Display screen driving method
Through edge detection and multi-layer convolutional neural network processing images, combined with color space conversion and gamma correction, the color restoration deviation and image quality loss problems of high refresh rate display screens are solved, and an efficient and reliable display driving method is realized.
Patent Information
- Application Number
- CN202510950373.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-10
- Publication Date
- 2025-08-08
AI Technical Summary
When the prior art processes 8K ultra-high definition, 240Hz high refresh rate, HDR10 and wide color gamut content, the amount of image data explodes, and the hardware capability bottleneck leads to large color restoration deviations, image quality loss, increased delay and reduced reliability, which cannot meet the needs of accurate and stable display.
Edge detection algorithm and multi-layer convolutional neural network are used to extract image features, perform noise interference processing and color space conversion, combine gamma correction and color gamut compression, dynamically adjust driving signals, set decompression and error verification algorithms to realize dynamic compression and temperature compensation of subframes.
By retaining image details, ensuring color accuracy, improving driving efficiency and reliability, improving adaptability, and achieving high-precision display.
Smart Images
Figure CN120452349A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of drive control, and in particular to a display screen driving method. Background Art
[0002] With the widespread adoption of 8K ultra-high definition, 240Hz high refresh rate, HDR10, and wide color gamut content, the amount of image data that displays must process is growing exponentially, posing stringent challenges to the entire chain from image preprocessing to color optimization to transmission compression to display driver development. Data Dimension Explosion: 8K images have four times the number of pixels as 4K. The dynamic range of brightness (0.01 nits to 10,000 nits) and color depth (12-bit) of HDR content far exceed traditional SDR (8-bit, 0 to 200 nits). Hardware capability bottleneck: The display's color gamut (such as OLED's DCI-P3), brightness (LCD's 600 nits), and drive bandwidth (traditional LVDS interface bandwidth is insufficient) are still limited by the luminescent materials and circuit design and cannot directly match content requirements.
[0003] Specifically, there are the following defects: simple noise reduction (such as Gaussian blur) will blur the real edges, resulting in loss of details in subsequent color processing; directly processing images in the RGB space will lead to large deviations in color restoration due to the different RGB primary color coordinates and backlight spectra of different display screens; existing solutions uniformly compress different subframes (such as using H.264 encoding for the full frame), but the data complexity of subframes varies greatly (such as high complexity of subframes in motion scenes and low complexity in static scenes): high-complexity subframes lose details after compression, and low-complexity subframes waste bandwidth, resulting in image quality loss (loss of details, color distortion), increased latency (accumulated waiting time in each link), and reduced reliability (errors cannot be repaired in a closed loop), which cannot meet the core requirements of accurate and stable display.
[0004] Therefore, the present invention provides a display screen driving method. Summary of the Invention
[0005] The present invention provides a display screen driving method to solve the above-mentioned technical problems.
[0006] The present invention provides a display screen driving method, comprising: Step 1: Use edge detection algorithm to identify edge areas and non-edge areas in the input image, extract image features through multi-layer convolutional neural network and perform noise interference processing; Step 2: The noise-processed image is subjected to color space conversion and contrast enhancement. The color components of the enhanced image are nonlinearly transformed according to the color gamut of the display. The luminance component is adjusted by establishing a gamma correction lookup table to compensate for the nonlinear electro-optical conversion characteristics of the display. The chrominance component is compressed and expanded to obtain a new image. Step 3: The new image is transmitted according to the preset scanning order, and the data complexity of the subframes in each scanning order is determined and compressed accordingly, and then transmitted to the display driver circuit; Step 4: Set a decompression algorithm and an error checking algorithm in the driving circuit, decompress and error-check the transmitted compressed sub-frame, and drive the pixel units of the display screen to display.
[0007] Preferably, performing contrast enhancement processing on the image after noise interference processing includes: The global average brightness and local brightness distribution of the image after noise interference processing are calculated. The baseline value for contrast enhancement is determined based on the global average brightness. For dark areas where the brightness of the local area is lower than the baseline value, a local contrast enhancement algorithm based on histogram equalization is used to expand the brightness histogram of the dark area to the entire dynamic range. For bright areas where the brightness of the local area is higher than the reference value, a brightness compression algorithm based on logarithmic transformation is used. At the same time, a visual saliency detection model is introduced to identify the region of interest in the image and perform contrast enhancement on the region of interest. An enhanced processed image is obtained according to the enhanced processing result.
