Liquid crystal display device, method for controlling a liquid crystal display device, and program

Abstract

This technology provides controllable luminescence of the backlight module to achieve both high video visibility and reduced flicker. [Solution] The liquid crystal display device of the present invention comprises a liquid crystal panel, an input means for inputting data of a first image, a backlight module capable of changing the luminous brightness for each of a plurality of divided regions, a generation means for inputting data generated from the first image and a stored backlight control value into a neural network to generate the luminous brightness of each divided region of the backlight module for each subframe, a correction means for correcting the first image to a second image based on the generated luminous brightness, a backlight control means for controlling the luminous brightness of each divided region of the backlight module based on the generated luminous brightness, and a liquid crystal panel means for controlling the transmittance of the liquid crystal panel based on data of the second image.

Description

Technical Field

[0001] The present invention relates to a liquid crystal display device capable of changing the emission luminance of a backlight module.

Background Art

[0002] There is a demand for increasing the contrast of display devices that display images having a relatively wide dynamic range, such as HDR (High Dynamic Range) images. Representative display devices include liquid crystal display devices (LCD (Liquid Crystal Display) devices). In an LCD device, a liquid crystal panel adjusts the amount of transmitted light of the light irradiated from a backlight module for each pixel. In an LCD device, since the light irradiated from the backlight module cannot be completely blocked, black floating occurs due to light leakage.

[0003] When reducing black floating and improving the contrast in an LCD device, for example, a technique called local dimming is used. Local dimming is a technique for reducing black floating by controlling the emission luminance of the backlight module for each divided region. In conventional local dimming, there is a method of applying a specific rule to the control value of the backlight module for each divided region so that luminance deficiency does not occur in a bright portion region with a small area.

[0004] In addition, when a display device with hold-type light emission such as a liquid crystal display device displays a moving image, the moving object may be displayed as if it has a trailing (hereinafter referred to as video blur). To improve the video blur of this liquid crystal display device, there is a method of performing BL scan (black insertion) in which the LED lighting of the backlight (hereinafter referred to as BL) is sequentially scanned from the top to the bottom of the panel to perform impulse-type light emission. Patent Document 1 discloses a technique of performing local dimming while performing BL scan in units of sub-frames.

Prior Art Documents

Patent Documents

[0005] [Patent Document 1] Japanese Patent Publication No. 2022-68750 [Overview of the project] [Problems that the invention aims to solve]

[0006] However, while the method disclosed in Patent Document 1 discloses a backlight scan that prevents flicker, it has the problem that it cannot perform backlight scan control to improve video responsiveness.

[0007] Therefore, the present invention aims to achieve both high video visibility and reduced flicker in a liquid crystal display device. [Means for solving the problem]

[0008] The liquid crystal display device comprises a liquid crystal panel, an input means for inputting data of a first image, a backlight module that irradiates the liquid crystal panel with light and whose luminous intensity can be changed for each of several divided regions, a generation means for generating the luminous intensity of each divided region of the backlight module for each subframe, a calculation means for calculating a correction value for correcting the first image based on the luminous intensity generated by the generation means, a holding means for holding the luminous intensity generated by the generation means, a correction means for correcting the first image to a second image based on the correction value calculated by the calculation means, a backlight control means for controlling the luminous intensity of each divided region of the backlight module based on the luminous intensity generated by the generation means, and a liquid crystal panel means for controlling the transmittance of the liquid crystal panel based on data of the second image, wherein the generation means inputs the data generated from the first image and the luminous intensity held by the holding means into a neural network to generate luminous intensity. [Effects of the Invention]

[0009] According to the present invention, it is possible to achieve both high video visibility and reduced flicker in a liquid crystal display device. [Brief explanation of the drawing]

[0010] [Figure 1] This is a block diagram showing the functional blocks of a liquid crystal display device according to Example 1. [Figure 2] This figure shows an example of a divided region of the backlight according to Example 1. [Figure 3] This figure shows an example of a backlight brightness estimation point according to Example 1. [Figure 4] This is a block diagram showing the functional blocks of the model learning unit according to Example 1. [Figure 5] This is a block diagram showing the functional block of the LD feature generation unit according to Example 1. [Modes for carrying out the invention]

[0011] Embodiments of the present invention will be described below with reference to the drawings. The technical scope of the present invention is defined by the claims and is not limited by the embodiments illustrated below. Furthermore, not all combinations of features described in the embodiments are essential to the present invention. The contents described in this specification and drawings are illustrative and should not be considered limiting to the present invention. Various modifications are possible based on the spirit of the present invention, and these do not exclude them from the scope of the present invention. That is, all configurations combining each embodiment and its modified forms are included in the present invention.

[0012] [Example 1] The following describes Embodiment 1 of the present invention. Figure 1 is a block diagram showing the functional blocks of the liquid crystal display device 100 according to Embodiment 1. The liquid crystal display device 100 includes an image input / conversion unit 101, a local dimming control unit 102, a liquid crystal panel control unit 103, a liquid crystal panel 104, a backlight control unit 105, and a backlight module 106.

