channel C, reflecting the luminance fluctuation characteristics.

[0089] Extract the overall luminance level, luminance contrast range, and luminance fluctuation characteristics as the reference basis for subsequent image dynamic adjustment and sub-pixel mapping.

[0090] Combine the luminance contrast range and luminance fluctuation characteristics to judge the contrast uniformity and dynamic performance ability of the image.

[0091] Perform spatial gradient calculation on the luminance matrix of each channel, analyze the luminance change rate between adjacent pixel points, generate the image luminance gradient map, identify the high-gradient regions (such as edges, details, motion regions) and low-gradient regions (such as smooth, background parts) in the image, and provide a partitioning basis for the virtual pixel reconstruction method.

[0092] For the luminance matrix of each channel C, calculate the horizontal gradient and vertical gradient. The expressions are: In the formula, I C (x, y) is the luminance value at the pixel point (x, y), and I C (x + 1, y) is the luminance value at the pixel point (x + 1, y), and I C (x, y + 1) is the luminance value at the pixel point (x, y + 1), and G C,x (x, y) is the horizontal luminance gradient value at the pixel point (x, y), and G C,y (x, y) is the vertical luminance gradient value at the pixel point (x, y). Then the calculation expression for the luminance change rate is: In the formula, G C (x, y) is the luminance change rate at the pixel point (x, y). After calculating the luminance change rates of all pixel points, an image luminance gradient map is constructed based on the luminance change rates of all pixel points;

[0093] The luminance change rate of the pixel point is compared with a preset change threshold. If the luminance change rate is greater than or equal to the change threshold, it is a high-gradient pixel point. If the luminance change rate is less than the change threshold, it is a low-gradient pixel point. All high-gradient pixel points form a high-gradient region, and all low-gradient pixel points form a low-gradient region.

[0094] Based on the above grayscale distribution, luminance characteristics, contrast range, and luminance gradient analysis results, the basic pixel information that needs to be highlighted in each frame of the image is screened. This includes: determining the target luminance value of each pixel point (optimized according to the original luminance and grayscale distribution), determining the target color value of each pixel point (adjusted according to the original RGB components and contrast results), marking the pixel points in the motion region or high-gradient region, and preparing for the subsequent adaptive sub-pixel reconstruction strategy.

[0095] The color information of each extracted basic pixel point is further split into three independent channels: red sub-pixel, green sub-pixel, and blue sub-pixel, forming a separate sub-pixel luminance matrix, and storing the target luminance values of each sub-pixel respectively, preparing for subsequent sub-pixel mapping and drive control.

[0096] Specifically, splitting the color information of each extracted basic pixel point into three independent channels: red sub-pixel, green sub-pixel, and blue sub-pixel, forming a separate sub-pixel luminance matrix, and storing the target luminance values of each sub-pixel respectively, includes the following steps:

[0097] Traverse all the basic pixel points in the image frame row by row and column by column, extract the target color information corresponding to each pixel point, including the target red luminance value, the target green luminance value, and the target blue luminance value. Extract the target red luminance value in each pixel point and fill it into the red sub-pixel luminance matrix in the order of the pixel positions in the image. Each element in the matrix corresponds to the target red luminance value of a sub-pixel in the current frame. Extract the target green luminance value in each pixel point and fill it into the green sub-pixel luminance matrix in the order of the pixel positions in the image. Each element in the matrix corresponds to the target green luminance value of a sub-pixel in the current frame. Extract the target blue luminance value in each pixel point and fill it into the blue sub-pixel luminance matrix in the order of the pixel positions in the image. Each element in the matrix corresponds to the target blue luminance value of a sub-pixel in the current frame.

[0098] Assume: The input is an image frame data in the form of (height, width, 3), with 3 channels (R, G, B), and the R, G, B luminance values of each pixel point are already the target luminance values, ranging from 0 to 255. The specific code example is as follows:

[0099] import numpy as np

[0100] # Assume there is an input image frame (height, width, 3), and the R, G, B luminance values of each pixel range from 0 to 255

[0101] height, width = 1080, 1920

[0102] image_frame = np.random.randint(0, 256, size=(height, width, 3), dtype=np.uint8)

[0103] # Step 1: Extract the red sub-pixel luminance matrix

[0104] R_matrix = image_frame[:, :, 0] # Take the 0th channel (R)

[0105] # R_matrix.shape == (height, width)

[0106] # Step 2: Extract the green sub-pixel luminance matrix

[0107] G_matrix = image_frame[:, :, 1] # Take the 1st channel (G)

[0108] # G_matrix.shape == (height, width)

