CN113823234A - RGB Mini-LED field sequence backlight control system and method - Google Patents

RGB Mini-LED field sequence backlight control system and method Download PDF

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CN113823234A
CN113823234A CN202111382153.3A CN202111382153A CN113823234A CN 113823234 A CN113823234 A CN 113823234A CN 202111382153 A CN202111382153 A CN 202111382153A CN 113823234 A CN113823234 A CN 113823234A
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backlight
partition
brightness
color
image
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CN113823234B (en
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文博
魏伟
殷永旸
徐金成
朱广鹏
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Nanjing Panda Electronics Manufacturing Co Ltd
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    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
    • G09G3/00—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes
    • G09G3/20—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters
    • G09G3/34—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters by control of light from an independent source
    • G09G3/3406—Control of illumination source
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
    • G09G3/00—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes
    • G09G3/20—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters
    • G09G3/34—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters by control of light from an independent source
    • G09G3/3406—Control of illumination source
    • G09G3/3413—Details of control of colour illumination sources
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
    • G09G3/00—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes
    • G09G3/20—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters
    • G09G3/34—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters by control of light from an independent source
    • G09G3/3406—Control of illumination source
    • G09G3/342—Control of illumination source using several illumination sources separately controlled corresponding to different display panel areas, e.g. along one dimension such as lines
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
    • G09G2320/00—Control of display operating conditions
    • G09G2320/02—Improving the quality of display appearance
    • G09G2320/0242—Compensation of deficiencies in the appearance of colours
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
    • G09G2320/00—Control of display operating conditions
    • G09G2320/06—Adjustment of display parameters
    • G09G2320/0626—Adjustment of display parameters for control of overall brightness
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
    • G09G2330/00—Aspects of power supply; Aspects of display protection and defect management
    • G09G2330/02—Details of power systems and of start or stop of display operation
    • G09G2330/021—Power management, e.g. power saving

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computer Hardware Design (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Liquid Crystal Display Device Control (AREA)
  • Control Of Indicators Other Than Cathode Ray Tubes (AREA)

Abstract

The invention provides an RGB Mini-LED field sequence backlight control system which comprises a video signal input module, a storage module, a data processing and control module, a panel driving display module, a backlight driving module and a backlight display module. According to the invention, a deep learning model between contrast, power consumption, color splitting indexes and system complexity and the backlight partition number of the color RGB Mini-LED lamp beads is established, and the optimal backlight partition is obtained. The backlight control system processes the field sequence partition images and adopts a backlight control algorithm of self-adaptive partition base color adjustment, so that the backlight driving signal is dynamically adjusted field by field and partition by partition, and the color and brightness control of the regional backlight matched with the field sequence display image information is realized.

Description

RGB Mini-LED field sequence backlight control system and method
Technical Field
The invention belongs to the technical field of liquid crystal display equipment, and particularly relates to an RGB Mini-LED field sequence backlight control system and method.
Background
Liquid crystal display equipment is widely applied in modern society, and the realization of image display of the liquid crystal panel is mainly completed under the control of a backlight module by matching a time sequence control module with a glass substrate at present. The backlight module is composed of a lamp bead array, and the display of different color effects is realized by adjusting and controlling the light through the brightness of the lamp beads and through a color filter. Due to the existence of the color filter inside the glass substrate, the cost of the whole machine is increased and the light effect is reduced. The traditional time color mixing field sequential display does not need a color filter, and the R/G/B LED color lamp beads are turned on field by field and time by frame to realize the time color mixing display, and the mode and the space color mixing can realize the same color image display effect. The traditional field sequential display is limited by the refresh frequency of the liquid crystal panel, which can cause the phenomenon of color splitting, thereby affecting the visual experience.
The traditional field sequential display backlight scheme performs time-sharing control on a global image to achieve the effect and the purpose of field sequential display. However, the conventional field sequential display scheme does not perform backlight control and adjustment of the partitions, does not research and quantify the partition scheme, fails to specify selection factors of the number of backlight partitions, does not quantitatively analyze and determine the relationship between the number of partitions and performances such as contrast and power consumption of the display device, fails to consider the complexity of implementation of the partition scheme, and does not consider the influence of the number of partitions on color splitting of field sequential display, so that the field sequential display device has poor display effect.
Disclosure of Invention
Aiming at the problems existing in the traditional scheme, the quantitative relation between the backlight partition quantity and the four of the contrast, the power consumption, the color splitting index and the system complexity is determined, so that a backlight partition quantity model is determined, and the effect superior to the traditional field sequential display is realized by a field-by-field partition-by-partition backlight base color control method. The technical problem to be solved by the invention is to provide an RGB Mini-LED field sequence backlight control system and method, firstly, a field sequence backlight control system is set up, a deep learning model between contrast, power consumption, color splitting indexes, system complexity and backlight subarea number is established, and the optimal backlight subarea number of the field sequence RGB Mini-LED lamp beads is researched. And finally, a backlight control algorithm of self-adaptive partition base color adjustment is adopted, the image distortion degree under the condition of the RGB Mini-LED ultra-multi partition backlight quantity is minimized, the image display contrast is improved, the color splitting problem of field sequential display is effectively improved, and the power consumption is optimized.
The invention first discloses an RGB Mini-LED field sequence backlight control system, which comprises:
and the video signal input module is mainly used for receiving external video or image signals and inputting the received signals to the data processing and control module. The video signal comprises a video stream signal in serial formats such as HDMI and DP. The image signal is a parallel data signal in a bitmap format.
And the storage module is mainly used for caching the frame data of the input video stream and carrying out algorithm processing on the backlight information of the extracted image data by the data processing and control module.
