Data processing method, data processing device and display panel
By identifying the focus and non-focus areas in the liquid crystal display device and using differentiated encoding, the problem of increased power consumption caused by the improvement of display resolution is solved, and low-power data transmission is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HKC CORP LTD
- Filing Date
- 2026-06-22
- Publication Date
- 2026-07-21
Smart Images
Figure CN122435901A_ABST
Abstract
Description
Technical Field
[0001] This disclosure belongs to the field of display technology, and specifically relates to a data processing method, a data processing device, and a display panel. Background Technology
[0002] In liquid crystal display devices, the transmission bandwidth of data signals is closely related to power consumption. As the display resolution increases, the amount of data transmission between the timing controller and the source driver increases dramatically, resulting in a significant increase in the overall power consumption of the panel.
[0003] To reduce data transmission power consumption, related technologies typically use a uniform encoding method for the entire frame of the image, such as transmitting 8 bits of data for each pixel or uniformly encoding fixed pixel blocks. However, this uniform encoding method results in a large amount of redundant data being transmitted in areas insensitive to the human eye, causing unnecessary power consumption.
[0004] Therefore, how to reduce data processing power consumption without affecting the display quality is an urgent problem to be solved. Summary of the Invention
[0005] This application provides a data processing method, a data processing device, and a display panel. By using human eye sensitivity partitioning, adaptive division of non-focus pixels, and collaborative processing of differential encoding, this application achieves a reduction in the power consumption of panel data processing without affecting the subjective image quality of the human eye.
[0006] In a first aspect, this application provides a data processing method, the data processing method comprising: calculating the edge density and color variance of each pixel within a preset window based on the brightness value of each pixel in a current input frame image; marking each pixel as a focal pixel or a non-focal pixel based on the edge density and the color variance; dividing all non-focal pixels into multiple non-focal pixel clusters of different sizes based on the number of consecutive non-focal pixels; encoding each focal pixel using a first encoding method to obtain first encoded data corresponding to each focal pixel; encoding each non-focal pixel cluster using a second encoding method to obtain second encoded data corresponding to each non-focal pixel cluster; wherein the data volume of the first encoding method is greater than the data volume of the second encoding method; and combining all the first encoded data and all the second encoded data into a transmission data frame corresponding to the current input frame image.
[0007] Secondly, this application provides a data processing apparatus, comprising: a pixel calculation module, configured to calculate the edge density and color variance of each pixel within a preset window based on the brightness value of each pixel in the current input frame image; a pixel marking module, configured to mark each pixel as a focal pixel or a non-focal pixel based on the edge density and the color variance; a cluster partitioning module, configured to divide all non-focal pixels into multiple non-focal pixel clusters of different sizes based on the number of consecutive non-focal pixels; a pixel encoding module, configured to encode each focal pixel using a first encoding method to obtain first encoded data corresponding to each focal pixel; and to encode each of the non-focal pixel clusters using a second encoding method to obtain second encoded data corresponding to each non-focal pixel cluster; wherein the data volume of the first encoding method is greater than the data volume of the second encoding method; and a data combination module, configured to combine all the first encoded data and all the second encoded data into a transmission data frame corresponding to the current input frame image.
[0008] Thirdly, this application provides a display panel, including a display area and a non-display area. The display area includes multiple scan lines and multiple data lines. The non-display area includes: a gate driving circuit electrically connected to the scan lines for outputting gate driving signals to the scan lines; a data driving circuit electrically connected to the data lines for outputting data signals to the data lines; and a timing controller electrically connected to the data driving circuit via a transmission line for sending data signals to the data driving circuit and for executing the data processing method.
[0009] The technical solution provided in this application has at least the following beneficial effects:
[0010] This application identifies focal and non-focal regions in an image by calculating the edge density and color variance of each pixel within a preset window, thus achieving image partitioning based on human visual attention characteristics. By adaptively dividing consecutive non-focal pixels into clusters of different sizes, the coordinate transmission overhead during encoding is reduced. By employing a differentiated encoding strategy for focal and non-focal regions, the data transmission volume in non-focal regions is significantly reduced while ensuring lossless image quality in the focal regions. Finally, all encoded data is combined into a transmission data frame, completing low-power data transmission. Therefore, this application, through the collaborative processing of human eye sensitivity partitioning, adaptive non-focal pixel partitioning, and differentiated encoding, significantly reduces the panel's data processing power consumption without affecting subjective image quality. Attached Figure Description
[0011] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.
[0012] Figure 1 The diagram shown is a flowchart of a data processing method provided in an embodiment of this application.
[0013] Figure 2 As shown Figure 1 A schematic diagram of an exemplary process for step S100.
[0014] Figure 3 As shown Figure 1 A schematic diagram of an exemplary process for step S400.
[0015] Figure 4 The diagram shown is a flowchart of another data processing method provided in an embodiment of this application.
[0016] Figure 5 The diagram shown is a structural schematic of a data processing device provided in an embodiment of this application.
[0017] Among them, 510 is the pixel calculation module, 520 is the pixel marking module, 530 is the cluster partitioning module, 540 is the pixel encoding module, and 550 is the data combination module. Detailed Implementation
[0018] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.
[0019] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.
[0020] The present application will now be described in further detail with reference to the accompanying drawings and specific embodiments. It should be noted that the technical features involved in the various embodiments described below can be combined with each other as long as they do not conflict with each other. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present application, and should not be construed as limiting the present application.
[0021] Firstly, this application provides a data processing method, specifically including the following embodiments: Figure 1 The diagram shown is a flowchart illustrating a data processing method provided in an embodiment of this application; as follows: Figure 1 As shown, the data processing method of this embodiment is applicable to the timing controller of a display panel, used to reduce data transmission power consumption and ensure display quality, and specifically includes the following steps: Step S100: Calculate the edge density and color variance of each pixel within a preset window based on the brightness value of each pixel in the current input frame image.
[0022] It should be noted that the current input frame image in this embodiment is the image to be displayed. First, the brightness value of each pixel needs to be obtained. Then, for each pixel, a preset window (e.g., a 3×3 pixel block) is constructed with it as the center. Within this preset window, two key parameters are calculated: edge density and color variance.