[0008] Preferably, before transmitting the new image according to the preset scanning order, the process includes: Dividing the display time of the new image into a plurality of subframes, each subframe corresponding to a different driving signal combination; For each pixel unit, the duty cycle of the driving signal in each subframe is calculated based on the corresponding color value and brightness value, and the corresponding driving pulse signal is generated through pulse width modulation technology. When generating the driving signal, the driving signal of adjacent subframes is smoothly transitioned based on the response time and afterglow effect of the display screen. At the same time, the driving signal is subjected to spectrum diffusion processing, using random subframe sequence and driving pulse phase to disperse the energy of the driving signal into a wider frequency range; A preset scanning order is obtained according to the smooth transition processing result and the dispersion result.
[0009] Preferably, transmitting to a display screen driving circuit includes: dividing each compressed subframe into multiple data blocks according to rows and columns, and transmitting the data blocks in parallel to different driving areas of the display screen through multiple independent data transmission channels, wherein each data transmission channel is equipped with an independent clock signal and synchronization control module, dynamically adjusting the clock frequency and transmission rate according to the position and data volume of each driving area, and synchronously delivering the data of each channel to the driving circuit; During the transmission process, a pre-fetch buffering technique is used to read the data block of the next compressed subframe in advance and store it in a buffer memory; At the same time, a data retransmission mechanism is set up. When a data transmission error is detected in the channel, the data block of the corresponding channel is automatically retransmitted.
[0010] Preferably, before driving the pixel unit of the display screen to display, the method further includes: The built-in sensor array is used to detect the threshold voltage and luminous efficiency of each pixel unit in the display in real time; For pixel units with drifting threshold voltage, the amplitude and bias voltage of the driving signal are dynamically adjusted; For pixel units with attenuated luminous efficiency, the pulse width or amplitude of the driving signal is increased to compensate for the brightness attenuation. During the compensation process, a fuzzy control algorithm is used to automatically adjust the compensation parameters based on the degree of parameter change of the pixel unit and the overall display status of the display. Preferably, after driving the pixel unit of the display screen to display, the method further includes: integrating a temperature sensor in the driving circuit to monitor the temperature of the driving chip and the circuit board in real time, and starting a heat dissipation mechanism when the temperature exceeds a preset threshold; A temperature-performance model is established to predict the performance changes of the driving circuit based on real-time temperature data, determine the advance compensation factor, and dynamically adjust the parameters of the driving signal based on the advance compensation factor while starting the heat dissipation mechanism to optimize the display of the pixel unit of the driving display.
[0011] Preferably, performing nonlinear transformation on the color components of the enhanced image according to the color gamut range of the display screen includes: The image after dynamic contrast enhancement is converted from RGB color space to CIELAB color space, and the CIELAB three-dimensional color gamut boundary model of the display color gamut is established, where the color gamut boundary is composed of N color gamut vertex coordinates. The convex polygonal area is represented by: For each pixel Define the color gamut matching function :
[0012] in, is the orthogonal projection of the chromaticity coordinates on the convex hull boundary of the color gamut, by solving the minimum Euclidean distance Determine the projection point; is the color gamut mapping function of the brightness component, is the global brightness extreme value of the image; is the brightness boundary value; is the brightness of the corresponding pixel.
[0013] Preferably, performing color gamut compression and expansion processing on the chrominance component includes: The current color gamut coordinates of each pixel unit are obtained through the built-in spectral sensor, a time-varying color gamut model is established, and a color gamut vertex set is obtained; The dimensionality of the color gamut vertex set is reduced to obtain the principal component directions e1 and e2, and an expansion transformation matrix is constructed to convert the chromaticity coordinates to be expanded into spatial coordinates, and the expansion boundary constraints are defined.
[0014] Preferably, predicting the performance change of the driving circuit according to the real-time temperature data and determining the advance compensation factor includes: Deploy multi-dimensional sensors at key nodes of the driving circuit to collect multi-dimensional data sets; The multidimensional dataset is input into the temperature-performance mapping model of the driving circuit to analyze the influence of temperature changes on the driving core parameters and establish a corresponding relationship between temperature and driving signal performance indicators. In combination with the predicted performance change direction and amplitude, an advance compensation factor is obtained.