[0013] The image input / transformation unit 101 acquires image data (data of an image) from the outside. Specifically, the image input / transformation unit 101 has an input interface such as SDI (Serial Digital Interface), and inputs the image data into the liquid crystal display device 100 from the outside via the input interface. Then, the image input / transformation unit 101 performs transformation processes such as gradation transformation and signal format transformation on the acquired (input) image data, and outputs the image data after the transformation process as an input image.

[0014] The gradation transformation is, for example, a gradation transformation using a one-dimensional look-up table (1D-LUT), and is a gradation transformation according to the gamma value (panel gamma) of the liquid crystal panel 104. Here, consider the case where the gamma characteristic (corresponding relationship between gradation value and luminance; gradation characteristic) of the image data acquired from the outside is a linear characteristic in which the luminance increases linearly with the increase in the gradation value, and the panel gamma is 2.0. In this case, a gradation transformation using the inverse gamma of the panel gamma (that is, 1 / 2.0) is performed. Thereby, the acquired image data (image data having a linear characteristic) is converted into image data having a gamma specification in which the luminance is proportional to the 1 / 2.0 power of the gradation value. Note that the transformation process in the image input / transformation unit 101 is not limited to the gradation transformation using a 1D-LUT, and may include a transformation process using a three-dimensional look-up table (3D-LUT), gain adjustment, offset adjustment, matrix transformation, and the like.

[0015] The signal format transformation is, for example, a process of converting the signal format of the image data from YCbCr, XYZ, etc. to RGB. Note that the signal formats before and after the transformation are not limited to YCbCr, XYZ, and RGB.

[0016] The local dimming control unit 102 is composed of a preprocessing unit 10201, a learned model holding unit 10202, a backlight control value generation unit 10203, a backlight luminance estimation unit 10204, a correction coefficient generation unit 10205, an image correction unit 10206, and a backlight control value storage unit 10207.

[0017] The preprocessing unit 10201 generates feature amounts (image feature amounts) based on the input image output from the image input / transformation unit 101. In the first embodiment, as shown in FIG. 2, the preprocessing unit 10201 generates image feature amounts by calculating the maximum gradation value and the average gradation value of the input image corresponding to a plurality of divided regions. That is, when the number of horizontal divisions of the backlight module 106 is n and the number of vertical divisions is m, the preprocessing unit 10201 generates two-dimensional data of n×m as the image feature amount. Then, the preprocessing unit 10201 outputs the generated image feature amount to the backlight control value generation unit 10203.

[0018] Generally, the video signal is input in time series from the upper part to the lower part of the screen. Therefore, the timing at which the image feature amount can be calculated is different for each divided region. Therefore, the image feature amount is output to the backlight control value generation unit 10203 at the timing calculated for each divided region.

[0019] The learned model holding unit 10202 holds a model (learned model) learned to convert the image feature amount and the backlight control value output in the past into the backlight control value. The backlight control value is a control value for controlling the backlight module 106. The learned model is a group of parameters applied to a neural network that converts the image feature amount and the backlight control value output in the past into the backlight control value, and includes the weights and biases between neurons constituting the neural network. Note that the learned model held by the learned model holding unit 10202 may be one that converts the input image into the backlight control value. The learned model held by the learned model holding unit 10202 is applied to the backlight control value generation unit 10203.

[0020] The backlight control value generation unit 10203 generates a backlight control value based on the image features output from the preprocessing unit 10201 and previously output backlight control values. The backlight control value is a control value for controlling the luminescence brightness of the backlight module 106 via the backlight control unit 105. As shown in Figure 2, the backlight module 106 has multiple divided regions that constitute the display surface, each of which has multiple light sources, and the luminescence brightness can be changed for each divided region. The light sources of the backlight module 106 are not particularly limited, but for example, they are LEDs (Light Emitting Diodes). The backlight control value generation unit 10203 generates a backlight control value for each divided region. The backlight control value generation unit 10203 then outputs the backlight control value to the backlight brightness estimation unit 10204 and the backlight control unit 105.

[0021] This example demonstrates how to generate backlight control values ​​by dividing the vertical synchronization period of the input video signal into multiple subframe periods. When the vertical synchronization period is divided into four periods, the image feature quantities input from the preprocessor 10201 are gradually updated from the top of the screen. In Figure 2, the divided area is divided into 12 regions vertically, so data equivalent to 3 regions vertically and 20 regions horizontally is updated with each subframe.

[0022] The backlight control value generation unit 10203 generates backlight control values ​​using data updated with image features input from the preprocessing unit 10201 for each subframe period.