And the data processing and control module is mainly used for realizing the conversion of data formats, the processing and realization of algorithms and backlight. The method specifically comprises the following steps: (1) and a data coding and decoding unit. The unit mainly converts a data signal received by the video signal input module into a screen end driving signal and outputs the screen end driving signal to the panel driving display module; (2) and a backlight partition processing unit. The unit mainly adjusts the color and the brightness of each backlight partition unit through a partition backlight adjusting method, and outputs the adjusted backlight driving signal to the backlight driving module to realize the control of the backlight partition brightness and the backlight partition color. (3) And a backlight refresh control unit. The unit mainly realizes the refreshing of the backlight primary colors and the synchronization of the field frequency, thereby ensuring the synchronization of the refreshing frequency and the field frequency of each backlight primary color of each subarea, and ensuring the synchronization of the display backlight control signal and the data signal of the field sequence color mixing.
The panel drives the display module, the module includes data drive unit, grid drive unit and liquid crystal display panel. The data driving unit is mainly used for receiving a coded data driving signal output by the data processing and controlling module, converting the coded data driving signal into pixel gray scale voltage of the panel through analog-digital conversion, and driving the panel to display; the grid driving unit is mainly used for receiving scanning driving control signals output by the data processing and control module, realizing the on and off of the panel pixel switch and realizing the display and refresh of a frame of image by matching with the data driving module. The pixel units in the liquid crystal display panel realize the display of different gray scales of images under the action of pixel gray scale voltage and scanning driving control signals. In the field sequential display system of the invention, the liquid crystal panel is a panel without a color filter with high refresh rate.
And the backlight driving module is mainly used for receiving the backlight data driving signals which are obtained by the data processing and control module in a field-by-field and partition-by-partition mode, so that the driving current of the corresponding lamp beads is dynamically adjusted, the color and the brightness information of the primary colors of the sub-field partitions are changed, and the self-adaptive adjustment and output of the backlight brightness and the color of each partition are realized.
And the backlight display module is mainly used for receiving the current output by the backlight driving module and realizing the display of the brightness and the primary colors of the RGB Mini-LED lamp beads in a field-by-field and partition-by-partition manner.
The invention also discloses an RGB Mini-LED field sequence backlight control method, which comprises the following two parts: and researching the field sequence optimal backlight partition number and the adaptive adjustment and control of the primary colors and the brightness of the regional backlight. The RGB Mini-LED field sequence backlight control method comprises the following steps:
step 1: under different experiment partition numbers and different types of pictures, utilizing a power meter to obtain corresponding power consumption data; measuring by a brightness meter to obtain the highest brightness and the lowest brightness of the corresponding picture and the corresponding partition, and calculating to obtain contrast data;
step 2: taking the partition number and the picture pixel three-channel RGB value as model input, taking the power consumption and contrast data obtained in the step 1 as model output, and respectively establishing a PCNN (power consumption) convolutional neural network deep learning algorithm model and a CCNN (contrast convolutional neural network) deep learning algorithm model;
and step 3: acquiring the space geometric distance between the color coordinates of the RGB primary colors of each partition and the color coordinates of the target pixel points in the partition, wherein the distance considers the influence of the difference between the target points and the RGB primary colors under the white picture on color splitting and is recorded as an influence factor delta;
and 4, step 4: calculating the distance between the color coordinates of the target pixel points and the color coordinates of the white points in the subareas, wherein the distance takes the influence factors of the colors of the target pixel points on color splitting into consideration and is recorded as an influence factor epsilon;
and 5: calculating the difference value between the brightness of the target pixel point in the partition and the maximum brightness value of the adjacent pixel points to obtain the contrast of the adjacent pixels, and recording the contrast as an influence factor zeta;
step 6: synthesizing the products of the influence factors of the calculation results of the steps 3, 4 and 5 to obtain the color splitting index value of the target pixel point;
and 7: taking the partition number and the image pixel three-channel RGB value as model input, taking the color splitting index value data obtained in the step 6 as model output, and establishing a CBCNN convolutional neural network deep learning algorithm model;
and 8: setting the partition-power consumption model function obtained according to the step 2 as F1(x) The partition-contrast model function is set to F2(x) And setting the partition-color splitting index model function partition obtained in the step 7 as F3(x) X is the number of partitions;
and step 9: obtaining characteristic values representing power consumption, contrast, color splitting and cost of all different types of pictures under different partitions, and establishing a partition objective function F (x) = kF according to the three characteristic values1(x)+mF2(x)+ qF3(x) + nP (x), wherein k, m, n and q are correction compensation coefficients, P (x) is a partition-cost function, and finally, F (x) is used for researching a comprehensive optimal partition value through a differential extreme point to obtain the optimal backlight partition number;
step 10: partitioning the image according to the optimal backlight partition number, extracting RGB three-channel gray-scale values of each pixel in the partition, determining color coordinates of all pixels in the partition according to the RGB values of the pixels of the partitioned image, acquiring reasonable color coordinates of new three primary colors in a clustering mode, and obtaining each primary color tristimulus value of the new three primary colors through coordinate system conversion, thereby determining the three primary colors under the partition as Qi1、Qi2、Qi3Where i ∈ [1, x ]]X is the number of partitions;
step 11: graying the partitioned image, taking a gray-scale value as a brightness value, selecting an optimal brightness matrix of the image, taking the gray-scale image as input, taking the optimal brightness matrix as output, establishing a backlight brightness convolution neural network model (BLCNN), and finally obtaining an image backlight target brightness matrix of all partitions of the whole image based on the BLCNN. The optimal brightness matrix acquisition method comprises the following steps: respectively adopting a maximum value method, an average method, a CDF method and a peak signal-to-noise ratio method to respectively extract backlight brightness of the input gray-scale image to obtain different backlight brightness matrixes, and selecting the backlight brightness matrix with the best display effect as an optimal brightness matrix through a subjective evaluation method;