[0015] Preferably, determining the data complexity of the subframes in each scanning order and performing corresponding compression processing includes: Determine the spatial complexity and time complexity of the corresponding subframe to obtain the data complexity; Comparing the data complexity with the complexity comparison table to obtain a compression level, wherein the compression level includes: high complexity compression, medium complexity compression, and low complexity compression; The corresponding subframe is compressed according to a compression method that matches the compression level.
[0016] Compared with the prior art, the present invention has the following advantages: Edge detection and CNN noise reduction preserve details, gamma correction and color gamut processing ensure color accuracy, contrast enhancement improves dark / bright details, sub-frame dynamic compression reduces compression loss of highly complex content, and temperature compensation and error checking mechanisms reduce the failure rate of the driving circuit, achieving full-link optimization from image preprocessing to driving control. While ensuring high-precision display, it improves driving efficiency, reliability, and adaptability.
[0017] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.
[0018] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a flow chart of a display screen driving method according to an embodiment of the present invention. DETAILED DESCRIPTION
[0020] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.
[0021] The present invention provides a display screen driving method, such as Figure 1 Shown, including: Step 1: Use edge detection algorithm to identify edge areas and non-edge areas in the input image, extract image features through multi-layer convolutional neural network and perform noise interference processing; Step 2: The noise-processed image is subjected to color space conversion and contrast enhancement. The color components of the enhanced image are nonlinearly transformed according to the color gamut of the display. The luminance component is adjusted by establishing a gamma correction lookup table to compensate for the nonlinear electro-optical conversion characteristics of the display. The chrominance component is compressed and expanded to obtain a new image. Step 3: The new image is transmitted according to the preset scanning order, and the data complexity of the subframes in each scanning order is determined and compressed accordingly, and then transmitted to the display driver circuit; Step 4: Set a decompression algorithm and an error checking algorithm in the driving circuit, decompress and error-check the transmitted compressed sub-frame, and drive the pixel units of the display screen to display.
[0022] Preferably, performing contrast enhancement processing on the image after noise interference processing includes: The global average brightness and local brightness distribution of the image after noise interference processing are calculated. The baseline value for contrast enhancement is determined based on the global average brightness. For dark areas where the brightness of the local area is lower than the baseline value, a local contrast enhancement algorithm based on histogram equalization is used to expand the brightness histogram of the dark area to the entire dynamic range. For bright areas where the brightness of the local area is higher than the reference value, a brightness compression algorithm based on logarithmic transformation is used. At the same time, a visual saliency detection model is introduced to identify the region of interest in the image and perform contrast enhancement on the region of interest. An enhanced processed image is obtained according to the enhanced processing result.
[0023] Preferably, before transmitting the new image according to the preset scanning order, the process includes: Dividing the display time of the new image into a plurality of subframes, each subframe corresponding to a different driving signal combination; For each pixel unit, the duty cycle of the driving signal in each subframe is calculated based on the corresponding color value and brightness value, and the corresponding driving pulse signal is generated through pulse width modulation technology. When generating the driving signal, the driving signal of adjacent subframes is smoothly transitioned based on the response time and afterglow effect of the display screen. At the same time, the driving signal is subjected to spectrum diffusion processing, using random subframe sequence and driving pulse phase to disperse the energy of the driving signal into a wider frequency range; A preset scanning order is obtained according to the smooth transition processing result and the dispersion result.
[0024] Preferably, transmitting to a display screen driving circuit includes: dividing each compressed subframe into multiple data blocks according to rows and columns, and transmitting the data blocks in parallel to different driving areas of the display screen through multiple independent data transmission channels, wherein each data transmission channel is equipped with an independent clock signal and synchronization control module, dynamically adjusting the clock frequency and transmission rate according to the position and data volume of each driving area, and synchronously delivering the data of each channel to the driving circuit; During the transmission process, a pre-fetch buffering technique is used to read the data block of the next compressed subframe in advance and store it in a buffer memory; At the same time, a data retransmission mechanism is set up. When a data transmission error is detected in the channel, the data block of the corresponding channel is automatically retransmitted.