[0023] In Example 1, the backlight control value generation unit 10203 is composed of at least a neural network that takes image features and previously output backlight control values ​​as input and outputs backlight control values. When the horizontal division of the backlight module 106 is n and the vertical division is m, the neural network of the backlight control value generation unit 10203 has a structure that inputs and outputs n × m two-dimensional data. Furthermore, the neural network of the backlight control value generation unit 10203 has a structure to which a trained model held by the trained model holding unit 10202 can be applied. Note that the network structure of the neural network of the backlight control value generation unit 10203 is not limited as long as it can generate backlight control values. In addition, the input to the neural network of the backlight control value generation unit 10203 is not limited to image features and previously output backlight control values, but may also be an input image and previously output backlight control values.

[0024] The backlight control value generation unit 10203 may include a configuration that generates backlight control values ​​based on specific rules in addition to a neural network. Furthermore, the backlight control value generation unit 10203 may include multiple neural networks or specific rules for generating backlight control values. In this case, the backlight control value generation unit 10203 can switch between neural networks or specific rules.

[0025] The backlight brightness estimation unit 10204 performs estimation calculations of the brightness of the light (backlight brightness) irradiated from the backlight module 106 onto the liquid crystal panel 104, based on the backlight control value output from the backlight control value generation unit 10203.

[0026] Since the backlight control values ​​output from the backlight control value generation unit 10203 are output for each subframe, the backlight brightness estimation calculation is performed after receiving the backlight control values ​​for one frame period.

[0027] The backlight brightness estimation calculation is performed by estimating (calculating) the backlight brightness based on the backlight control value of each light source (each divided region) and the brightness distribution model of the light emitted from the light source (the part of the backlight module 106 corresponding to the divided region). Here, the backlight brightness estimation unit 10203 estimates (calculates) the backlight brightness for each brightness estimation point discretely arranged within the display surface, as shown in Figure 3(A).

[0028] Specifically, the backlight control value B is determined according to the following formula (1). ij and weight W ij The backlight brightness L is calculated by performing a sum-of-products operation. In the following formula (1), n ​​represents the number of horizontal divisions of the backlight module, and m represents the number of vertical divisions of the backlight module. Also, B ij The backlight control values ​​are for vertical position i and horizontal position j, W ij This is the backlight control value B ij The weight applied, L, represents the backlight brightness at the brightness estimation point. The backlight lit in each divided region decreases in intensity as the distance increases. Therefore, the backlight control value B ij Weight W applied to it ij The closer the distance from the brightness estimation point, the greater the value.

number

[0029] In the example shown in Figure 3(A), one luminance estimation point is placed for one divided region of the backlight module 106, but this is not limited to this. For example, points may be placed at the four corners of the divided region as shown in Figure 3(B), or one point may be placed for each of the four divided regions as shown in Figure 3(C). The backlight luminance estimation unit 10204 interpolates the backlight luminance between the luminance estimation points and scales it to the resolution of the input image. Therefore, when luminance estimation points are placed as shown in Figure 3(B), the number of luminance estimation points increases compared to Figure 3(A), suppressing interpolation errors but increasing computational load. On the other hand, when luminance estimation points are placed as shown in Figure 3(C), the number of luminance estimation points decreases compared to Figure 3(A), suppressing computational load but increasing interpolation errors. Methods for scaling backlight luminance include, for example, bicubic interpolation and bilinear interpolation, but are not limited to these methods as long as they can be scaled to the resolution of the input image. The backlight luminance estimation unit 10204 then outputs the estimated backlight luminance to the correction coefficient generation unit 10205. Alternatively, the backlight brightness estimation unit 10204 may output discretely arranged backlight brightness values ​​to the correction coefficient generation unit 10205 without scaling the calculated backlight brightness, as shown in Figure 3(A). In this case, the correction coefficient generation unit 10205 scales the calculated correction coefficient to the resolution of the input image.

[0030] The correction coefficient generation unit 10205 generates a correction coefficient to be applied to the input image output from the image input / conversion unit 101, based on the backlight brightness output from the backlight brightness estimation unit 10203. In Example 1, the correction coefficient Gt is calculated using the reciprocal of the backlight brightness L (a value normalized to 0.0 to 1.0) according to the following formula (2). For example, if the backlight brightness is reduced to 1 / 3, the reduction in backlight brightness can be compensated for by the image by increasing the input image by its reciprocal, i.e., by 3 times. Note that the method for calculating the correction coefficient is not limited to the reciprocal of the backlight brightness.

number

[0031] The image correction unit 10206 generates (calculates) the pixel values ​​of the corrected image by multiplying the correction coefficient output from the correction coefficient generation unit 10205 by the pixel values ​​of the input image output from the image input / conversion unit 101. In Example 1, the RGB values ​​(Vrc, Vgc, Vbc), which are the pixel values ​​of the corrected image, are generated by multiplying the RGB values ​​(R value, G value, B value) = (Vr, Vg, Vb), which are the pixel values ​​of the input image, by the correction coefficient Gt, according to the following formulas (3) to (5). The image correction unit 10206 then outputs the generated corrected image. Vrc = Vr × Gt...(3) Vgc = Vg × Gt ... (4) Vbc = Vb × Gt ... (5)

[0032] The backlight control value storage unit 10207 is a memory that stores backlight control values ​​output from the backlight control value generation unit 10203 across multiple subframes. The stored backlight control values ​​are output to the backlight control value generation unit 10203. The stored backlight control values ​​are updated by the backlight module 106 for each subframe. The brightness emitted for each subframe corresponds to the amount of light incident on the human eye for each subframe, and therefore correlates with the video response to vision and flicker. For this reason, the longer the data period for which the backlight control values ​​are stored, the more possible inference becomes based on the amount of correlated data.