step 12: calculating the proportion of the luminous flux of each channel of the three primary colors according to the color coordinates of the three primary colors, and obtaining new brightness of the three primary colors field by field through the target brightness after the light mixing of the partitions;
step 13: performing backlight fuzzy simulation on the original backlight brightness matrix by adopting a backlight fuzzy function to obtain real backlight brightness distribution;
step 14: by BL, based on the principle that the image is not distorted before and after dimmingr*LCr=BLf*LCfObtaining a gray-scale compensation value LC of the pixel after dimming by taking the influence of the nonlinear compensation GAMMA into considerationf. Wherein BLrFor the brightness of the backlight before dimming, LCrFor the pixel gray scale before dimming, BLfAnd (4) realizing the driving output of the pixel gray-scale compensation signal for the real backlight distribution obtained in the step (13), namely the backlight brightness after dimming. Finally, the brightness of the regional image primary color backlight is adjusted by matching with the backlight of different primary colors under three fields, and the image display under the final field sequence scheme is realized through convolution of the real backlight distribution and the image pixel gray scale.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
1. according to the method, the quantity of the backlight partitions, the contrast, the power consumption and the color splitting index quantification model are established, and the influence of the complexity of the partition implementation scheme on the optimal partition scheme is comprehensively considered, so that the determination of the optimal partition quantity is more objective and more accurate, the backlight power consumption is reduced, and the contrast and the display quality of image display are improved;
2. according to the invention, by selecting a reasonable backlight partition number scheme and adopting a field-by-field partition-by-partition base color and brightness self-adaptive adjustment method, the color splitting phenomenon of field sequential display is greatly reduced, so that the display effect is improved;
3. according to the scheme, the optimal backlight is determined according to the actual image content, and the backlight fuzzy function is combined, so that the RGB Mini-LED regional dimming precision can be effectively improved, more details are reserved, and the display performance is improved.
Drawings
Fig. 1 is a block diagram of a field sequential backlight control system scheme according to an embodiment of the present invention.
FIG. 2 is a flowchart illustrating steps of an RGB Mini-LED field sequential backlight control method according to the present invention.
FIG. 3 is a schematic diagram of a convolutional neural network deep learning model of backlight partitioning according to the present invention.
FIG. 4 is a schematic diagram of a convolutional neural network deep learning model of a backlight luminance matrix according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
With reference to fig. 1, an RGB Mini-LED field sequential backlight control system includes:
the video signal input module 1 is mainly used for receiving an external video or image signal and inputting the received signal to the data processing and control module 2. The video signal comprises a video stream signal or an image signal in serial formats such as HDMI and DP. The image signal is a parallel data signal in a bitmap format. In an embodiment of the present invention, the video signal input module receives RGB bitmap image data of 24 bits for transmission of an HDMI signal stream input from the front end, and transmits the signal stream data to the data processing and control module 2.
And the storage module 6 is mainly used for caching frame data of the input video stream and performing algorithm processing on the backlight information of the extracted image data by the data processing and control module 2. In an embodiment of the present invention, the storage module is a DDR module, and the storage module is configured to buffer one frame of image data written by the video signal data processing module, and after the data processing and control module 2 finishes processing one frame of data and sends one frame of image field by field, read the image data of the next frame from the storage module for processing until all the video stream data or the image data are completely displayed.
The data processing and control module 2 mainly realizes the conversion of data formats and the processing and realization of algorithms. In an embodiment of the present invention, the data processing and control module 2 is an FPGA module having algorithm operation and data processing capabilities, and specifically includes: (1) and a data coding and decoding unit. The unit mainly converts a data signal received by the video signal input module 1 into a screen end driving signal and outputs the screen end driving signal to the panel driving display module 3; (2) and a backlight partition processing unit. The unit mainly adjusts the color and the brightness of the primary color of each backlight partition unit through a partition backlight adjusting method, and outputs the adjusted backlight driving signal to the backlight driving module to realize the field-by-field primary color brightness and color control of the backlight partition. The data coding and decoding unit is used for decoding the received HDMI serial signal stream into parallel RGB bitmap image data signals and line-field synchronous control signals, obtaining 30-bit bitmap data by bit width expansion of 24-bit bitmap data, and coding the 30-bit bitmap data into serial data conforming to the type of a panel interface protocol under the action of the control signals according to the interface protocol of the panel display driving module. Meanwhile, the data processing and control module 2 adjusts the brightness and color of each backlight partition unit by adopting a partition dimming scheme according to the image content under the corresponding image partition with 30-bit bitmap data, and outputs the adjusted backlight driving signal to the backlight driving module 4 to realize the control of the brightness and color of the backlight partition. (3) And a backlight refresh control unit. The unit mainly realizes refreshing of the backlight primary colors and synchronization of the field frequency, so that the refreshing frequency of each backlight primary color of each partition is ensured to be synchronous with the field frequency (three fields of backlight primary colors exist under each partition, in order to ensure the synchronization of the backlight and the image content, namely, the refreshing frequency of the backlight of a first field is consistent with the field frequency of the first field, and in the same way, the refreshing frequency of the backlight of a second field and the refreshing frequency of the backlight of a third field are also consistent with the field frequency of the first field), and synchronization of a display backlight control signal and a data signal of field sequence color mixing is ensured. In an embodiment of the present invention, the frame frequency is 120Hz, and the field frequency is 360Hz, and in order to ensure synchronization of the backlight and the image data, the backlight refresh control unit controls the backlight refresh frequency of the primary colors in all the partitions of each field to be 360Hz, so as to implement synchronization of the backlight primary color refresh and the field frequency.