[0025] Preferably, before driving the pixel unit of the display screen to display, the method further includes: The built-in sensor array is used to detect the threshold voltage and luminous efficiency of each pixel unit in the display in real time; For pixel units with drifting threshold voltage, the amplitude and bias voltage of the driving signal are dynamically adjusted; For pixel units with attenuated luminous efficiency, the pulse width or amplitude of the driving signal is increased to compensate for the brightness attenuation. During the compensation process, a fuzzy control algorithm is used to automatically adjust the compensation parameters based on the degree of parameter change of the pixel unit and the overall display status of the display. Preferably, after driving the pixel unit of the display screen to display, the method further includes: integrating a temperature sensor in the driving circuit to monitor the temperature of the driving chip and the circuit board in real time, and starting a heat dissipation mechanism when the temperature exceeds a preset threshold; A temperature-performance model is established to predict the performance changes of the driving circuit based on real-time temperature data, determine the advance compensation factor, and dynamically adjust the parameters of the driving signal based on the advance compensation factor while starting the heat dissipation mechanism to optimize the display of the pixel unit of the driving display.
[0026] Preferably, performing color gamut compression and expansion processing on the chrominance component includes: The current color gamut coordinates of each pixel unit are obtained through the built-in spectral sensor, a time-varying color gamut model is established, and a color gamut vertex set is obtained; The dimensionality of the color gamut vertex set is reduced to obtain the principal component directions e1 and e2, and an expansion transformation matrix is constructed to convert the chromaticity coordinates to be expanded into spatial coordinates, and the expansion boundary constraints are defined.
[0027] Preferably, predicting the performance change of the driving circuit according to the real-time temperature data and determining the advance compensation factor includes: Deploy multi-dimensional sensors at key nodes of the driving circuit to collect multi-dimensional data sets; The multidimensional dataset is input into the temperature-performance mapping model of the driving circuit to analyze the influence of temperature changes on the driving core parameters and establish a corresponding relationship between temperature and driving signal performance indicators. In combination with the predicted performance change direction and amplitude, an advance compensation factor is obtained.
[0028] Preferably, determining the data complexity of the subframes in each scanning order and performing corresponding compression processing includes: Determine the spatial complexity and time complexity of the corresponding subframe to obtain the data complexity; Comparing the data complexity with the complexity comparison table to obtain a compression level, wherein the compression level includes: high complexity compression, medium complexity compression, and low complexity compression; The corresponding subframe is compressed according to a compression method that matches the compression level.
[0029] In this embodiment, the edge detection algorithm uses mathematical operators to identify areas (edges) where pixel grayscale changes dramatically in the image, and distinguish the structural contours of the image from the smooth background. For example, the Canny edge detection algorithm is used to extract clear edges through Gaussian filtering noise reduction → calculation of gradient amplitude and direction → non-maximum suppression → hysteresis threshold processing. The edge detection kernel is implemented in hardware in the FPGA, or the OpenCV library function is called in the DSP.
[0030] In this embodiment, a multi-layer convolutional neural network (CNN) is a deep learning model consisting of multiple convolutional layers, pooling layers, and fully connected layers. It is used to extract image features and reduce noise. For example, a three-layer CNN is designed: the first layer uses 3×3 convolution kernels to extract basic textures, the second layer uses 5×5 kernels to capture complex structures, and the third layer uses deconvolution to restore image resolution. The input is a noisy image, and the output is a clear, denoised image. The model is trained using TensorFlow / PyTorch and then quantized and deployed to an embedded GPU or NPU chip.
[0031] In this embodiment, color space conversion (RGB→CIELAB) converts the image from a three-channel representation of red, green, and blue to a CIELAB space of luminance (L*) and chrominance (a*, b*), facilitating independent processing of luminance and color. For example, the image is converted to XYZ space using the formula X=0.4124R+0.3576G+0.1805B, and then L∗=116(Y / Yn)1 / 3-16 (Yn=0.008856) is calculated. A dedicated color conversion circuit is designed in the ASIC, or a lookup table (LUT) is used to accelerate the conversion.
[0032] In this embodiment, the gamma correction lookup table compensates for the nonlinear electro-optical response of the display (such as a gamma value of 2.2) and establishes a mapping relationship between the input signal and the actual brightness. For example, for an 8-bit grayscale value V, the output brightness L=(V / 255)2.2×Lmax. A high-precision LUT is generated by offline measuring the voltage-brightness curve of the display. It is calibrated with a spectrometer before leaving the factory, and the LUT is stored in the driver chip register for real-time lookup and correction.