[0033] The liquid crystal panel control unit 103 controls the transmittance of the liquid crystal panel 104 (transmittance distribution within the display surface) based on the corrected image output from the image correction unit 10206 so that the image based on the corrected image is displayed on the liquid crystal panel 104.

[0034] The liquid crystal panel 104 is controlled by the liquid crystal panel control unit 103 and displays an image on its display surface.

[0035] The backlight control unit 105 controls the luminescence brightness of the backlight module 106 (the light source of the backlight module 106) according to the backlight control value output from the backlight control value generation unit 10203. For example, the backlight control unit 105 determines the duty cycle of PWM (Pulse Width Modulation) control according to the backlight control value, and controls the luminescence brightness of the backlight module 106 by PWM control at the determined duty cycle.

[0036] The backlight module 106 illuminates the back of the liquid crystal panel 104 with light. As described above, the luminescence brightness of the backlight module 106 is adjustable. In Embodiment 1, multiple divided regions constituting the display surface are pre-set, and the backlight module 106 has multiple light sources corresponding to each of the multiple divided regions, and the luminescence brightness can be changed for each divided region.

[0037] As explained with reference to Figure 1, in the liquid crystal display device 100 of Embodiment 1, the backlight module 106 can be controlled by applying a trained model to the neural network of the backlight control value generation unit 10203.

[0038] Figure 4 is a block diagram showing the functional blocks of the model learning unit 200 according to Embodiment 1. The model learning unit 200 includes an image input / conversion unit 201, a preprocessing unit 202, a backlight control value generation unit 203, a first setting unit 204, and an LD feature generation unit 205. The model learning unit 200 also includes a second setting unit 206, a target feature generation unit 207, a video response error calculation unit 208, a trained model output unit 209, a frame brightness storage unit 210, a flicker error calculation unit 211, and a backlight control value storage unit 212. The model learning unit 200 is, for example, a program that runs on a computer, but is not limited to that, and may also operate as a functional block constituting the liquid crystal display device 100.

[0039] The image input / conversion unit 201 acquires image data (image data) from an external source. Specifically, the image input / conversion unit 101 acquires image data from a group of files stored in the computer's memory, but is not limited to this; image data may also be input via an input interface such as SDI. The image input / conversion unit 101 can then perform the same conversion processing on the acquired (input) image data as the image input / conversion unit 101 of the liquid crystal display device 100, and outputs the converted image data as the input image. However, the image input / conversion unit 201 may also acquire (input) pre-converted image data. In this case, the image input / conversion unit 201 does not need to perform image data conversion processing.

[0040] The preprocessor 202 generates feature quantities (image feature quantities) based on the input image output from the image input / conversion unit 201. Specifically, the preprocessor 202 generates image feature quantities in the same way as the preprocessor 10201 of the liquid crystal display device 100. That is, if the number of horizontal divisions of the backlight module 106 of the liquid crystal display device 100 is n and the number of vertical divisions is m, the preprocessor 202 generates n × m two-dimensional data as image feature quantities. The preprocessor 202 then outputs the generated image feature quantities to the backlight control value generation unit 203, the LD feature quantity generation unit 205, the target feature quantity generation unit 207, and the video response error calculation unit 208, respectively.

[0041] Generally, video signals are input sequentially from the top to the bottom of the screen. Therefore, the timing at which image features can be calculated differs for each divided region. Accordingly, the image features are output to the backlight control value generation unit 203 at the timing at which they are calculated for each divided region.

[0042] The backlight control value generation unit 203 consists of a neural network that receives image features output from the preprocessing unit 202 and previously output backlight control values ​​as input and outputs backlight control values. The neural network of the backlight control value generation unit 203 has the same structure as the neural network of the backlight control value generation unit 10203 of the liquid crystal display device 100. That is, if the horizontal division number of the backlight module 106 of the liquid crystal display device 100 is n and the vertical division number is m, the neural network of the backlight control value generation unit 203 has a structure that inputs and outputs n × m two-dimensional data. For each subframe, the backlight control value generation unit 203 updates the image features used in the previous subframe with image features input from the preprocessing unit 201 and generates backlight control values.