[0023] Edge density measures the proportion of pixels with abrupt changes in brightness within a window, reflecting the level of detail in that area. Specifically, it iterates through each pair of adjacent pixels within the window. If the difference in brightness values between the two pixels is significant (e.g., greater than or equal to a preset difference), an edge is considered to exist, and the corresponding pixel is marked as an edge pixel. The edge density is calculated by dividing the total number of edge pixels within the window by the total number of pixels in the window. Furthermore, color variance measures the dispersion of pixel brightness values within the window, reflecting the contrast strength of that area. A larger variance indicates a greater difference in brightness within the window, typically corresponding to high-contrast areas (such as text edges or facial features).
[0024] Step S200: Mark each pixel as a focal pixel or a non-focal pixel based on edge density and color variance.
[0025] Specifically, if the edge density of the current pixel is greater than the density threshold and the color variance of the current pixel is greater than the variance threshold, the current pixel is marked as a focal pixel; otherwise, the current pixel is marked as a non-focal pixel.
[0026] It should be noted that the human eye pays significantly different attention to different areas in an image: for areas with rich edges and high contrast (such as the strokes of text, facial features, and object outlines), the human eye is extremely sensitive, and image quality degradation will be immediately noticed; while for flat, low-contrast areas (such as solid color backgrounds, skies, and walls), the human eye pays less attention, and a slight reduction in image quality will not attract attention.
[0027] Based on the characteristics of human vision, this embodiment sets a density threshold and a variance threshold. When the edge density of a pixel is greater than the density threshold and the color variance is greater than the variance threshold, the pixel is marked as a focal pixel (representing a region sensitive to human vision); otherwise, it is marked as a non-focal pixel (representing a region insensitive to human vision).
[0028] Step S300: Based on the number of consecutive non-focal pixels, divide all non-focal pixels into multiple non-focal pixel clusters of different sizes.
[0029] It should be noted that pixels marked as non-focal pixels are usually located in flat areas of the image (such as solid color backgrounds, gradient skies, etc.), and these areas often exhibit large areas of continuity. If each non-focal pixel is encoded separately, coordinate information needs to be transmitted for each pixel, which would actually increase the amount of data. Therefore, this embodiment adopts an adaptive clustering strategy, merging multiple consecutive non-focal pixels into pixel blocks (clusters), and encoding each cluster as a whole, thereby significantly reducing the overhead of coordinate information transmission.
[0030] In this embodiment, based on the number of consecutive non-focal pixels, all non-focal pixels are divided into multiple non-focal pixel clusters of different sizes, specifically including: If the number of consecutive non-focal pixels is greater than or equal to a first number threshold (e.g., 64), then these consecutive pixels are divided into clusters of a first size (e.g., an 8×8 cluster, i.e., 64 pixels forming a square block). If the number of consecutive non-focal pixels is greater than or equal to the second number threshold (e.g., 32) and less than the first number threshold, they are divided into clusters of the second size (e.g., 4×4 clusters, i.e., 16 pixels forming a square block). If the number of consecutive non-focal pixels is less than the second threshold, they are not grouped into separate blocks. Instead, these scattered pixels are merged into adjacent clusters to avoid coding redundancy caused by fragmented clusters.
[0031] This adaptive cluster partitioning ensures effective compression of non-focus areas while avoiding the additional coordinate overhead caused by small-sized clusters, further improving data transmission efficiency.
[0032] Step S400: Encode each focal pixel using the first encoding method to obtain the first encoded data corresponding to each focal pixel; encode each non-focal pixel cluster using the second encoding method to obtain the second encoded data corresponding to each non-focal pixel cluster.
[0033] It should be noted that, since the focal region and non-focal regions have different visual importance to the human eye, this embodiment adopts a differentiated encoding strategy for the two types of regions. Specifically: (1) For the focal pixel (the area sensitive to human eyes), the first encoding method is adopted, that is, the complete information is preserved. Since the number of focal pixels is usually small (for example, only the area of text strokes), even if each pixel transmits more data bits, the impact on the overall data volume is very limited, and the image quality of the area can be guaranteed to be lossless.
[0034] (2) For non-focal pixel clusters (areas insensitive to the human eye), a second encoding method is used, namely compressed transmission. Since the pixel brightness values within the cluster usually change gradually, only a reference brightness value and a small number of differential quantization values can be transmitted, which greatly reduces the amount of data.
[0035] In this embodiment, the amount of second data obtained by the second encoding method is significantly smaller than the amount of first data obtained by the first encoding method, thereby achieving effective compression of non-focus areas.
[0036] Step S500: Combine all the first encoded data and all the second encoded data into a transmission data frame corresponding to the current input frame image.
[0037] It should be noted that after encoding all focal and non-focal pixel clusters, these encoded data are sequentially stitched together according to the raster scan order, i.e., from left to right and from top to bottom, to form a complete transmission data frame. This transmission data frame can be sent to the source driver via a standard display interface (such as LVDS or MIPI DSI). After receiving the transmission data frame, the source driver parses each encoded data packet according to the same protocol, restores the brightness value of each pixel, and then drives the liquid crystal pixels to emit light, completing the display of one frame of image.
[0038] In summary, this application identifies focal and non-focal regions in an image by calculating the edge density and color variance of each pixel within a preset window, thus achieving image partitioning based on human visual attention characteristics. By adaptively dividing consecutive non-focal pixels into clusters of different sizes, it reduces coordinate transmission overhead during encoding. By employing differentiated encoding strategies for focal and non-focal regions, it significantly reduces data transmission volume in non-focal regions while ensuring lossless image quality in the focal areas. Finally, it combines all encoded data into a transmission data frame, achieving low-power data transmission. Therefore, this application, through the collaborative processing of human eye sensitivity partitioning, adaptive non-focal pixel partitioning, and differentiated encoding, significantly reduces panel data processing power consumption without affecting subjective image quality.