The panel drives the display module 3, which includes a data driving unit, a gate driving unit and a liquid crystal display panel (the liquid crystal display panel is a liquid crystal panel without a color filter and with a refresh rate of 360 Hz). The data driving unit is mainly used for receiving the coded data driving signal output by the data processing and control module 2, converting the coded data driving signal into pixel gray scale voltage of the panel through analog-digital conversion, and driving the panel to display; the grid driving unit is mainly used for receiving scanning driving control signals output by the data processing and control module, realizing the on and off of the panel pixel switch and realizing the display and refresh of a frame of image by matching with the data driving module. The pixel units in the liquid crystal display panel realize the display of different gray scales of images under the action of pixel gray scale voltage and scanning driving control signals. In an embodiment of the present invention, the panel is a 4K ultra high definition panel, the resolution is 3840 × 2160, the refresh rate is 360Hz, the bit width of the panel is 10bit, and the interface protocol of the panel is a data signal in a PHI point-to-point format. Because the invention is the scheme design of the field sequential display control system, the panel is a panel without a color filter, in one embodiment of the invention, the field frequency is 360Hz, the first primary color backlight is started in one field, and the first field image is refreshed at the same time; the second field starts the second primary color backlight and refreshes the second field image at the same time; and the third field starts a third primary color backlight, simultaneously refreshes a third field image, and finally realizes the display purpose and effect of the field sequential system through temporal color combination.
And the backlight driving module 4 is mainly used for receiving the backlight data driving signals of each subarea of each field, which are obtained by calculation of the data processing and control module 2, so that the driving current of the lamp beads of each field subarea is dynamically adjusted, and the adjustment and the output of the backlight brightness and the color of each subarea are realized. In an embodiment of the present invention, the FPGA processing and controlling module obtains the backlight primary color and brightness data signal containing field by field and partition by partition through a partition dimming algorithm, and the signal is a data signal in an SPI format. The backlight control system determines the number of backlight driving modules according to the number of backlight partitions and the number of channels of the backlight driving module 4, thereby realizing the output of the partition backlight brightness and color. The backlight driving module 4 is a multi-channel RGB Mini-LED driving circuit, and can output a plurality of paths of adjustable PWM signals to drive the backlight display module 5.
And the backlight display module 5 is mainly used for receiving the current output by the backlight driving module 4 and dynamically adjusting the brightness and color of the RGB Mini-LED lamp beads in each partition of the backlight module to realize backlight partition brightness display. The backlight display module 5 is a display module consisting of RGB Mini-LED lamp beads. The module receives the PWM signal output by the backlight driving module 4, so that the current of RGB Mini-LED lamp blocks in different partitions is adjusted, and the adjustment of the backlight brightness and the primary color of each partition is realized.
The invention also comprises an RGB Mini-LED backlight control method, which comprises two parts: and determining the optimal backlight partition number and adjusting the regional backlight. With reference to fig. 2, the RGB Mini-LED backlight control method includes the following steps:
step 1: under different experiment partition numbers and different types of pictures, utilizing a power meter to obtain corresponding power consumption data; testing the highest brightness and the lowest brightness of the corresponding pictures and the partitions through a brightness meter, and calculatingContrast data were obtained. In one embodiment of the present invention, eight types of partitions, 1 × 1,8 × 8, 16 × 16, 24 × 24, 32 × 32, 40 × 40, 50 × 50, and 64 × 64, are selected as the number of samples of the partition, and are denoted by Ni，i∈[1,8]. The invention selects picture samples containing different saturation, chroma and brightness types, and totally 100 natural images. The picture samples substantially cover the types of pictures that are usually displayed. The backlight power consumption in these eight partition types is measured by a power meter for each specific picture and is denoted as Pj，j∈[1,800]. According to one embodiment of the invention, under a specific picture, according to the size of the backlight partition, the input image is grayed to obtain NiMaximum value G of pixel gradation of the kth divisionmax(Ni,k) And minimum value Gmin(Ni,k) While determining the backlight brightness value BL (N) of the partitioni,k). Setting the whole backlight brightness as the backlight brightness of the partition, and respectively measuring the display brightness BD of the maximum gray scale and the minimum gray scale of the pixelmax(Ni,k) And BDmin(Ni,k) Repeating the above method, and obtaining the picture in N by statisticsiMaximum and minimum brightness BD for each partitionmax(Ni) And BDmax(Ni) And calculating the picture at NiContrast ratio at CR = BDmax(Ni)/ BDmax(Ni) And recording the image contrast under eight partition types, and recording as CRj,j∈[1,800]And synthesizing the data obtained by the measurement to obtain a sample set.
Step 2: (1) by the number of partitions NiAnd the RGB value of the original image is used as model input, and the power consumption P obtained in the step 1 is usedjAnd contrast data CRjAs model output, establishing convolutional neural network deep learning algorithm models PCNN and CCNN; in an embodiment of the present invention, 80% of the sample set obtained in step 1 is used as a training set, 20% is used as a testing set, and then all sample pictures are preprocessed to complete the picture size unification.
(2) And constructing a convolutional neural network deep learning algorithm model. The deep learning model is configured by a convolutional neural network (as shown in fig. 3), and performs a downsampling operation on the sample image to extract image data. As shown in fig. 3, in one embodiment of the present invention, the convolution kernel size of the L1 convolutional layer is 5 × 5, the convolution kernel size of the L2A convolutional layer is 3 × 1, the convolution kernel size of the L2B convolutional layer is 1 × 3, the convolution kernel size of the L3A convolutional layer is 1 × 3, and the convolution kernel size of the L3B convolutional layer is 3 × 1. The stimulus function is ReLU, the pooling layer is maximum pooling, the window size is 5 × 5, and the Dropout layer randomly discards 25% of the neurons, i.e. the parameter is set to 0.25. The L2 convolutional layer and the L3 convolutional layer adopt 1 x 3 and 3 x 1 symmetric convolution filters by decomposing convolution kernels, so that the characteristic dimension is reduced, and the network learning capability can be effectively improved.