[0033] In this embodiment, color gamut compression and expansion is to map the image color to the color gamut range that the display can display (compression), or to expand it to a color gamut close to the original through an algorithm (expansion). For example, for HDR images that exceed the sRGB color gamut, the CIEDE2000 color difference formula is used to calculate the compressed chromaticity coordinates to ensure that ΔE≤3; during expansion, principal component analysis (PCA) is used to stretch along the main direction of the color gamut. For example, the color gamut mapping of each pixel is calculated in parallel in the GPU, or a dedicated color gamut processing IP core is integrated in the driver chip.
[0034] In this embodiment, the subframe and the duty cycle of the driving signal are to divide a frame time into multiple subframes (such as 10 subframes), and each subframe controls the pixel brightness through pulse width modulation (PWM). The duty cycle determines the proportion of the luminous time. For example, for 256 levels of brightness, the subframe length is 10μs, and the brightness value of 128 corresponds to a duty cycle of 50% (5μs high level). Specifically, the subframe clock is generated in the timing controller, and the digital duty cycle is converted into an analog driving voltage through a D / A converter.
[0035] In this embodiment, spectrum diffusion processing is performed by randomizing the subframe sequence and pulse phase to diffuse the energy of the driving signal from narrowband noise to broadband, thereby reducing electromagnetic interference (EMI). For example, a pseudo-random number generator (PRNG) is used to generate a subframe sequence, and the pulse phases of adjacent subframes are offset by 10° to 30°. The PRNG module is integrated into the clock generator of the driving circuit to implement spectrum diffusion in hardware.
[0036] The decompression algorithm restores the original image information from the compressed sub-frame data, supporting both lossless (such as Huffman coding) and lossy (such as inverse DCT) decompression. For example, for the motion vectors and residual signals of low-complexity sub-frames, pixel blocks are restored through inverse quantization and IDCT; for high-complexity sub-frames, CABAC entropy decoding is used to restore pixel values. Specifically, a reconfigurable decompression engine is designed in the FPGA to support dynamic switching between multiple compression algorithms.
[0037] The error checking algorithm detects and corrects transmission errors through redundant check codes, such as cyclic redundancy check (CRC) and Hamming code. For example, the sender calculates the CRC-32 check code for each data block, and the receiver verifies it. The erroneous block triggers a retransmission mechanism. Specifically, a hardware CRC check module is integrated into the data transmission channel to verify data integrity in real time.
[0038] Histogram equalization expands the brightness histogram of dark areas to the entire dynamic range to improve the visibility of details. For example, for local dark areas (brightness <50), its histogram is calculated and mapped to the range of 0~255 to make the grayscale distribution of dark pixels more uniform. Specifically, the histogram is calculated by partition in the image processing chip and each area is processed in parallel.
[0039] In this embodiment, the temperature-performance model establishes a mapping relationship between temperature and parameters such as the threshold voltage of the driver chip and the mobility of the transistor, predicts performance degradation and compensates in advance. For example, the formula Vth=Vth0-0.5mV / °C×(T-25°C) for fitting the threshold voltage change with temperature is used through experimental data. When the temperature rises, the driving voltage is increased to compensate for the threshold drift. Specifically, a temperature sensor is integrated inside the driver chip, and the compensation parameters are updated in real time through the microcontroller.
[0040] The beneficial effects of the above technical solution are: details are retained based on edge detection and CNN noise reduction, gamma correction and color gamut processing ensure color accuracy, contrast enhancement improves dark / bright details, sub-frame dynamic compression reduces compression loss of high-complexity content, temperature compensation and error checking mechanism reduce the failure rate of the driving circuit, and realize full-link optimization from image preprocessing to driving control, while ensuring high-precision display, improving driving efficiency, reliability and adaptability.
[0041] The present invention provides a display screen driving method, which performs nonlinear transformation on the color components of an enhanced image according to the color gamut range of the display screen, comprising: The image after dynamic contrast enhancement is converted from RGB color space to CIELAB color space, and the CIELAB three-dimensional color gamut boundary model of the display color gamut is established, where the color gamut boundary is composed of N color gamut vertex coordinates. The convex polygonal area is represented by: For each pixel Define the color gamut matching function :
[0042] in, is the orthogonal projection of the chromaticity coordinates on the convex hull boundary of the color gamut, by solving the minimum Euclidean distance Determine the projection point; is the color gamut mapping function of the brightness component, is the global brightness extreme value of the image; is the brightness boundary value; is the brightness of the corresponding pixel.