[0039] In one embodiment, before calculating the edge density and color variance of each pixel within a preset window based on the brightness value of each pixel in the current input frame image, the data processing method further includes: weighting and summing the red channel value, green channel value, and blue channel value of each pixel in the current input frame image according to a preset weight to obtain the initial brightness value of each pixel; obtaining a filtering window corresponding to each pixel with each pixel as the center, and using the arithmetic mean of the initial brightness values of all pixels within the filtering window as the brightness value of the corresponding pixel.
[0040] It should be noted that before calculating edge density and color variance, the original input image needs to be preprocessed to obtain reliable and suitable brightness data for analysis. Specifically, this embodiment also includes the following preprocessing operations: (1) Gray-scale normalization processing: The timing controller receives the original RGB data of the current input frame image (each pixel contains three channels: red, green, and blue, each 8 bits). To simplify the subsequent edge density and color variance calculations and avoid the high computational overhead caused by calculating the three channels separately, this embodiment converts the color image into a single-channel gray-scale image; based on the experimental results of human eye sensitivity to different colors, the green channel is given the highest weight, followed by the red channel, and the blue channel is given the lowest weight. The specific formula is as follows: Y = 0.299×R + 0.587×G + 0.114×B (1) Where R, G, and B are the red, green, and blue channel pixel values of the original image (range 0-255), respectively, and Y is the calculated brightness value (range 0-255). Through the brightness conversion of formula (1), the 24-bit RGB data of each pixel is compressed into 8-bit brightness data. All subsequent calculations such as edge density and color variance are based on this single-channel brightness value. The amount of computation is reduced by about 66% compared with the three-channel processing. At the same time, there is no need to cache the three-channel data, which effectively reduces the logic gate occupation and power consumption of the TCON chip.
[0041] (2) Filtering: Raw RGB data is prone to salt-and-pepper noise during acquisition or transmission (e.g., individual pixels in a pure white background abnormally turn into black dots). If the edge density is directly calculated on the noisy luminance data, these noise points will be misjudged as edge pixels, causing the pure color background area to be incorrectly marked as the focal area, thus wasting encoding resources and driving power. To eliminate such interference, this embodiment performs mean filtering on the luminance data after grayscale conversion. The specific operation is as follows: taking each pixel as the center, select 9 neighboring pixels in 3 rows and 3 columns around it to form a filtering window, calculate the arithmetic mean of the initial luminance values of all pixels in the window, and use this average value as the luminance value of the current pixel after filtering. The filtering formula is as follows: Y'(x,y) = [Σ(i=-1 to 1) Σ(j=-1 to 1) Y(x+i,y+j)] / 9(2) Where Y'(x,y) is the brightness value of the (x,y) coordinate after filtering, and Y(x+i,y+j) is the original brightness value within a 3×3 window surrounding (x,y). Through this mean filtering, isolated noise points are smoothed by the average brightness of the surrounding pixels, and their brightness values are corrected to be consistent with the neighborhood, so they will not be misclassified as edge pixels in subsequent edge detection. At the same time, mean filtering preserves the overall edge features of the image.
[0042] Therefore, this embodiment converts the RGB three-channel data into single-channel brightness data by grayscale normalization, which greatly reduces the complexity of subsequent calculations; the 3×3 mean filtering effectively suppresses salt-and-pepper noise, avoids the influence of noise on the misjudgment of the focus area, ensures the accuracy of edge density and color variance calculation, and lays a reliable data foundation for subsequent visual focus recognition.
[0043] Figure 2 As shown Figure 1 A schematic diagram of an exemplary process for step S100; as shown below. Figure 2 As shown, based on the brightness value of each pixel in the current input frame image, the edge density and color variance of each pixel within a preset window are calculated, specifically including the following steps: Step S110: Construct a preset window corresponding to the current pixel, centered on the current pixel.
[0044] Step S120: Traverse each pair of adjacent pixels within the preset window. If the absolute value of the difference in brightness values between the current pair of adjacent pixels is greater than or equal to the preset difference, then mark the next pixel in the current pair of adjacent pixels as an edge pixel.
[0045] Step S130: Count the total number of pixels marked as edges within the preset window, divide the total number by the total number of pixels within the preset window, and obtain the edge density corresponding to the current pixel.
[0046] Step S140: Based on the brightness value of the current pixel and the average brightness value of all pixels in the preset window, obtain the color variance corresponding to the current pixel.
[0047] It should be noted that in this embodiment, edge density reflects the richness of detail in the area where the current pixel is located, and color variance reflects the brightness dispersion in that area. Together, they constitute the core basis for judging human eye attention. The specific calculation process is as follows: (1) Constructing a preset window: Centered on the currently processed pixel P, with coordinates (x, y), select 9 neighboring pixels in a row and column of 3 to form a preset window. This window covers the current pixel and its 8 neighboring pixels above, below, left, right, upper left, upper right, lower left, and lower right, forming a 3×3 square pixel block. The choice of 3×3 for the window size is based on empirical values of human visual characteristics: a window that is too small (e.g., 1×1) cannot reflect local texture information, while a window that is too large (e.g., 5×5) will lose the precision of detail position. Therefore, 3×3 achieves a good balance between computational complexity and perceptual accuracy.
[0048] (2) Edge density calculation: Edge density is used to measure the proportion of pixels where the brightness changes abruptly within a window, reflecting whether there are human-sensitive details such as the outline of an object or the strokes of text in that area. Specifically: First, iterate through each pair of adjacent pixels within the window. Adjacent pixel pairs include horizontally adjacent (e.g., left-right) and vertically adjacent (e.g., top-bottom), totaling 12 pairs (2 pairs of horizontally adjacent pixels per row in 3 rows, for a total of 6 pairs; 2 pairs of vertically adjacent pixels per column in 3 columns, for a total of 6 pairs). For each pair of adjacent pixels, calculate the absolute value of the difference between the brightness values of the two pixels.
[0049] Then, this difference is compared with a preset difference value ΔYth. In this embodiment, ΔYth is set to 8, which is a threshold determined based on human visual experiments: when the brightness difference between adjacent pixels is less than 8, the human eye can hardly perceive the brightness change; when it reaches or exceeds 8, the human eye can clearly perceive the brightness change, that is, it is considered that an edge exists. Therefore, if the absolute value of the brightness difference between the current pair of adjacent pixels is ≥8, then the next pixel in the pair (according to the scanning direction, for example, the pixel to the right in the horizontal direction, and the pixel below in the vertical direction) is marked as an edge pixel.