(3) Initializing all parameters of the convolutional neural network deep learning model, setting model training end conditions, and completing the training process of the deep learning model by using training samples which account for 80% of a sample set; training procedure is based on the number of partitions NiAnd the RGB value of the original image is used as the input of the deep learning model, and the power consumption P obtained in the step 1 is usedjAnd contrast data CRjAnd continuously adjusting all parameters of the deep learning model as the output of the deep learning model until a training termination condition (namely, the maximum iteration number) is reached.
(4) And inputting the test samples accounting for 20% of the sample set into the trained deep learning model to obtain the test output corresponding to the test samples, namely the power consumption and the contrast of the test samples. The 20% of the sample set tested comprises a selected partial partition NiAnd natural images and their corresponding power consumption and contrast data. The model output power consumption and contrast data is then compared to the contrast CR in the test sample setjAnd power consumption PjComparing, evaluating the quality of the model by calculating the result size of the loss function, and adjusting the super parameters of the model to determine a final model function;
the loss function is a mean square loss function:
Figure 552883DEST_PATH_IMAGE001
where x and y are the model output of the loss function and the output of the corresponding sample.
And step 3: and acquiring the space geometric distance between the color coordinates of the RGB primary colors of each partition and the color coordinates of the target pixel points in the partition, wherein the distance considers the influence of the difference between the target points and the RGB primary colors under the white picture on color splitting and is recorded as an influence factor delta. In one embodiment of the present invention, assuming N =3 as the RGB primary color coordinates, i e [1, N ], as the target pixel color coordinates, δ is calculated as follows:
Figure 407707DEST_PATH_IMAGE002
the formula only considers the color splitting severity of the white picture, and the smaller the delta value, the lower the color splitting degree.
And 4, step 4: and calculating the distance between the color coordinates of the target pixel points and the color coordinates of the white points in the subareas, wherein the distance takes the influence factors of the colors of the target pixel points on color splitting into consideration and is recorded as the influence factor epsilon. In one embodiment of the present invention,
Figure 374395DEST_PATH_IMAGE003
as target pixel color coordinates, white point color coordinates of
Figure 707287DEST_PATH_IMAGE004
Then, the formula for calculating the influence factor epsilon is:
Figure 708610DEST_PATH_IMAGE005
and when the coordinates of the target pixel point are white, the epsilon value is 1. Therefore, the value can better reflect the influence of the color of the pixel point display color image on color splitting.
And 5: and calculating the difference value between the brightness of the target pixel point in the partition and the maximum brightness of the adjacent pixel points to obtain the contrast of the adjacent pixels, and recording the contrast as an influence factor zeta. In one embodiment of the present invention,L 0 is the brightness of the target pixel point and,L b the background brightness of the target pixel point is obtained. Said objectThe background brightness of the pixel point is the maximum brightness of eight adjacent pixel points which take the pixel point as the center. The impact factor ζ is:
Figure 531072DEST_PATH_IMAGE006
this value represents the effect of background content contrast on the color breakup phenomenon.
Step 6: and (5) synthesizing the products of the influence factors of the calculation results of the steps (3), (4) and (5) to obtain the color splitting index value of the target pixel point. In an embodiment of the present invention, since the severity of the color splitting phenomenon of the natural picture is related to the distribution of the primary colors of the subfields and the color of the displayed image, and the contrast between the pixel brightness and the ambient brightness, the three influence factors are considered comprehensively, so as to determine the color splitting index value MOS of the target pixel as:
Figure 798106DEST_PATH_IMAGE007
。
and 7: and (4) taking the partition number and the image pixel three-channel RGB value as model input, and taking the color splitting index value data obtained in the step (6) as model output, so as to establish a CBCNN convolutional neural network deep learning algorithm model. In an embodiment of the present invention, the model input is consistent with the model input in step 1, and the average of the features of all the pixels of the designated image in different partitions is used as the MOS characterization value of the picture in the partition, which is referred to as MOSj，j∈[1,800]And taking the value as an output value of the CBCNN convolutional neural network deep learning algorithm model. And (3) obtaining a final model function by adopting the model establishing and training mode in the step (2).
And 8: setting the partition-power consumption model function obtained according to the step 2 as F1(x) The partition-contrast model function is set to F2(x) And setting the partition-color splitting index model function partition obtained in the step 7 as F3(x) And x is the number of partitions.
And step 9: the different partitions are characterized (in one embodiment of the invention)Eight types of partitions with the sample number of 1 × 1,8 × 8, 16 × 16, 24 × 24, 32 × 32, 40 × 40, 50 × 50, and 64 × 64 are selected and recorded as Ni, i ∈ [1,8 ]]) And establishing a partition objective function F (x) = kF according to the characteristic values of power consumption, contrast, color splitting and cost of all the different types of pictures1(x)+mF2(x)+ qF3(x) + nP (x), wherein k, m, n and q are correction compensation coefficients, P (x) is a partition-cost function, and finally, the optimal partition value is synthesized through F (x) differential extreme point research; the partition-cost function P (x) is mainly related to different partitions NiThe system complexity, cost, etc. of the backlight driving scheme are positively correlated. In an embodiment of the present invention, after the partition backlight driving scheme is determined, key factors such as system complexity and cost can be determined, so as to obtain an optimal backlight partition value, i.e. an optimal backlight partition number, according to the above steps.