[0043] In this embodiment, dynamic contrast enhancement is to increase the brightness of dark areas of the image, compress overexposure of bright areas, and enhance the contrast between light and dark details through algorithms (such as local histogram equalization and Retinex), thereby providing a richer color foundation for subsequent color gamut processing.
[0044] RGB→CIELAB color space conversion converts device-dependent RGB signals (such as the electrical signals output by a display) into a CIELAB space consistent with human visual perception (L*: brightness, a*: red-green axis, b*: yellow-blue axis), avoiding color deviations between different devices and facilitating unified color gamut processing.
[0045] The CIELAB three-dimensional color gamut boundary model uses the most extreme color coordinates that a display can display as vertices (such as the red, green, and blue primaries and the white point) to construct a three-dimensional convex polyhedron (simplified as a convex polygonal region in this article, but actually the convex hull of Lab three-dimensional space) to define the color capability boundary of the display.
[0046] The color gamut matching function determines whether the pixel (L, a, b) is within the color gamut, and segments the pixels within the color gamut (retained) and outside the color gamut (mapped to the boundary) to ensure color displayability.
[0047] In this embodiment, the brightness linear mapping formula retains the brightness hierarchy relationship (dark areas remain dark, bright areas remain bright, but the range is compressed), which is easier to implement than nonlinear mapping. In addition, the human eye is more sensitive to linear changes in brightness, ensuring visual consistency.
[0048] The beneficial effects of the above technical solution are: through CIELAB space processing and orthogonal projection mapping, colors outside the color gamut are pulled back to the displayable boundaries of the display. The formula design has low computational complexity (Euclidean distance and linear scaling are both O(1) operations). Combined with segmented processing, single-pixel mapping can be completed at the μs level. The color gamut boundary model is defined by the display vertex coordinates (N vertices can be customized for different screens) and is compatible with various display technologies such as LCD, OLED, and MiniLED; the brightness range can be flexibly adjusted according to screen parameters. Through the complete chain of color space conversion → color gamut boundary modeling → segmented mapping (chromaticity projection + brightness scaling), the core problem of how to realistically display image colors when they exceed the display's capabilities is solved. It combines accuracy, real-time performance, and versatility, and is a key technical support for color processing on high-end displays.
[0049] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.
Claims
1. A display screen driving method, characterized in that: include: Step 1: Use edge detection algorithm to identify edge areas and non-edge areas in the input image, extract image features through multi-layer convolutional neural network and perform noise interference processing; Step 2: The noise-processed image is subjected to color space conversion and contrast enhancement. The color components of the enhanced image are nonlinearly transformed according to the color gamut of the display. The luminance component is adjusted by establishing a gamma correction lookup table to compensate for the nonlinear electro-optical conversion characteristics of the display. The chrominance component is compressed and expanded to obtain a new image. Step 3: The new image is transmitted according to the preset scanning order, and the data complexity of the subframes in each scanning order is determined and compressed accordingly, and then transmitted to the display driver circuit; Step 4: Set a decompression algorithm and an error checking algorithm in the driving circuit, decompress and error-check the transmitted compressed sub-frame, and drive the pixel units of the display screen to display.
2. The display screen driving method according to claim 1, wherein: The image after noise interference processing is subjected to contrast enhancement processing, including: The global average brightness and local brightness distribution of the image after noise interference processing are calculated. The baseline value for contrast enhancement is determined based on the global average brightness. For dark areas where the brightness of the local area is lower than the baseline value, a local contrast enhancement algorithm based on histogram equalization is used to expand the brightness histogram of the dark area to the entire dynamic range. For bright areas where the brightness of the local area is higher than the reference value, a brightness compression algorithm based on logarithmic transformation is used. At the same time, a visual saliency detection model is introduced to identify the region of interest in the image and perform contrast enhancement on the region of interest. An enhanced processed image is obtained according to the enhanced processing result.
3. The display screen driving method according to claim 1, wherein: Before transferring new images into the preset scanning order, including: Dividing the display time of the new image into a plurality of subframes, each subframe corresponding to a different driving signal combination; For each pixel unit, the duty cycle of the driving signal in each subframe is calculated based on the corresponding color value and brightness value, and the corresponding driving pulse signal is generated through pulse width modulation technology. When generating the driving signal, the driving signal of adjacent subframes is smoothly transitioned based on the response time and afterglow effect of the display screen. At the same time, the driving signal is subjected to spectrum diffusion processing, using random subframe sequence and driving pulse phase to disperse the energy of the driving signal into a wider frequency range; A preset scanning order is obtained according to the smooth transition processing result and the dispersion result.