[0050] For example, in a 3x3 window, if the brightness values of two horizontally adjacent pixel pairs are 100 and 110 respectively, and the difference is 10 ≥ 8, then the pixel on the right is marked as an edge pixel. After traversing all 12 pairs of adjacent pixels, the total number of pixels marked as edge pixels within the window is counted and denoted as Nedge. Since there are 9 pixels in the window, the edge density D = Nedge / 9. The value of D ranges from 0 to 1; the larger the value of D, the more edge pixels there are within the window, and the richer the details.
[0051] (3) Color variance calculation: Color variance is used to measure the dispersion of pixel brightness values within a window, reflecting the contrast strength of that area. The larger the variance, the more significant the brightness difference within the window, usually corresponding to high-contrast areas (such as the edge of black text on a white background); the smaller the variance, the more uniform the brightness distribution, which may be a flat area or a gradient area. The specific steps for calculating color variance are as follows: First, calculate the average brightness value of all 9 pixels within the window, denoted as Yavg.
[0052] Then, calculate the square of the difference between the brightness value of each pixel and the average value, sum these squared values, and divide by the total number of pixels (9) to obtain the variance σ². To maintain consistency with the dimensions of the brightness values, the standard deviation σ is usually obtained by taking the square root as the color variance. The formula for calculating the color variance can be expressed as: σ = sqrt(Σ(Y_i - Y_avg)² / 9).
[0053] Therefore, this embodiment determines edge pixels and calculates their density by measuring the brightness difference between adjacent pixel pairs within a 3×3 preset window, effectively identifying the richness of local details; by calculating the variance of brightness values within the window, it quantifies the strength of local contrast. The combination of these two methods accurately captures the high sensitivity of the human eye to areas such as text, faces, and object edges, providing a precise spatial partitioning basis for subsequent differentiated coding and power consumption optimization.
[0054] Figure 3 As shown Figure 1 A schematic diagram of an exemplary process for step S400; as shown below. Figure 3 As shown, each focal pixel is encoded using the first encoding method to obtain the first encoded data corresponding to each focal pixel, specifically including: Step S410: The preset first type identifier, the position information of the focus pixel in the current input frame image, and the brightness value of the focus pixel are concatenated in a preset order to generate the first encoded data corresponding to the focus pixel.
[0055] It should be noted that for pixels marked as focal pixels, complete brightness information needs to be retained to ensure lossless image quality. In this embodiment, the first encoded data corresponding to each focal pixel is composed of three parts concatenated in a preset order: (1) First type identifier: occupies 1 bit and is used to distinguish the current encoded data type. In this embodiment, the first type identifier is set to "0", which indicates that the current encoded data corresponds to the focal pixel type.
[0056] (2) Position information: occupies 16 bits, of which the high 8 bits represent the row coordinates (Y coordinates) of the focus pixel in the image and the low 8 bits represent the column coordinates (X coordinates), which are used to indicate the specific position of the pixel on the display panel.
[0057] (3) Brightness value: occupies 8 bits and directly stores the filtered brightness value of the focal pixel (range 0 to 255) without any compression or quantization.
[0058] Therefore, the total length of the first encoded data corresponding to each focal pixel is 1 + 16 + 8 = 25 bits. Although 25 bits is longer than the original 8-bit brightness data, since the focal pixel usually occupies only a small proportion of the entire image (for example, the area of the text region is much smaller than the background), the additional transmission overhead has a negligible impact on the overall power consumption, and in return, the image quality of that area remains unaffected.
[0059] For example, suppose a focal pixel is located at row 100 and column 200 of an image, and its original brightness value is 220. Then the first encoded data corresponding to this pixel is: first type identifier "0" (1 bit) + row coordinate "01100100" (8 bits, i.e., 100 in binary) + column coordinate "11001000" (8 bits, i.e., 200 in binary) + brightness value "11011100" (8 bits, i.e., 220 in binary). The concatenated complete bit string is "0 01100100 11001000 11011100".
[0060] In this embodiment, each non-focal pixel cluster is encoded using a second encoding method to obtain the second encoded data corresponding to each non-focal pixel cluster, specifically including: Step S420: Determine the center pixel of the current non-focus pixel cluster and use the brightness value of the center pixel as the reference brightness value.
[0061] Step S430: For each pixel in the current non-focus pixel cluster, calculate the difference between the brightness value of each pixel and the reference brightness value, quantize the difference according to the preset quantization bit, and obtain the difference quantization value corresponding to each pixel.
[0062] Step S440: The preset second type identifier, the position information of the non-focal pixel cluster in the current input frame image, the reference brightness value, and the difference quantization value corresponding to all pixels are concatenated in a preset order to generate the second encoded data corresponding to the non-focal pixel cluster.
[0063] It should be noted that for areas divided into non-focal pixel clusters, since the brightness changes between adjacent pixels are gradual, this embodiment uses compression encoding to significantly reduce the amount of data transmission. The specific encoding process is as follows: (1) Determine the reference brightness value. For the current non-focal pixel cluster to be encoded, such as an 8×8 pixel block with a total of 64 pixels, first determine the center pixel of the cluster. Since there is no unique center pixel in an 8×8 cluster, the pixel in the 4th row and 4th column (or the 4th row and 5th column) is usually taken as the center pixel; obtain the filtered brightness value of the center pixel and use it as the reference brightness value Yref of the entire cluster. The reference brightness value characterizes the overall brightness level of the cluster.
[0064] (2) Calculate and quantize the difference for each pixel. For each pixel in the cluster (including the center pixel itself), calculate the difference between its brightness value and the reference brightness value: ΔY = Yi - Yref. Since the brightness change of pixels in the cluster is gradual, ΔY is usually within a very small range (e.g., between -7 and +8). In this embodiment, a preset quantization bit depth (e.g., 4 bits) is used to quantize this difference. 4 bits can represent 16 levels from 0 to 15. The range of ΔY (e.g., -7 to +8) is mapped to 0 to 15 through linear mapping to obtain the difference quantization value. The center pixel has ΔY = 0, which corresponds to a certain intermediate value (e.g., 7 or 8) after quantization. The quantization formula can be expressed as: quantization value = round( (ΔY + offset) / quantization step size).