Step 10: partitioning the image according to the optimal backlight partition number sxt, extracting RGB three-channel gray-scale values of each pixel in the partition, determining color coordinates of all pixels in the partition according to the RGB values of the partition image pixels, acquiring reasonable color coordinates of new three primary colors through a clustering mode, and obtaining each primary color tristimulus value of the new three primary colors through coordinate system conversion, thereby determining the three primary colors under the partition as Qi1、Qi2、Qi3Where i ∈ [1, x ]]X is the number of partitions; in an embodiment of the invention, the image is partitioned according to the number of partitions, in the ith partition, the color coordinates of all pixels under the partition can be obtained according to the RGB values of the image pixels in the partition, all the pixels are clustered by adopting a 95% normal distribution confidence interval, and three vertexes of a minimum triangle containing the color coordinates of all the clustered pixels are used as the color coordinates of new three primary colors. Obtaining tristimulus values under an XYZ coordinate system through CIE 1931 XYZ coordinate conversion aiming at three primary color coordinates respectively, obtaining the tristimulus values under the RGB coordinate system through CIE 1931 RGB coordinate conversion, and obtaining the tristimulus values under the RGB coordinate system through a color combination formula:
Figure 997006DEST_PATH_IMAGE008
. Thereby obtaining three corresponding topsThe new three primary colors of the color coordinates of the dots, denoted as Qi1、Qi2、Qi3Where i ∈ [1, x ]]And x is the number of partitions, and the primary color adjustment of the RGB Mini-LED backlight which is partition by partition field by field can be realized in the mode.
Step 11: and graying the subarea image, and taking a gray-scale value as a brightness value. And selecting a traditional brightness extraction scheme to obtain a brightness matrix of the image of each scheme, and selecting an optimal brightness matrix of the image. And establishing a backlight brightness convolution neural network model (BLCNN) by taking the gray-scale image as input and the optimal brightness matrix as output, and finally obtaining an image backlight target brightness matrix under all partitions of the whole image based on the BLCNN. In an embodiment of the invention, three-channel RGB values of different types of picture pixels are used as model input, five luminance matrixes are obtained by a maximum value method, a root mean square method, an average value method, a signal-to-noise ratio method and a CDF method respectively for each picture, and an optimal luminance matrix is obtained according to a subjective judgment mode and is used as model output (for example, 30 volunteers in different years are randomly selected for voting, and the backlight which has the most votes in a multi-person observation display effect is selected as the optimal backlight and is used as the optimal luminance matrix of the current picture). The number of the input picture samples is the number of the pictures in the 800 different partitions described in the step 1. And (3) establishing a CBCNN deep learning algorithm model in the training mode of the step (2), wherein the deep learning model is formed by adopting a convolutional neural network, and performing downsampling operation on the sample image to extract image data. As shown in fig. 4, the convolution kernel size of the convolutional layer is 3 × 3, the excitation function is ReLU, the pooling layer uses a maximum pooling approach, the window size is 4 × 4, and the Dropout layer randomly discards 30% of the neurons, i.e., the parameter is set to 0.3.
And finally obtaining an image backlight target brightness matrix under all the partitions of the whole image based on the BLCNN.
Step 12: and calculating the proportion of the luminous flux of each channel of the three primary colors according to the color coordinates of the three primary colors, and obtaining new brightness of the three primary colors field by field through the target brightness after the light mixing of the partitions. In one embodiment of the present invention, the new three primary colors Q are seti1、Qi2、Qi3Respectively have color coordinates of (x)1,y1），（x2,y2）,（x3,y3) Where i ∈ [1, x ]]And x is the number of partitions. Let the new three primary colors be equal excitation, i.e. the luminous flux of the three primary colors is the same and is 1lm, Y1, Y2, Y3The luminous flux proportion of the new three primary colors is obtained by the following calculation method:
Figure 603568DEST_PATH_IMAGE009
according to the obtained luminous flux proportion and the target brightness matrix obtained in the step 11, the brightness of the new three primary colors of each field and each subarea can be obtained, and the adjustment of the backlight brightness of the primary colors RGB Mini-LED of each field and each subarea can be realized in such a way.
Step 13: performing backlight fuzzy simulation on the original backlight brightness matrix by adopting a backlight fuzzy function to obtain real backlight brightness distribution; the method comprises the following specific steps:
1) performing two-dimensional convolution on the backlight brightness matrix in the step 11;
2) performing diffusion amplification on the convolved backlight brightness matrix by using a diffusion template with a fixed size; in one embodiment of the present invention, the template is 3 × 3 in size;
3) and repeating the step 1) and the step 2) for N times, and performing bilinear interpolation to the original image size to obtain the real backlight brightness distribution, wherein N belongs to [3,10 ]. In one embodiment of the present invention, N =5, since the number of partitions is s × t, after repeating 5 times, the backlight size is 32s × 32t
4) And finally, obtaining the backlight resolution of 3840 × 2160 by a bilinear interpolation scheme, wherein the resolution is consistent with the resolution of the original image.
Step 14: by BL, based on the principle that the image is not distorted before and after dimmingr*LCr=BLf*LCfObtaining a gray-scale compensation value LC of the pixel after dimming by taking the influence of the nonlinear compensation GAMMA into considerationf. Wherein BLrFor the brightness of the backlight before dimming, LCrFor the pixel gray scale before dimming, BLfIs the step ofAnd 13, the real backlight distribution obtained in the step 13, namely the backlight brightness after dimming, realizes the driving output of the pixel gray-scale compensation signal. In one embodiment of the present invention,γthe value is 2.2 to satisfy the correction value of the display device standard. Setting NiEach of the partitions of (a), (b)s,t) Each local area of (a)p,q) The sub-pixel has an original gray scale ofR org (p, q),G org (p,q),B org (p,q)The corrected gray level of the sub-pixel isR final (p,q),G final (p,q),B final (p,q)The following relationship is satisfied:
R final (p,q)= R org (p,q) ×（255/BL f (p,q)） 1/γ
G final (p,q)= G org (p,q) ×（255/BL f (p,q)） 1/γ
B final (p,q)= B org (p,q) ×（255/BL f (p,q)） 1/γ
finally, the brightness of the regional image primary color backlight is adjusted by matching with the backlight of different primary colors under three fields, and the image display under the final field sequence scheme is realized through convolution of the real backlight distribution and the image pixel gray scale.