[0109] # Step 3: Extract the blue sub-pixel brightness matrix

[0110] B_matrix = image_frame[:, :, 2] # Take the 2nd channel (B)

[0111] # B_matrix.shape == (height, width)

[0112] # Verify the output

[0113] print("Shape of the red sub-pixel brightness matrix:", R_matrix.shape)

[0114] print("Shape of the green sub-pixel brightness matrix:", G_matrix.shape)

[0115] print("Shape of the blue sub-pixel brightness matrix:", B_matrix.shape)

[0116] In the above code, image_frame[:, :, 0] represents selecting the 0th channel (R) of all pixels, image_frame[:, :, 1] represents selecting the 1st channel (G) of all pixels, and image_frame[:, :, 2] represents selecting the 2nd channel (B) of all pixels. After extraction, each matrix is (height, width), corresponding to the target brightness value at each pixel position in the image.

[0117] Save the generated red sub-pixel brightness matrix, green sub-pixel brightness matrix, and blue sub-pixel brightness matrix separately to ensure that each sub-pixel can participate in dynamic display control independently and accurately.

[0118] Based on the pixel density of the input image and the arrangement structure of each sub-pixel in the physical LED module, the processing end uses the embedded sub-pixel mapping algorithm to reconstruct the virtual pixel grid in the image space, including the following steps:

[0119] Obtain the relationship between the image pixel density and the sub-pixel distribution ratio of the display screen. The expression is: In the formula, R d is the pixel mapping density ratio, representing the spatial mapping ratio between the input image pixels and the actual sub-pixels, and P r is the number of pixels per unit length in the input image (pixel density), and P s is the number of sub-pixels per unit length in the LED module.

[0120] Divide the virtual pixel grid in the image space according to the pixel mapping density ratio. The mapping formula is: In the formula, (x s, y s ), which is the physical LED module sub - pixel position, (x v , y v ) is the position of a certain pixel point within the virtual pixel grid, and S x 、S y is the scaling factor between the virtual pixel and the sub - pixel, and In the formula, W s 、H s are the width and height of the sub - pixel matrix, and W v 、H v are the width and height of the virtual pixel grid.

[0121] For each virtual pixel, determine the set of adjacent physical sub - pixels it covers, and define the coverage range: C(x v , y v ) = {(x s , y s ) | |x s - x v S x | ≤ Δ x , |y s - y v S y | ≤ Δ y}}, where C(x v , y v ) is the set of physical sub - pixels corresponding to the position (x v , y v ) within the virtual pixel grid. Δ x 、Δ y represent the tolerance of the coverage range, which depends on the sub - pixel arrangement structure, such as RGB stripe, RGB rectangle, or hexagonal arrangement, and describe which physical sub - pixel points around a certain virtual pixel point belong to its coverage range. Among them, the distance between the physical sub - pixel (x s , y s ) and the actual position (x v S x , y v S y ) after mapping with the virtual pixel must be within a certain tolerance range Δ x 、Δ y to be considered that this sub - pixel belongs to the "coverage" range of this virtual pixel.

[0122] According to the input image pixel density and the LED module sub - pixel arrangement structure, determine the mapping relationship between the virtual pixel and the physical sub - pixel, and establish a virtual pixel grid to achieve high - precision dynamic display control.

[0123] Map the color information of each virtual pixel to multiple adjacent physical sub-pixels through a dynamic distribution mapping model, and perform brightness weight allocation on them, including the following steps:

[0124] For each sub-pixel in the set C(x v ,y v ) corresponding to a pixel position in the virtual pixel grid, calculate its distance from the center position of the virtual pixel, and determine the brightness weight based on the distance. The expression is: In the formula, w(x s ,y s ) is the distance weight, d(x s ,y s ) is the Euclidean distance from the physical sub-pixel point to the center of the virtual pixel. The calculation expression is: In the formula, (x s ,y s ) is the position of the physical sub-pixel point, (x v S x ,y v S y ) is the center position of the virtual pixel.

[0125] Normalize all weights so that their sum is 1. The calculation expression is:

[0126] In the formula, W norm (x s ,y s ) is the normalized value of the distance weight, w(x s ,y s ) is the distance weight. Allocate the color brightness value of the virtual pixel to the corresponding color channels of the adjacent sub-pixels according to the normalized value of the distance weight. The expression is: In the formula, represents the brightness value of the sub-pixel, represents the brightness value of the virtual pixel, W norm (x s ,y s ) is the normalized value of the distance weight, C ∈ {R, G, B}.