[0065] (3) Concatenate to generate second encoded data: Concatenate the following fields in a preset order to form the second encoded data corresponding to the non-focal pixel cluster: ① Second type identifier: occupies 1 bit, and is set to "1" in this embodiment, indicating that the current encoded data corresponds to a non-focal pixel cluster.
[0066] ② Cluster position information: Occupies 16 bits, of which the high 8 bits represent the row coordinates of the top left pixel of the cluster and the low 8 bits represent the column coordinates, used to indicate the starting position of the cluster on the display panel.
[0067] ③ Reference brightness value: occupies 8 bits, which is the brightness value Yref of the center pixel.
[0068] ④ Differential quantization values of all pixels: Each differential quantization value occupies 4 bits. There are M pixels in the cluster (for example, an 8×8 cluster has 64 pixels), so the total length of this field is 4×M bits. All differential quantization values are arranged in the raster scan order (from left to right, from top to bottom).
[0069] Therefore, the total length of the second encoded data for an 8×8 non-focal pixel cluster containing 64 pixels is: 1 + 16 + 8 + 64 × 4 = 1 + 16 + 8 + 256 = 281 bits. However, if the traditional method is used to transmit the original 8 bits of data per pixel, it would require 64 × 8 = 512 bits. The data compression rate in this embodiment is (512 - 281) / 512 ≈ 45.1%, which reduces the data volume by nearly half. For a 4×4 cluster (16 pixels), the encoded length is 1 + 16 + 8 + 16 × 4 = 1 + 16 + 8 + 64 = 89 bits, while the original is 16 × 8 = 128 bits, resulting in a compression rate of approximately 30.5%.
[0070] Therefore, this embodiment uses lossless encoding for the focal area to preserve the complete details that are sensitive to the human eye and ensure that the image quality is not degraded; however, the non-focal area uses compression encoding of the reference brightness value + difference quantization value, which reduces the data volume by about 30% to 45% and directly reduces the power consumption of the transmission link.
[0071] In one embodiment, after combining the first encoded data and the second encoded data into a transmission data frame corresponding to the current input frame image, the data processing method further includes: a timing controller sending the transmission data frame to a source driver circuit in the form of a serial data stream; the source driver circuit parsing the type identifier in the currently received encoded data; if the type identifier in the current encoded data is a first type identifier, then before outputting the data voltage corresponding to the current encoded data, configuring the operating voltage of the source driver circuit to a first operating voltage and configuring the operating current of the source driver circuit to a first operating current; if the type identifier in the current encoded data is a second type identifier, then before outputting the data voltage corresponding to the current encoded data, configuring the operating voltage of the source driver circuit to a second operating voltage and configuring the operating current of the source driver circuit to a second operating current; wherein, the second operating voltage is less than the first operating voltage, and the second operating current is less than the first operating current.
[0072] It should be noted that after the encoding and combination of the transmitted data frames are completed, the timing controller (TCON) needs to send the data to the source driver circuit, which then dynamically adjusts its operating voltage and current according to the received data type to further optimize power consumption in the non-focus area. This includes the following steps: (1) Data transmission and type identification: The timing controller sends the transmission data frame generated in step S500 above to the source driver circuit bit by bit through the display interface (such as LVDS or MIPI DSI) in the form of a serial data stream. While receiving the data, the source driver circuit performs real-time parsing on each encoded data packet and first reads the type identifier in the data packet. Based on the different type identifiers, the source driver circuit can determine whether the data to be processed is focal pixel data or non-focal pixel cluster data.
[0073] (2) Differentiated configuration of operating voltage and operating current: Since the requirements for transmission reliability and driving accuracy of focal pixel data and non-focal pixel cluster data are different, this embodiment adopts differentiated source drive circuit operating voltage and operating current, as follows: ① When the type identifier is the first type identifier (focus pixel): The focus pixel data contains the original 8-bit brightness value, which is extremely sensitive to transmission errors. If the operating voltage is insufficient, it may cause errors in high-bit data, resulting in obvious image quality defects. Therefore, before outputting the data voltage corresponding to the current encoded data, the source driver circuit configures its operating voltage to a higher first operating voltage (e.g., 3.3V) and its operating current to a larger first operating current (e.g., 10μA). The higher voltage and current ensure signal integrity, guaranteeing error-free transmission of 8-bit data, while providing sufficient driving capability to quickly establish a high-precision grayscale voltage.
[0074] ② When the type identifier is the second type identifier (non-focal pixel cluster): The non-focal pixel cluster data only contains 4 bits of differential quantization value, which has a higher tolerance for voltage fluctuations and noise; even if the operating voltage is appropriately reduced, it will not cause decoding errors. Therefore, before outputting the data voltage corresponding to the current encoded data, the source driver circuit configures its operating voltage to a lower second operating voltage (e.g., 1.8V), and simultaneously configures its operating current to a smaller second operating current (e.g., 4μA). The second operating voltage is lower than the first operating voltage, and the second operating current is lower than the first operating current.
[0075] (3) To ensure that voltage switching does not affect data transmission and display effects, this embodiment completes the configuration switching before outputting the data voltage corresponding to the current encoded data. Specifically, after the source driver circuit parses the type identifier of the current encoded data packet, there is a short time window (e.g., one or more clock cycles) before it starts converting the data packet into pixel grayscale voltage and outputting it to the data line. The source driver circuit uses this time window to switch the operating voltage and current from the previous state to the current target state through internal registers or control logic.
[0076] To avoid voltage fluctuations and screen flickering caused by frequent switching, this embodiment also employs a smooth switching mechanism: the voltage / current switching is completed within one clock cycle (or a preset short time window) before cluster data transmission, and the switching process uses a gradual or soft switching method to avoid voltage abrupt changes. Since non-focus pixel clusters usually occupy most of the screen area, multiple consecutive clusters may have the same type identifier. In this case, the voltage and current remain unchanged, and frequent switching does not occur; switching only occurs when focus pixels and non-focus pixel clusters alternate (e.g., at the edge of text), but since the switching occurs between data packets, it is imperceptible to the human eye.