[0043] The backlight control value generation unit 203 inputs the parameters generated by the video response error calculation unit 208 and the parameters generated by the flicker error calculation unit 211 into the neural network. This allows the neural network to reflect errors that take into account image features and previously output backlight control values ​​during training. In other words, it performs training that takes into account previously output backlight control values ​​that correlate with the video response and flicker that affect the viewer, based on the luminous intensity of past backlight control values.

[0044] The backlight control value generation unit 203 then outputs the backlight control values ​​generated by the neural network to the LD feature generation unit 205 and the backlight control value storage unit 212.

[0045] The first setting unit 204 outputs parameters to the LD feature generation unit 205. Specifically, the first setting unit 204 outputs the panel contrast and the luminance distribution model to the LD feature generation unit 205. The panel contrast is the contrast value of the liquid crystal panel 104 of the liquid crystal display device 100. The luminance distribution model is a model of the luminance distribution of light emitted from the light source of the liquid crystal display device 100 (the part of the backlight module 106 corresponding to the divided area). The parameters set by the first setting unit 204 are obtained from the computer's memory, but are not limited to this, and may also be input from an external source via an OSD (On Screen Display) menu or the like. Note that the parameters set by the first setting unit 204 to the LD feature generation unit 205 are not limited to the panel contrast and the luminance distribution model, but may also include other parameters necessary for the LD feature generation unit 205.

[0046] The LD feature generation unit 205 generates LD (Local Dimming) features for each subframe based on the image features output from the preprocessing unit 202 and the backlight control values ​​output from the backlight control value generation unit 203.

[0047] The LD features generated by the LD feature generation unit 205 are based on backlight control values ​​and image features that change with each subframe, and therefore represent image features that show how the input image changes and is displayed with each subframe.

[0048] In Example 1, the LD feature generation unit 205 generates LD features using parameters output from the first setting unit 204. The parameters output from the first setting unit 204 to the LD feature generation unit 205 are panel contrast and luminance distribution model, but are not limited to these; other parameters may be used, and external parameters may not be used at all. The LD features correspond to a simulated image of local dimming in the liquid crystal display device 100.

[0049] The LD feature generation unit 205 then outputs the generated LD features to the frame brightness storage unit 210 and the flicker error detection unit 211. Details of the processing of the LD feature generation unit 205 will be described later.

[0050] The second setting unit 206 outputs parameters to the target feature generation unit 207. Specifically, the second setting unit 206 outputs the target contrast to the target feature generation unit 207. The target contrast is a contrast value used to convert the image features output from the preprocessing unit 202 to the target contrast. The parameters set by the second setting unit 206 are obtained from the computer's memory, but are not limited to this; they may also be input externally via an OSD (On Screen Display) menu or the like. Note that the parameters set by the second setting unit 206 to the target feature generation unit 207 are not limited to the target contrast and may include other parameters necessary for the target feature generation unit 207.

[0051] The target feature generation unit 207 generates target features based on the image features output from the preprocessing unit 202. The target features correspond to the target display image when local dimming control is performed on the liquid crystal display device 100. In Embodiment 1, the target feature generation unit 207 generates target features by converting the image features into a target contrast output from the second setting unit 206. Specifically, the data value Vt of the target feature data is calculated by performing a gain / offset calculation on the image feature data value V based on the target contrast Ct, according to the following formula (6). The target contrast is obtained from the first setting unit 204. If the contrast ratio of the target contrast is 1,000,000:1, then 1,000,000 is entered for the target contrast Ct.

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[0052] In Example 1, the target feature generation unit 207 generates target features by converting image features to a target contrast, but is not limited to this; any target feature corresponding to the target display image when local dimming control is performed on the liquid crystal display device 100 is acceptable. For example, the target feature generation unit 207 may acquire pre-prepared target features from an external source. Alternatively, the target feature generation unit 207 may output the image features output from the preprocessing unit 202 as the target features. The target feature generation unit 207 then outputs the generated target features to the video response error calculation unit 208.

[0053] The video response error calculation unit 208 generates parameters for the neural network of the backlight control value generation unit 203 based on the difference between the LD feature quantity output from the frame brightness accumulation unit 210 and the target feature quantity output from the target feature quantity generation unit 207 for each frame. The difference between the LD feature quantity and the target feature quantity is, for example, L1 (sum of absolute differences). In this case, the video response error calculation unit 208 calculates the parameters for the neural network of the backlight control value generation unit 203 so as to minimize the difference between the LD feature quantity and the target feature quantity. The backlight control value generation unit 203 then outputs the generated parameters to the backlight control value generation unit 203. The parameters that reduce the error calculated by the frame brightness accumulation unit 210 are parameters that correct the error between the feature quantity that takes into account the response speed of the liquid crystal and the target feature quantity, making it possible to improve video responsiveness.