[0127] Introduce multi-frame pixel fusion technology between consecutive image frames for visual compensation. To enhance the virtual pixel display effect, multi-frame pixel fusion technology is introduced in the method: between consecutive image frames, time multiplexing is performed on the display content of the physical sub-pixels. According to the human eye visual persistence characteristic, the system performs smooth transition and superposition on the sub-pixel brightness in several adjacent frames to generate a dynamic fusion image, and establishes an inter-frame synchronization mechanism to ensure the complete restoration of the virtual pixel in the time domain, including the following steps:

[0128] In N consecutive frames of images, the brightness values of each physical sub-pixel in each frame are extracted respectively, and a time weight is assigned to each frame to control the fusion ratio of brightness among multiple frames: w a , a = 0, 1, …, N - 1, where w a is the time weight of the a-th frame image, and According to the time weight, the brightness values of the same physical sub-pixel in consecutive frames are weighted and superimposed to generate a fused dynamic brightness value: In the formula, is the fused dynamic brightness value, is the brightness value of the color channel C ∈ {R, G, B} of the sub-pixel s at the moment of the (t + a)-th frame.

[0129] Apply the fused dynamic brightness value to the current display frame to improve the visual restoration effect of virtual pixels in time, and utilize the visual persistence of the human eye to enhance the display smoothness and dynamic performance.

[0130] After identifying the moving region in the image, adaptively adjust the virtual pixel reconstruction method for the moving region (based on the pixel change situation between the current image frame and the previous frame, identify the moving region in the image in real time, and adaptively adjust the virtual pixel reconstruction method for the moving region using a dynamic visual perception model. For high-speed moving regions, preferentially allocate the refresh frequency and sub-pixel weights to reduce problems such as image ghosting and edge blurring), including the following steps:

[0131] In the current frame and the previous frame, calculate the difference in the brightness value or color value of the corresponding pixel points to obtain the pixel change amplitude matrix: D(x, y) = |F t (x, y) - F t-1 (x, y)|, where D(x, y) is the pixel change amplitude value, and F t (x, y) is the pixel brightness or color value of the current frame, and F t-1 is the pixel brightness or color value of the previous frame.

[0132] Mark the pixel points with pixel change amplitude values greater than the motion threshold (where the motion threshold includes the color change threshold and the brightness change threshold. When the pixel change amplitude matrix is a color change, the motion threshold is the color change threshold; when the pixel change amplitude matrix is a brightness change, the motion threshold is the brightness change threshold) as moving pixels, and form a motion region mask:

[0133] In the formula, M(x, y) is the motion region mask, 1 represents the motion region, 0 represents the static region, and T mLet \(T\) be the motion threshold, and \(D(x,y)\) be the pixel change amplitude value. Based on the motion area mask, the virtual pixel reconstruction strategy within the motion area is dynamically adjusted as follows: For the motion area, the refresh frequency priority is increased, the refresh times of the sub-pixels in this area are increased, the brightness change rate of the sub-pixels is increased, and the visual afterimage is reduced. For the static area, the reference refresh frequency is maintained, the sub-pixel brightness is stabilized, and unnecessary flashes are avoided to balance brightness and contrast and improve the static image quality. According to the position and intensity of the motion area, the refresh priority of the physical sub-pixels within this area is increased, and the refresh frequency is dynamically allocated to ensure the display fluency within the motion area first.

[0134] The control end generates drive data for controlling each physical sub-pixel according to the virtual pixel mapping result and the dynamic enhancement strategy. The drive data includes the brightness value, emission timing, and refresh frequency of the sub-pixel. Transmitting the drive data to the LED module to control the display of the LED display screen includes the following steps:

[0135] The control end obtains the virtual pixel mapping result of the current frame, including the set of physical sub-pixels corresponding to each virtual pixel and the normalized distance weight value assigned to each sub-pixel;

[0136] Generate drive data for each physical sub-pixel. The drive data includes: sub-pixel coordinates, sub-pixel brightness value, emission timing (Start-time / End-time / Duty-Cycle), and refresh frequency. The code example is as follows:

[0137]

[0138] Package all the sub-pixel drive data in chronological order to form the corresponding control frame, ensuring: inter-frame synchronization, sub-pixel timing coordination, and the refresh cycle completely covering the display time. The control frame is shown as follows: [Frame1]→[Frame2]→[Frame3]→... Each frame includes: the brightness + timing + frequency of all sub-pixels, and a synchronization signal header. Transmit the drive data to the LED module in real time through a dedicated LED control interface (such as SPI / LVDS / HUB75 / Ethernet): ensure sufficient bandwidth, controllable delay, and ensure data integrity and timing synchronization. The LED module receives the drive data and, according to the drive instructions: sets the sub-pixel brightness, controls the turn-on and turn-off times of the emission, and cyclically updates the sub-pixel state at the specified refresh frequency. Through the human eye's visual persistence effect, a smooth, continuous, and dynamically enhanced virtual pixel display effect is achieved.