The foregoing is directed to embodiments of the present invention and, more particularly, to a method and apparatus for controlling a power converter in a power converter, including a power converter, a display and a display panel.

Claims (9)

1. The RGB Mini-LED field sequence backlight control system is characterized by comprising a data processing and control module, a backlight synchronization control module and a data processing and control module, wherein the data processing and control module is used for realizing conversion of data formats, processing and realization of algorithms and synchronous control of backlight:
the data processing module and the control module comprise a backlight partition processing unit; the backlight partition processing unit adjusts the color and the brightness of each backlight partition unit by a partition backlight adjusting method, and outputs the adjusted backlight driving signal to the backlight driving module to realize the control of the backlight partition brightness and the color; the backlight partition processing unit realizes the synchronization of the refreshing of the backlight primary colors and the field frequency, thereby ensuring the synchronization of the refreshing frequency and the field frequency of the backlight primary colors of each partition, and further ensuring the synchronization of the display backlight control signals and the data signals of the field sequence color mixing.
2. The RGB Mini-LED field sequential backlight control system according to claim 1, wherein the data processing module and the control module further comprise a data encoding/decoding unit; the unit converts the data signal received by the video signal input module into a screen end driving signal and outputs the screen end driving signal to the panel driving display module.
3. The RGB Mini-LED field sequential backlight control system of claim 1, wherein the backlight driving signal is an SPI signal containing the luminance and color information of the subarea image.
4. The RGB Mini-LED field sequential backlight control system according to claim 3, wherein the color information is new three primary colors formed by combining RGB three primary colors according to different component ratios, and the new three primary colors are different in different sub-fields.
5. The RGB Mini-LED field sequential backlight control system of claim 1, further comprising:
the video signal input module is used for receiving external video or image signals and inputting the received signals to the data processing and control module;
the storage module is used for caching the frame data of the input video stream and performing algorithm processing on the backlight information of the extracted image data by the data processing and controlling module;
the panel driving display module comprises a data driving unit, a grid driving unit and a liquid crystal display panel and is used for driving the image display of the panel;
the backlight driving module is used for receiving the backlight data driving signals which are calculated by the data processing and control module and are divided field by field, so that the driving current of the corresponding lamp beads is dynamically adjusted, the color and the brightness information of the primary colors of the sub-field division are changed, and the self-adaptive adjustment and the output of the backlight brightness and the color of each division are finally realized;
and the backlight display module is used for receiving the current output by the backlight driving module, dynamically adjusting the brightness of the RGB Mini-LED lamp beads in each partition of the backlight module and realizing the display of the brightness and the color of the backlight partition.
6. The RGB Mini-LED field sequential backlight control system of claim 5, wherein the backlight driver module is an LED Dirver module receiving multiple backlight driving signals.
7. The RGB Mini-LED field-sequential backlight control system of claim 1, wherein the backlight adjustment method specifically comprises:
step 1: under different experiment partition numbers and different types of pictures, utilizing a power meter to obtain corresponding power consumption data; measuring by a brightness meter to obtain the highest brightness and the lowest brightness of the corresponding picture and the corresponding partition, and calculating to obtain contrast data;
step 2: taking the partition number and the three-channel RGB value of the picture pixel as model input, taking the power consumption and contrast data obtained in the step 1 as model output, and respectively establishing a PCNN (pulse coupled neural network) and CCNN (pulse coupled neural network) convolutional neural network deep learning algorithm model;
and step 3: acquiring the space geometric distance between the color coordinates of the RGB primary colors of each partition and the color coordinates of the target pixel points in the partition, wherein the distance considers the influence of the difference between the target points and the RGB primary colors under the white picture on color splitting and is recorded as an influence factor delta;
and 4, step 4: calculating the distance between the color coordinates of the target pixel points and the color coordinates of the white points in the subareas, wherein the distance takes the influence factors of the colors of the target pixel points on color splitting into consideration and is recorded as an influence factor epsilon;
and 5: calculating the difference value between the brightness of the target pixel point in the partition and the maximum brightness value of the adjacent pixel points to obtain the contrast of the adjacent pixels, and recording the contrast as an influence factor zeta;
step 6: synthesizing the products of the influence factors of the calculation results of the steps 3, 4 and 5 to obtain the color splitting index value of the target pixel point;
and 7: taking the partition number and the image pixel three-channel RGB value as model input, taking the color splitting index value data obtained in the step 6 as model output, and establishing a CBCNN convolutional neural network deep learning algorithm model;
and 8: setting the partition-power consumption model function obtained according to the step 2 as F1(x) The partition-contrast model function is set to F2(x) And setting the partition-color splitting index model function partition obtained in the step 7 as F3(x) X is the number of partitions;
and step 9: obtaining characteristic values representing power consumption, contrast, color splitting and cost of all different types of pictures in different partitions, establishing a partition target function according to the three characteristic values, and taking an extreme point of the target function as the optimal backlight partition number;
step 10: partitioning the image according to the optimal backlight partition number, extracting RGB three-channel gray-scale values of each pixel in the partition, determining color coordinates of all pixels in the partition according to the RGB values of the pixels of the partitioned image, and acquiring reasonable color coordinates of new three primary colors in a clustering mode;
step 11: graying the partitioned image, selecting an optimal brightness matrix of the image by taking a gray value as a brightness value, and constructing a deep learning model to obtain an image backlight target brightness matrix of all partitions of the whole image; the optimal brightness matrix acquisition method comprises the following steps: respectively adopting a maximum value method, an average method, a CDF method and a peak signal-to-noise ratio method to respectively extract backlight brightness of the input gray-scale image to obtain different backlight brightness matrixes, and selecting the backlight brightness matrix with the best display effect as an optimal brightness matrix through a subjective evaluation method;
step 12: calculating the proportion of the luminous flux of each channel of the three primary colors according to the color coordinates of the three primary colors, and obtaining the brightness of the new three primary colors field by field through the target brightness after the light mixing of the subareas;
step 13: performing backlight fuzzy simulation on the original backlight brightness matrix by adopting a backlight fuzzy function to obtain real backlight brightness distribution;
step 14: and obtaining the gray scale compensation value of the pixel after dimming by the equal product of the light transmittance of the image before and after dimming and the backlight brightness.