[0054] The method for generating parameters by the video response error calculation unit 208 is not limited to one based on the difference between the LD feature and the target feature, but may be based on other methods. For example, the video response error calculation unit 208 may generate parameters for the neural network of the backlight control value generation unit 203 based on the truth value determination of the LD feature and the target feature by the classifier. The classifier is composed of a neural network. In this case, the video response error calculation unit 208 is trained so that when a combination of image feature and target feature is input, the output value approaches 1 (true), and when a combination of image feature and LD feature is input, the output value approaches 0 (fake). Furthermore, the video response error calculation unit 208 calculates the parameters for the neural network of the backlight control value generation unit 203 so that the output value of the classifier when a combination of image feature and LD feature is input approaches 1 (true).

[0055] Furthermore, the method for generating parameters by the video response error calculation unit 208 may be a combination of multiple methods. For example, a method based on the difference between the LD feature and the target feature may be combined with a method based on the truth value determination of the LD feature and the target feature by a classifier.

[0056] The trained model output unit 209 outputs the parameters of the neural network of the backlight control value generation unit 203 to the outside as a trained model. For example, the trained model output unit 209 outputs the trained model to the trained model holding unit 10202 of the liquid crystal display device 100 via external memory or the like.

[0057] The frame brightness accumulation unit 210 is an accumulation unit that stores (accumulates) the LD feature quantities output from the LD feature quantity generation unit 205 for each subframe, and stores them as an image for one frame period. The LD feature quantity for one frame period is created by adding and storing the LD feature quantities input for each subframe. The stored LD feature quantity is output to the video response error calculation unit 208.

[0058] The flashing error calculation unit 211 generates parameters for the neural network of the backlight control value generation unit 203 for each subframe based on the difference between the LD features output from the LD feature generation unit 205 and the target features output from the target feature generation unit 207.

[0059] The difference between the LD features and the target features is calculated for each subframe, and therefore represents the error in the amount of brightness perceived by the eye for each subframe. By reducing this error, the image will appear as if it were the target features, thus reducing flicker caused by the backlight flickering.

[0060] Furthermore, the difference between the LD features and the target features is, for example, L1 (the sum of the absolute values ​​of the differences). In this case, the blinking error calculation unit 211 calculates the parameters of the neural network of the backlight control value generation unit 203 so that the difference between the LD features and the target features is minimized. The backlight control value generation unit 203 then outputs the generated parameters to the backlight control value generation unit 203.

[0061] The method for generating parameters by the flashing error calculation unit 211 is not limited to one based on the difference between the LD feature and the target feature, but may be based on other methods. For example, the flashing error calculation unit 211 may generate parameters for the neural network of the backlight control value generation unit 203 based on the truth value determination of the LD feature and the target feature by the classifier. The classifier is composed of a neural network. In this case, the flashing error calculation unit 211 is trained so that when a combination of image feature and target feature is input, the output value approaches 1 (true), and when a combination of image feature and LD feature is input, the output value approaches 0 (fake). Furthermore, the flashing error calculation unit 211 calculates the parameters for the neural network of the backlight control value generation unit 203 so that when a combination of image feature and LD feature is input, the output value of the classifier approaches 1 (true).

[0062] Therefore, it is possible to calculate parameters that correct the error between the LD features from the LD feature generation unit 205 and the target features for each subframe. Since the calculated parameters that reduce the error are parameters that correct the error between the LD features from the LD feature generation unit 205 and the target features, the change in the amount of light input to the eye becomes smaller, and flicker can be improved.

[0063] Furthermore, the method for generating parameters by the flashing error calculation unit 211 may be a combination of multiple methods. For example, a method based on the difference between the LD feature and the target feature may be combined with a method based on the truth value determination of the LD feature and the target feature by the classifier.

[0064] The backlight control value storage unit 212 is a memory that stores the backlight control values ​​output from the backlight control value generation unit 203 over multiple frames. The stored backlight control values ​​are output to the backlight control value generation unit 203.

[0065] The method for storing backlight control values ​​in the backlight control value storage unit 212 is the same as that of the backlight control value storage unit 10207 in Figure 1. Therefore, a detailed explanation of the backlight control value storage method is omitted.

[0066] As explained with reference to Figure 4, the model learning unit 200 of Example 1 can be trained to output a backlight control value so that the displayed image due to local dimming approaches the target value.

[0067] Figure 5 is a block diagram showing the functional blocks of the LD feature generation unit 205 according to Example 1. The LD feature generation unit 205 includes a backlight brightness estimation unit 20501, a correction coefficient generation unit 20502, a correction unit 20503, a contrast conversion unit 20504, a liquid crystal response estimation unit 20505, and a synthesis unit 20506.

[0068] The backlight brightness estimation unit 20501 estimates and calculates the brightness of the light irradiated onto the liquid crystal panel 104 (backlight brightness) when the backlight module 106 of the liquid crystal display device 100 is illuminated using the backlight control value output from the backlight control value generation unit 203.