[0139] It should be understood that the term "and / or" in this text is merely a description of the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, the character " / " in this text generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship. The specific meaning can be understood by referring to the context before and after.

[0140] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0141] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this text can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians 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. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated here.

[0142] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by 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. A sub-pixel LED display screen dynamic display method, characterized in that: The display method comprises the following steps: The acquisition end receives the image frame data input from the outside and performs preprocessing operations on the image frame data; The processing end uses an embedded sub-pixel mapping algorithm to reconstruct a virtual pixel grid in the image space, maps the color information of each virtual pixel to multiple adjacent physical sub-pixels, and after identifying the motion area in the image, adaptively adjusts the virtual pixel reconstruction method of the motion area; The control end generates driving data for controlling each physical sub-pixel according to the virtual pixel mapping result and the dynamic enhancement strategy, and transmits the driving data to the LED module to control the display of the LED display screen.

2. A sub-pixel LED display screen dynamic display method according to claim 1, characterized in that: After identifying the motion area in the image, the virtual pixel reconstruction method of the motion area is adaptively adjusted, including the following steps: In the current frame and the previous frame, the difference in brightness or color value of the corresponding pixel is calculated to obtain the pixel change amplitude matrix; Pixels whose pixel change amplitude is greater than the motion threshold are marked as motion pixels, and a motion area mask is formed: Based on the motion region mask, the virtual pixel reconstruction strategy in the motion region is dynamically adjusted; For the motion area, the refresh frequency priority is increased, the refresh times of the sub-pixels in the area are increased, and the sub-pixel brightness change rate is increased.

3. A sub-pixel LED display screen dynamic display method according to claim 2, characterized in that: Get the pixel change amplitude matrix: D(x,y)=|F t (x,y)-F t-1 (x,y)|, where D(x,y) is the pixel change amplitude value, F t (x, y) is the brightness or color value of the pixel in the current frame, F t-1 It is the brightness or color value of the pixel in the previous frame; Form the motion region mask: Where M(x,y) is the motion region mask, 1 represents the motion region, 0 represents the static region, T m is the motion threshold.

4. A sub-pixel LED display screen dynamic display method according to claim 3, characterized in that: The control end generates driving data for controlling each physical sub-pixel according to the virtual pixel mapping result and the dynamic enhancement strategy, and transmits the driving data to the LED module to control the LED display screen, including the following steps: The control end obtains the virtual pixel mapping result of the current frame, including the set of physical sub-pixels corresponding to each virtual pixel and the normalized distance weight value assigned to each sub-pixel; Generate driving data for each physical sub-pixel, the driving data including sub-pixel coordinates, sub-pixel brightness value, light emission timing and refresh frequency; All sub-pixel driving data are packaged in time sequence to form a control frame. The inter-frame synchronization, sub-pixel timing coordination, and refresh cycle of the control frame cover the display time. The control frame contains all sub-pixel brightness + timing + frequency and synchronization signal header; The driving data is transmitted to the LED module in real time through the LED control interface. After receiving the driving data, the LED module sets the sub-pixel brightness according to the driving instructions, controls the light on and off time, and cyclically updates the sub-pixel status at the specified refresh frequency.

5. A sub-pixel LED display screen dynamic display method according to claim 4, characterized in that: Mapping the color information of each virtual pixel to a plurality of adjacent physical sub-pixels includes the following steps: For each sub-pixel in the physical sub-pixel set corresponding to a pixel point position in the virtual pixel grid, calculate the distance between the sub-pixel and the center position of the virtual pixel, determine the brightness weight according to the distance, normalize all the weights, and obtain the distance weight normalization value; The color brightness value of the virtual pixel is assigned to the corresponding color channel of the adjacent sub-pixel according to the distance weight normalized value. The expression is: In the formula, represents the brightness value of the sub-pixel, Represents the brightness value of the virtual pixel, W norm (x s ,y s ) is the distance weight normalized value, C∈{R,G,B}.