[0077] For example, suppose a frame contains 200 focal pixels and multiple clusters of non-focal pixels. The timing controller sends encoded data sequentially: first, it sends the first encoded data for a focal pixel. The source driver circuit resolves the type identifier to "0", switches the operating voltage from the current 1.8V to 3.3V, and the operating current from 4μA to 10μA, then outputs the grayscale voltage of that focal pixel. Next, it sends the second encoded data for an 8×8 non-focal cluster (containing 64 pixels). The source driver circuit resolves the type identifier to "1", switches the operating voltage from 3.3V back to 1.8V, and the operating current from 10μA back to 4μA, then outputs the grayscale voltages of all 64 pixels in that cluster sequentially. This process repeats, allowing the source driver circuit to consume higher power only when processing focal pixels, while operating in a low-power mode when processing large background areas, significantly reducing the average power consumption of the entire frame.
[0078] Figure 4 The diagram shown is a flowchart illustrating another data processing method provided in an embodiment of this application; as follows: Figure 4 As shown, after combining all the first encoded data and all the second encoded data into the transmission data frame corresponding to the current input frame image, the data processing method further includes the following steps: Step S600: At every preset number of display frames, collect the actual display brightness value of the center pixel in each non-focus pixel cluster in the current display frame.
[0079] Step S700: Calculate the distortion rate of each non-focal pixel cluster based on the difference between the actual display brightness value of each center pixel and the reconstructed brightness value corresponding to the center pixel.
[0080] Step S800: Obtain the display distortion rate corresponding to the current display frame based on the average distortion rate of all non-focus pixel clusters.
[0081] Step S900: When the display distortion rate is greater than the first distortion rate threshold, reduce the cluster size of the non-focus pixel cluster by one level; when the display distortion rate is less than the second distortion rate threshold, increase the cluster size of the non-focus pixel cluster by one level; when the display distortion rate is greater than or equal to the second distortion rate threshold and less than or equal to the first distortion rate threshold, keep the current cluster size of the non-focus pixel cluster unchanged.
[0082] It should be noted that, due to the use of lossy compression encoding in the non-focal pixel clusters and the operation of the source drive circuit at lower voltage and current in the non-focal area, the actual displayed brightness may deviate slightly from the theoretically reconstructed brightness. To control these deviations within a range imperceptible to the human eye, this embodiment introduces a closed-loop feedback calibration mechanism. This mechanism periodically collects the actual displayed brightness, calculates the distortion rate, and dynamically adjusts the size of the non-focal pixel clusters accordingly. This achieves a dynamic balance between power consumption optimization and image quality assurance. Specifically, the following steps are included: (1) Periodic sampling: In this embodiment, feedback calibration is not performed for every frame, but rather a sampling operation is performed once every preset number of display frames to reduce the power consumption of the feedback calibration itself. For example, it can be set to trigger sampling and calibration once every 10 frames of images are displayed. This preset number can be configured according to the panel response speed and the frequency of changes in the displayed content, such as 5 frames, 10 frames or 20 frames.
[0083] (2) Actual display brightness acquisition: After calibration is triggered, the system samples each non-focal pixel cluster in the current display frame. To improve sampling efficiency and ensure statistical representativeness, only the actual display brightness value of the center pixel of each non-focal pixel cluster is acquired. The position of the center pixel is defined in the same way as the reference pixel position selected during encoding. The acquisition of the actual brightness value can be achieved by the analog-to-digital converter (ADC) integrated inside the LCD panel driver chip. This ADC can read the voltage on the pixel electrode and convert it into a digital brightness value without additional hardware.
[0084] (3) Obtaining the reconstructed luminance value: For each sampled center pixel, the system needs to obtain its reconstructed luminance value. The reconstructed luminance value refers to the theoretical luminance value of the pixel after quantization and compression during the encoding stage, and then dequantization back to 8 bits. Specifically, in steps S400 and S500, each pixel is quantized to obtain a target quantization value Q, which is encoded and stored in the transmitted data frame. During the feedback calibration stage, the system calculates the reconstructed luminance value Lrecon based on the stored Q value using the same dequantization formula as during encoding. For example, if a center pixel uses 4-bit quantization (quantization level 0~15) during encoding, the dequantization formula is: Lrecon = Q×(255 / 15) ≈ Q×17. Therefore, the reconstructed luminance value Lrecon(i) corresponding to each center pixel is known.
[0085] (4) Calculation of distortion rate of a single cluster: For the i-th non-focal pixel cluster, the distortion rate δ(i) of the cluster is obtained by dividing the absolute difference between the actual brightness Lact(i) and the reconstructed brightness Lrecon(i) of the center pixel by the maximum brightness value 255 and then multiplying by 100%, as shown in formula (2): δ(i) = |Lact(i) - Lrecon(i)| / 255×100% (2) The distortion rate δ(i) reflects the degree of brightness deviation caused by encoding compression and driving voltage variations to the central pixel of the cluster. The larger the δ(i), the more severe the image quality loss.
[0086] For example: Suppose the center pixel of an 8×8 cluster has a reconstructed brightness Lrecon=200 and an actual displayed brightness Lact=195, with a difference of 5. Then the distortion rate δ = 5 / 255×100% ≈ 1.96%. If the center pixel of another cluster has Lrecon=100, Lact=92, and a difference of 8, the distortion rate δ≈3.14%.
[0087] (5) Calculation of average display distortion rate: Take the arithmetic mean of the distortion rates δ(i) of all non-focus pixel clusters to obtain the display distortion rate Davg corresponding to the current display frame, as shown in formula (3): Davg = (δ(1)+δ(2)+…+δ(N)) / N(3) Where N is the total number of non-focal pixel clusters; the display distortion rate Davg reflects the overall distortion level of the non-focal area of the entire frame, avoiding misjudgment due to individual pixel abnormalities.