8. A RGB Mini-LED field sequence backlight control method is characterized by comprising the following steps:
step 1: obtaining a sample set: under different experiment partition numbers and different types of pictures, utilizing a power meter to obtain corresponding power consumption data; measuring by a brightness meter to obtain the highest brightness and the lowest brightness of the corresponding picture and the corresponding partition, and calculating to obtain contrast data;
step 2: taking the partition number and the three-channel RGB value of the picture pixel as model input, taking the power consumption and contrast data obtained in the step 1 as model output, and respectively establishing a PCNN (pulse coupled neural network) and CCNN (pulse coupled neural network) convolutional neural network deep learning algorithm model;
and step 3: acquiring the space geometric distance between the color coordinates of the RGB primary colors of each partition and the color coordinates of the target pixel points in the partition, wherein the distance considers the influence of the difference between the target points and the RGB primary colors under the white picture on color splitting and is recorded as an influence factor delta;
and 4, step 4: calculating the distance between the color coordinates of the target pixel points and the color coordinates of the white points in the subareas, wherein the distance takes the influence factors of the colors of the target pixel points on color splitting into consideration and is recorded as an influence factor epsilon;
and 5: calculating the difference value between the brightness of the target pixel point in the partition and the maximum brightness value of the adjacent pixel points to obtain the contrast of the adjacent pixels, and recording the contrast as an influence factor zeta;
step 6: synthesizing the products of the influence factors of the calculation results of the steps 3, 4 and 5 to obtain the color splitting index value of the target pixel point;
and 7: taking the partition number and the image pixel three-channel RGB value as model input, taking the color splitting index value data obtained in the step 6 as model output, and establishing a CBCNN convolutional neural network deep learning algorithm model;
and 8: setting the partition-power consumption model function obtained according to the step 2 as F1(x) The partition-contrast model function is set to F2(x) And setting the partition-color splitting index model function partition obtained in the step 7 as F3(x) X is the number of partitions;
and step 9: obtaining characteristic values representing power consumption, contrast, color splitting and cost of all different types of pictures in different partitions, establishing a partition target function according to the three characteristic values, and taking an extreme point of the target function as the optimal backlight partition number;
step 10: partitioning the image according to the optimal backlight partition number, extracting RGB three-channel gray-scale values of each pixel in the partition, determining color coordinates of all pixels in the partition according to the RGB values of the pixels of the partitioned image, and acquiring reasonable color coordinates of new three primary colors in a clustering mode;
step 11: graying the partitioned image, selecting an optimal brightness matrix of the image by taking a gray value as a brightness value, and constructing a deep learning model to obtain an image backlight target brightness matrix of all partitions of the whole image; the optimal brightness matrix acquisition method comprises the following steps: respectively adopting a maximum value method, an average method, a CDF method and a peak signal-to-noise ratio method to respectively extract backlight brightness of the input gray-scale image to obtain different backlight brightness matrixes, and selecting the backlight brightness matrix with the best display effect as an optimal brightness matrix through a subjective evaluation method;
step 12: calculating the proportion of the luminous flux of each channel of the three primary colors according to the color coordinates of the three primary colors, and obtaining the brightness of the new three primary colors field by field through the target brightness after the light mixing of the subareas;
step 13: performing backlight fuzzy simulation on the original backlight brightness matrix by adopting a backlight fuzzy function to obtain real backlight brightness distribution;
step 14: and obtaining the gray scale compensation value of the pixel after dimming by the equal product of the light transmittance of the image before and after dimming and the backlight brightness.
9. The RGB Mini-LED field sequential backlight control method according to claim 8, wherein in the step 2, specifically:
(1) selecting a training set and a testing set from the sample set obtained in the step 1, and then preprocessing all sample pictures to finish the picture size unification;
(2) establishing convolutional neural network deep learning algorithm models PCNN and CCNN; the deep learning algorithm model is formed by a convolutional neural network, and performs downsampling operation on the sample image to extract image data;
(3) initializing all parameters of the convolutional neural network deep learning model, setting model training end conditions, and completing the training process of the deep learning model by using training samples which account for 80% of a sample set; in the training process, the number of the partitions and the RGB value of the original image are used as the input of the deep learning model, the power consumption and contrast data obtained in the step 1 are used as the output of the deep learning model, and all parameters of the deep learning model are continuously adjusted until the training termination condition is reached;
(4) inputting the test set sample into the trained deep learning model to obtain test output corresponding to the test sample, wherein the test output comprises power consumption and contrast; and then comparing the output power consumption and the contrast data of the model with the contrast and the power consumption of the test sample set, evaluating the quality of the model by calculating the result size of the loss function, and carrying out model hyper-parameter adjustment to determine a final model function.
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