6. A sub-pixel LED display screen dynamic display method according to claim 1, characterized in that: The processing side uses an embedded sub-pixel mapping algorithm to reconstruct a virtual pixel grid in the image space, which includes the following steps: Obtain the relationship between the image pixel density and the sub-pixel distribution ratio of the display screen, and divide the virtual pixel grid in the image space according to the pixel mapping density ratio. The mapping formula is: In the formula, (x s ,y s ) is the physical LED module sub-pixel position, (x v ,y v ) is the position of a pixel point in the virtual pixel grid, S x , S y is the ratio scaling factor of virtual pixel to sub-pixel; For each virtual pixel, determine the set of adjacent physical sub-pixels to be covered and define the coverage range: C(x v ,y v )={(x s ,y s )||x s -x v S x |≤Δ x ,|y s -y v S y |≤Δ y }, where C(x v ,y v ) is the position of a pixel point in the virtual pixel grid (x v ,y v ) corresponds to the set of physical sub-pixels, Δ x , Δ y Indicates the tolerance for coverage.

7. A sub-pixel LED display screen dynamic display method according to claim 6, characterized in that: Get the relationship between the image pixel density and the sub-pixel distribution ratio of the display screen. The expression is: In the formula, R d is the pixel mapping density ratio, which indicates the spatial mapping ratio between the input image pixels and the actual sub-pixels, P r is the number of pixels per unit length of the input image, P s is the number of sub-pixels per unit length of the LED module; The calculation expression of the scaling factor of virtual pixel to sub-pixel is: Where W s , H s is the width and height of the sub-pixel matrix, W v , H v is the width and height of the virtual pixel grid.

8. The sub-pixel LED display screen dynamic display method according to claim 5, characterized in that: The acquisition end receives the image frame data input from the outside and performs preprocessing operations on the image frame data, including the following steps: The received original image frame data is sampled according to the resolution requirements of the LED display screen, and the sampled image frame data is subjected to color separation operation, the color information of each pixel is decomposed into three channels of red, green and blue, and the brightness value of each channel is saved respectively; Perform grayscale distribution analysis on the brightness values ​​of the separated red, green and blue channels, calculate the grayscale value distribution of each channel in the image frame, and extract the image brightness characteristics, contrast range and brightness gradient distribution; Based on the grayscale distribution analysis results, the pixel information to be displayed in each frame of the image is extracted, and the color information of each pixel in the current frame is determined. The color information includes the target brightness value and the target color value. The extracted color information of each pixel is further split into three channels: red sub-pixel, green sub-pixel and blue sub-pixel to form a sub-pixel brightness matrix.

9. A sub-pixel LED display screen dynamic display method according to claim 8, characterized in that: The grayscale distribution analysis is performed on the brightness values ​​of the separated red, green and blue channels, the grayscale value distribution of each channel in the image frame is counted, and the image brightness characteristics, contrast range and brightness gradient distribution are extracted, including the following steps: The grayscale values ​​of the brightness value matrices of the separated red, green and blue channels are counted respectively, and the distribution number of brightness values ​​in each channel within the range of 0 to 255 is calculated to generate the grayscale histogram corresponding to each channel; According to the grayscale histogram, the average brightness value, maximum brightness value, minimum brightness value and brightness variance of each channel are calculated to extract the overall brightness level, brightness contrast range and brightness fluctuation characteristics; Combine the brightness contrast range and brightness fluctuation characteristics to judge the contrast uniformity and dynamic performance of the image; The spatial gradient of the brightness matrix of each channel is calculated, the brightness change rate between adjacent pixels is analyzed, the image brightness gradient map is generated, and the high gradient area and low gradient area in the image are identified.

10. A sub-pixel LED display screen dynamic display method according to claim 9, characterized in that: The spatial gradient calculation is performed on the brightness matrix of each channel, the brightness change rate between adjacent pixels is analyzed, the image brightness gradient map is generated, and the high gradient area and low gradient area in the image are identified, including the following steps: For the brightness matrix of each channel C, the horizontal gradient and vertical gradient are calculated, and the expression is: In the formula, I C (x, y) is the brightness value at the pixel point (x, y), I C (x+1,y) is the brightness value at the pixel point (x+1,y), I C (x, y+1) is the brightness value at the pixel point (x, y+1), G C,x (x, y) is the horizontal brightness gradient value at the pixel point (x, y), G C,y (x, y) is the vertical brightness gradient value at the pixel point (x, y); The calculation expression of brightness change rate is: In the formula, G C (x, y) is the brightness change rate at the pixel point (x, y). After calculating the brightness change rates of all pixels, the image brightness gradient map is constructed based on the brightness change rates of all pixels. Compare the pixel brightness change rate with the preset change threshold. If the brightness change rate is greater than or equal to the change threshold, it is a high-gradient pixel. If the brightness change rate is less than the change threshold, it is a low-gradient pixel. All high-gradient pixels are grouped into a high-gradient region, and all low-gradient pixels are grouped into a low-gradient region.

Images