4. The display screen driving method according to claim 1, wherein: Transmitting the data to the display screen drive circuit includes: dividing each compressed subframe into multiple data blocks according to rows and columns, and transmitting the data in parallel to different drive areas of the display screen through multiple independent data transmission channels, wherein each data transmission channel is equipped with an independent clock signal and synchronization control module, and dynamically adjusting the clock frequency and transmission rate according to the location and data volume of each drive area, so that the data of each channel reaches the drive circuit synchronously; During the transmission process, a pre-fetch buffering technique is used to read the data block of the next compressed subframe in advance and store it in a buffer memory; At the same time, a data retransmission mechanism is set up. When a data transmission error is detected in the channel, the data block of the corresponding channel is automatically retransmitted.
5. The display screen driving method according to claim 1, wherein: Before driving the pixel unit of the display screen to display, it also includes: The built-in sensor array is used to detect the threshold voltage and luminous efficiency of each pixel unit in the display in real time; For pixel units with drifting threshold voltage, the amplitude and bias voltage of the driving signal are dynamically adjusted; For pixel units with attenuated luminous efficiency, the pulse width or amplitude of the driving signal is increased to compensate for the brightness attenuation. During the compensation process, a fuzzy control algorithm is used to automatically adjust the compensation parameters based on the degree of parameter change of the pixel unit and the overall display status of the display.
6. The display screen driving method according to claim 1, wherein: After driving the pixel units of the display screen to display, the process also includes: integrating a temperature sensor in the driving circuit to monitor the temperature of the driving chip and circuit board in real time, and activating a heat dissipation mechanism when the temperature exceeds a preset threshold; A temperature-performance model is established to predict the performance changes of the driving circuit based on real-time temperature data, determine the advance compensation factor, and dynamically adjust the parameters of the driving signal based on the advance compensation factor while starting the heat dissipation mechanism to optimize the display of the pixel unit of the driving display.
7. The display screen driving method according to claim 1, wherein: Perform nonlinear transformation on the color components of the enhanced image according to the color gamut range of the display, including: The image after dynamic contrast enhancement is converted from RGB color space to CIELAB color space, and the CIELAB three-dimensional color gamut boundary model of the display color gamut is established, where the color gamut boundary is composed of N color gamut vertex coordinates. The convex polygonal area is represented by: For each pixel Define the color gamut matching function : in, is the orthogonal projection of the chromaticity coordinates on the convex hull boundary of the color gamut, by solving the minimum Euclidean distance Determine the projection point; is the color gamut mapping function of the brightness component, is the global brightness extreme value of the image; is the brightness boundary value; is the brightness of the corresponding pixel.
8. The display screen driving method according to claim 7, wherein: Perform color gamut compression and expansion on chroma components, including: The current color gamut coordinates of each pixel unit are obtained through the built-in spectral sensor, a time-varying color gamut model is established, and a color gamut vertex set is obtained; The dimensionality of the color gamut vertex set is reduced to obtain the principal component directions e1 and e2, and an expansion transformation matrix is constructed to convert the chromaticity coordinates to be expanded into spatial coordinates, and the expansion boundary constraints are defined.
9. The display screen driving method according to claim 6, wherein: Predict the performance changes of the drive circuit based on real-time temperature data and determine the advance compensation factor, including: Deploy multi-dimensional sensors at key nodes of the driving circuit to collect multi-dimensional data sets; The multidimensional dataset is input into the temperature-performance mapping model of the driving circuit to analyze the influence of temperature changes on the driving core parameters and establish a corresponding relationship between temperature and driving signal performance indicators. In combination with the predicted performance change direction and amplitude, an advance compensation factor is obtained.
10. The display screen driving method according to claim 1, wherein: Determine the data complexity of the subframes in each scanning order and perform corresponding compression processing, including: Determine the spatial complexity and time complexity of the corresponding subframe to obtain the data complexity; Comparing the data complexity with the complexity comparison table to obtain a compression level, wherein the compression level includes: high complexity compression, medium complexity compression, and low complexity compression; The corresponding subframe is compressed according to a compression method that matches the compression level.