[0088] (6) Dynamic cluster size adjustment: Based on the comparison results of the display distortion rate Davg with the first distortion rate threshold (e.g., 3%) and the second distortion rate threshold (e.g., 1%), different cluster size adjustment strategies are implemented: ① When Davg > 3%: This indicates that the image quality distortion has exceeded the range perceptible to the human eye, and the currently used cluster size is too large, resulting in insufficient quantization accuracy. In this case, the cluster size of the non-focal pixel clusters should be reduced by one level, i.e., the 8×8 cluster should be reduced to a 4×4 cluster, and the 4×4 cluster should be reduced to a 1×1 cluster. Reducing the cluster size can improve the spatial resolution of the encoding, thereby reducing the range of brightness variation within a single cluster and reducing quantization error.
[0089] ② When Davg < 1%: This indicates that the current image quality has a margin of error and distortion is extremely low, meaning it can withstand greater compression to further save power. In this case, the cluster size of non-focus pixel clusters is increased by one level, i.e., a 4×4 cluster is increased to an 8×8 cluster, and 1×1 clusters, if existing and consecutive, are merged into 4×4 or 8×8 clusters. Increasing the cluster size can further reduce the transmission overhead of coordinates and reference values, thus reducing the amount of data.
[0090] ③ When 1%≤Davg≤3%: This indicates that the current quantization parameters and drive settings have reached an ideal balance between image quality and power consumption, and the distortion rate is within a range imperceptible to the human eye. At this time, the current cluster size remains unchanged.
[0091] (7) Adjusted parameter memory: The cluster size configuration that has been adjusted and stabilized can be associated with the grayscale feature parameters of the current image and stored to form an optimal parameter record. When a frame with similar image features is encountered again, the optimal cluster size can be directly used without re-performing feedback calibration, further reducing system overhead.
[0092] Therefore, this embodiment periodically collects actual display brightness, calculates the distortion rate, and dynamically adjusts the cluster size based on the distortion rate to ensure that the average distortion rate is always controlled within a threshold imperceptible to the human eye, maintaining stable image quality even under conditions of panel aging, temperature changes, or individual differences. Furthermore, when image quality is high enough, the cluster size is actively increased to further reduce data volume and transmission power consumption; when image quality deteriorates, the cluster size is reduced to protect image quality, achieving an adaptive balance between power consumption and image quality.
[0093] Secondly, this application provides a data processing apparatus, specifically including the following embodiments: Figure 5 The diagram shown is a structural schematic of a data processing device provided in an embodiment of this application; as follows: Figure 5 As shown, the data processing apparatus of this embodiment includes: The pixel calculation module 510 is used to calculate the edge density and color variance of each pixel within a preset window based on the brightness value of each pixel in the current input frame image.
[0094] The pixel marking module 520 is used to mark each pixel as a focal pixel or a non-focal pixel based on edge density and color variance.
[0095] The cluster partitioning module 530 is used to divide all non-focal pixels into multiple non-focal pixel clusters of different sizes based on the number of consecutive non-focal pixels.
[0096] The pixel encoding module 540 is used to encode each focal pixel using a first encoding method to obtain first encoded data corresponding to each focal pixel; and to encode each non-focal pixel cluster using a second encoding method to obtain second encoded data corresponding to each non-focal pixel cluster; wherein the amount of data in the first encoding method is greater than the amount of data in the second encoding method.
[0097] The data combination module 550 is used to combine all the first encoded data and all the second encoded data into a transmission data frame corresponding to the current input frame image.
[0098] It should be noted that the working principle of the data processing device in this embodiment is the same as that of the method embodiment described above, and will not be repeated here.
[0099] Thirdly, this application provides a display panel including a display area and a non-display area. The display area includes multiple scan lines and multiple data lines. The non-display area includes: a gate driving circuit electrically connected to the scan lines for outputting gate driving signals to the scan lines; a data driving circuit electrically connected to the data lines for outputting data signals to the data lines; and a timing controller electrically connected to the data driving circuit via a transmission line for sending data signals to the data driving circuit, and also for the data processing method shown in the above embodiments.
[0100] Furthermore, the terms "first," "second," and "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.
[0101] In the description of this specification, references to terms such as "some embodiments," "exemplarily," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. The illustrative expressions of the above terms in this specification do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
[0102] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application. Therefore, any changes or modifications made in accordance with the claims and description of this application should fall within the scope of this patent application.
Claims
1. A data processing method, characterized in that, The data processing method includes: Based on the brightness value of each pixel in the current input frame image, calculate the edge density and color variance of each pixel within a preset window; wherein, the edge density represents the proportion of pixels whose brightness changes abruptly within the preset window, and the color variance represents the degree of dispersion of the pixel brightness values within the preset window; Based on the edge density and the color variance, each pixel is marked as a focal pixel or a non-focal pixel; wherein, if the edge density of the current pixel is greater than a density threshold and the color variance of the current pixel is greater than a variance threshold, the current pixel is marked as a focal pixel; otherwise, the current pixel is marked as a non-focal pixel. Based on the number of consecutive non-focal pixels, all non-focal pixels are divided into multiple non-focal pixel clusters of different sizes; Each focal pixel is encoded using a first encoding method to obtain first encoded data corresponding to each focal pixel; each non-focal pixel cluster is encoded using a second encoding method to obtain second encoded data corresponding to each non-focal pixel cluster; wherein, the data volume of the first encoding method is greater than the data volume of the second encoding method; Combine all the first encoded data and all the second encoded data into the transmission data frame corresponding to the current input frame image.
2. The data processing method according to claim 1, characterized in that, Before calculating the edge density and color variance of each pixel within a preset window based on the brightness value of each pixel in the current input frame image, the data processing method further includes: The red, green, and blue channel values of each pixel in the current input frame image are weighted and summed according to a preset weight to obtain the initial brightness value of each pixel; Centered on each pixel, obtain the corresponding filter window for each pixel, and take the arithmetic mean of the initial brightness values of all pixels in the filter window as the brightness value of the corresponding pixel.