[0069] The backlight brightness estimation unit 20501 performs estimation calculations similar to the backlight brightness estimation unit 10204, estimating (calculating) the backlight brightness for each brightness estimation point discretely arranged within the display surface, as shown in Figure 3(A), based on the backlight control value and the brightness distribution model. Here, the backlight brightness estimation unit 20501 estimates (calculates) one backlight brightness for each divided region of one backlight module. The backlight brightness estimation unit 20501 obtains the brightness distribution model used for estimation calculations from the first setting unit 204. The backlight brightness estimation unit 20501 then outputs the estimated backlight brightness to the correction coefficient generation unit 20502 and the synthesis unit 20506.

[0070] The correction coefficient generation unit 20502 generates a correction coefficient to be applied to the image features based on the backlight brightness output from the backlight brightness estimation unit 20501. The method for generating the correction coefficient by the correction coefficient generation unit 20502 is the same as that of the correction coefficient generation unit 10205 of the liquid crystal display device 100, for example, by calculating it using the reciprocal of the backlight brightness. The correction coefficient generation unit 20502 then outputs the generated correction coefficient to the correction unit 20503.

[0071] The correction unit 20503 generates (calculates) corrected features by multiplying the image features output from the preprocessing unit 206 by the correction coefficient output from the correction coefficient generation unit 20502. Specifically, the data value Vc of the corrected features is calculated by multiplying the data value V of the image features by the correction coefficient Gt according to the following formula (7). Here, the data value V of the features is a value normalized to between 0.0 and 1.0. Vc = V × Gt ... (7)

[0072] The correction unit 20503 then outputs the generated correction feature quantity to the contrast conversion unit 20504.

[0073] The contrast conversion unit 20504 generates panel features by converting the correction features output from the correction unit 20503 into panel contrast. The panel features correspond to a simulated image when the backlight module 106 of the liquid crystal display device 100 is made to emit light uniformly and the correction features output from the correction unit 20503 are displayed on the liquid crystal panel 104. Specifically, the data value Vp of the panel features is calculated by performing a gain / offset calculation on the data value Vc of the correction features based on the panel contrast Cp, according to the following formula (6). The panel contrast is the contrast value of the liquid crystal panel 104 of the liquid crystal display device 100 and is obtained from the first setting unit 204. If the contrast of the liquid crystal panel 104 of the liquid crystal display device 100 is 1000:1, then 1000 is entered for the panel contrast Cp.

[0074]

number

[0075] The contrast conversion unit 20504 then outputs the corrected feature quantity (panel feature quantity) converted to panel contrast to the liquid crystal response estimation unit 20505.

[0076] The liquid crystal response estimation unit 20505 estimates and calculates LD feature quantities that estimate the responsiveness of the liquid crystal panel 104 due to gradation changes for each subframe. In general, liquid crystal panels are known to have a slow response to brightness changes, so the liquid crystal response estimation unit 20505 estimates and calculates corrected feature quantities that take into account the responsiveness of each subframe of the image, which are corrected feature quantities (panel feature quantities) output from the contrast conversion unit 20504.

[0077] The calculation of corrected features that take into account the responsiveness of the LCD panel first involves pre-measuring the LCD panel's responsiveness to brightness changes as a brightness value for each frame. For example, if the brightness changes from 0% to 100% in one frame period, and the actual responsiveness of the LCD panel only changes up to 90%, the responsiveness data would be 0.9. This responsiveness data is measured in advance for all brightness changes. The response data is measured in one frame, but since it is data representing the ratio of change, it can also be used as response data that changes for each subframe.

[0078] Next, the brightness changes of the previously input correction features and the currently input correction features are multiplied by the previously measured responsiveness data to estimate and calculate the LD features that take into account the responsiveness of the liquid crystal panel.

[0079] The liquid crystal response estimation unit 20505 then outputs corrected feature quantities (panel feature quantities) that take into account the responsiveness of the liquid crystal to the synthesis unit 20506.

[0080] The synthesis unit 20506 generates LD (Local Dimming) features by multiplying the panel features output from the liquid crystal response estimation unit 20505 by the backlight brightness output from the backlight brightness estimation unit 20501. Specifically, the data value Vl of the LD features is calculated by multiplying the data value Vp of the panel features by the backlight brightness L according to the following equation (9). Vl = Vp × L···(9)

[0081] As explained with reference to Figure 5, the LD feature generation unit 205 of Example 1 can generate data equivalent to a simulation image of local dimming for calculating the error with the target feature.

[0082] As described above, in Example 1, in a liquid crystal display device in which the luminescence brightness of the backlight module can be changed, a machine learning model can be used to generate control values ​​that take into account video responsiveness and flicker. By training the machine learning model so that the brightness of the displayed image due to local dimming approaches the target value, local dimming that can improve visual characteristics according to the characteristics of the image becomes possible.

[0083] [Other examples] The present invention can also be realized by supplying a program that implements one or more of the functions of the above-described embodiments to a system or device via a network or storage medium, and by having one or more processors in the computer of that system or device read and execute the program. It can also be realized by a circuit (e.g., an ASIC) that implements one or more functions.