3. The data processing method according to claim 1, characterized in that, Based on the brightness value of each pixel in the current input frame image, calculate the edge density and color variance of each pixel within a preset window, including: Construct a preset window centered on the current pixel; Traverse each pair of adjacent pixels within the preset window. If the absolute value of the difference in brightness values between the current pair of adjacent pixels is greater than or equal to a preset difference, then mark the next pixel in the current pair of adjacent pixels as an edge pixel. The total number of pixels marked as edges within the preset window is counted, and the total number is divided by the total number of pixels within the preset window to obtain the edge density corresponding to the current pixel. The color variance corresponding to the current pixel is obtained based on the brightness value of the current pixel and the average brightness value of all pixels within the preset window.
4. The data processing method according to claim 3, characterized in that, Based on the edge density and the color variance, each pixel is marked as a focal pixel or a non-focal pixel, including: If the edge density of the current pixel is greater than the density threshold and the color variance of the current pixel is greater than the variance threshold, then the current pixel is marked as the focus pixel. Otherwise, mark the current pixel as a non-focus pixel.
5. The data processing method according to claim 1, characterized in that, Each focal pixel is encoded using a first encoding method to obtain the first encoded data corresponding to each focal pixel, including: The first type identifier, the position information of the focal pixel in the current input frame image, and the brightness value of the focal pixel are concatenated in a preset order to generate the first encoded data corresponding to the focal pixel; wherein, the first type identifier indicates the type of the focal pixel; Each of the non-focal pixel clusters is encoded using a second encoding method to obtain second encoded data corresponding to each non-focal pixel cluster, including: Determine the center pixel of the current non-focus pixel cluster, and use the brightness value of the center pixel as the reference brightness value; For each pixel in the current non-focus pixel cluster, calculate the difference between the brightness value of each pixel and the reference brightness value, and quantize the difference according to a preset quantization bit to obtain the difference quantization value corresponding to each pixel; The second type identifier, the position information of the non-focal pixel cluster in the current input frame image, the reference brightness value, and the difference quantization values corresponding to all pixels are concatenated in a preset order to generate the second encoded data corresponding to the non-focal pixel cluster; wherein, the second type identifier represents the non-focal pixel cluster.
6. The data processing method according to claim 5, characterized in that, After combining the first encoded data and the second encoded data into a transmission data frame corresponding to the current input frame image, the data processing method further includes: The timing controller sends the transmitted data frame to the source drive circuit in the form of a serial data stream; The source drive circuit parses the type identifier in the currently received encoded data; If the type identifier in the current encoded data is the first type identifier, then before outputting the data voltage corresponding to the current encoded data, the operating voltage of the source drive circuit is configured to the first operating voltage, and the operating current of the source drive circuit is configured to the first operating current. If the type identifier in the current encoded data is the second type identifier, then before outputting the data voltage corresponding to the current encoded data, the operating voltage of the source drive circuit is configured to the second operating voltage, and the operating current of the source drive circuit is configured to the second operating current. Wherein, the second operating voltage is less than the first operating voltage, and the second operating current is less than the first operating current.
7. The data processing method according to any one of claims 1-6, characterized in that, After combining all the first encoded data and all the second encoded data into the transmission data frame corresponding to the current input frame image, the data processing method further includes: At every preset number of display frames, the actual display brightness value of the center pixel in each non-focus pixel cluster in the current display frame is collected; The distortion rate of each non-focal pixel cluster is calculated based on the difference between the actual displayed brightness value of each center pixel and the reconstructed brightness value corresponding to the center pixel; wherein, the reconstructed brightness value is the brightness value obtained by inverse quantization based on the target quantization value corresponding to the center pixel; The display distortion rate corresponding to the current display frame is obtained by averaging the distortion rates of all non-focus pixel clusters. When the display distortion rate is greater than the first distortion rate threshold, the cluster size of the non-focus pixel cluster is reduced by one level; When the display distortion rate is less than the second distortion rate threshold, the cluster size of the non-focus pixel cluster is increased by one level; When the display distortion rate is greater than or equal to the second distortion rate threshold and less than or equal to the first distortion rate threshold, the cluster size of the current non-focus pixel cluster remains unchanged.
8. The data processing method according to any one of claims 1-5, characterized in that, The step of dividing all non-focal pixels into multiple non-focal pixel clusters of different sizes based on the number of consecutive non-focal pixels includes: When the number of consecutive non-focal pixels is greater than or equal to a first quantity threshold, the consecutive non-focal pixels are divided into clusters of a first size; When the number of consecutive non-focal pixels is greater than or equal to a second quantity threshold and less than a first quantity threshold, the consecutive non-focal pixels are divided into clusters of a second size, where the second size is smaller than the first size. When the number of consecutive non-focal pixels is less than the second quantity threshold, the consecutive non-focal pixels are merged into an adjacent cluster; The first quantity threshold can be 64, and the second quantity threshold can be 32; the first size is 8×8, and the second size is 4×4.
9. A data processing apparatus, characterized in that, The data processing device includes: The pixel calculation module is used to calculate the edge density and color variance of each pixel within a preset window based on the brightness value of each pixel in the current input frame image. A pixel marking module is used to mark each pixel as a focal pixel or a non-focal pixel based on the edge density and the color variance; The cluster partitioning module is used to divide all non-focal pixels into multiple non-focal pixel clusters of different sizes based on the number of consecutive non-focal pixels. A pixel encoding module is used to encode each focal pixel using a first encoding method to obtain first encoded data corresponding to each focal pixel; and to encode each non-focal pixel cluster using a second encoding method to obtain second encoded data corresponding to each non-focal pixel cluster; wherein, the data volume of the first encoding method is greater than the data volume of the second encoding method; The data combination module is used to combine all the first encoded data and all the second encoded data into a transmission data frame corresponding to the current input frame image.
10. A display panel, comprising a display area and a non-display area, wherein the display area includes multiple scan lines and multiple data lines; characterized in that, The non-display area includes: A gate driving circuit, electrically connected to the scan line, is used to output a gate driving signal to the scan line; A data driving circuit, electrically connected to the data line, is used to output data signals to the data line; A timing controller, electrically connected to the data driving circuit via a transmission line, is used to send data signals to the data driving circuit and to execute the data processing method according to any one of claims 1-8.