[0084] It should be noted that the present invention is not limited to the embodiments described above, and the components can be modified and implemented in practice without departing from the spirit of the invention. Furthermore, various inventions can be formed by appropriately combining the multiple components disclosed in the above embodiments. For example, some components may be deleted from all the components shown in the embodiments. Moreover, components from different embodiments may be appropriately combined.

[0085] In the above embodiment, at least one of A and B may be A alone, B alone, or A and B.

[0086] Herein, the disclosure of this embodiment includes the following configuration and method.

[0087] (Composition 1) LCD panel and An input means for inputting data from the first image, A backlight module that illuminates the aforementioned liquid crystal panel with light, and whose luminescence brightness can be changed for each of the multiple divided regions, A generation means for generating the luminous intensity of each divided region of the backlight module for each subframe, Correction means for correcting the first image to a second image based on the luminescence brightness generated by the generation means, A holding means for holding the luminescence brightness generated by the generating means, A backlight control means that controls the luminous brightness of each divided region of the backlight module based on the luminous brightness generated by the generation means, A liquid crystal panel means for controlling the transmittance of the liquid crystal panel based on the data of the second image, Equipped with, The liquid crystal display device is characterized in that the generation means inputs the data generated from the first image and the luminescence brightness held by the holding means into a neural network to generate luminescence brightness.

[0088] (Configuration 2) The liquid crystal display device according to configuration 1, characterized in that the data generated from the first image is a feature quantity of the first image.

[0089] (Composition 3) The liquid crystal display device according to configuration 1 or 2, characterized in that the trained model applied to the neural network in the generation means can be changed.

[0090] (Method 1) The input process involves inputting data from the input image, A first generation step generates the luminous intensity of the backlight module for each subframe, A holding step for maintaining the luminescence brightness generated by the first generation step, A second generation step generates panel features based on the data generated from the input image and the luminescence brightness generated in the generation step, A third generation step generates LD features based on the responsiveness of the liquid crystal panel, using the panel features generated in the second generation step. An accumulation step for accumulating the LD features generated in the third generation step, A fourth generation step involves generating target features based on data generated from the aforementioned input image, A first calculation step of calculating the difference between the LD feature and the target feature, A second calculation step of calculating the difference between the LD feature and the target feature, An update step which updates the parameters of the neural network based on the difference between the first calculation step and the second calculation step, It has, The method for generating a trained model is characterized in that the generation step involves inputting the data generated from the input image and the luminescence intensity held in the retention step into a neural network to generate luminescence intensity.

[0091] (Method 2) The method for generating a trained model according to Method 1, characterized in that, in the first generation step and the second generation step, the data generated from the input image is the feature quantities of the input image.

[0092] (Method 3) The method for generating a trained model according to Method 1, characterized in that, in the third generation step, the data generated from the input image is the feature quantities of the input image.

[0093] (Method 4) The method for generating a trained model according to Method 1, characterized in that, in the third generation step, the data generated from the input image is the input image.

[0094] (Method 5) A method for generating a trained model according to any one of methods 1 to 4, characterized in that in the second generation step, the panel features are generated based on the data generated from the input image, the luminescence generated in the generation step, and the contrast of the liquid crystal panel.

[0095] (Method 6) The method for generating a trained model according to Method 5, characterized in that the contrast of the liquid crystal panel can be changed in the second generation step.

[0096] (Method 7) A method for generating a trained model according to Method 1, characterized in that, in the third generation step, the panel features are generated based on the data generated from the input image and the target contrast.

[0097] (Method 8) The method for generating a trained model according to Method 7, characterized in that the target contrast can be changed in the third generation step.

Claims

1. LCD panel and An input means for inputting data from the first image, A backlight module that illuminates the aforementioned liquid crystal panel with light, and whose luminescence brightness can be changed for each of the multiple divided regions, A generation means for generating the luminous intensity of each divided region of the backlight module for each subframe, Correction means for correcting the first image to a second image based on the luminescence brightness generated by the generation means, A holding means for holding the luminescence brightness generated by the generating means, A backlight control means that controls the luminous brightness of each divided region of the backlight module based on the luminous brightness generated by the generation means, A liquid crystal panel means for controlling the transmittance of the liquid crystal panel based on the data of the second image, Equipped with, The liquid crystal display device is characterized in that the generation means inputs the data generated from the first image and the luminescence brightness held by the holding means into a neural network to generate luminescence brightness.

2. The liquid crystal display device according to claim 1, characterized in that the data generated from the first image is a feature of the first image.

3. The liquid crystal display device according to claim 1, characterized in that the trained model applied to the neural network in the generation means is changeable.

4. LCD panel and An input means for inputting data from the first image, A backlight module that illuminates the aforementioned liquid crystal panel with light, and whose luminescence brightness can be changed for each of the multiple divided regions, A method for controlling a liquid crystal display device having the following characteristics: A generation step for generating the luminous intensity of each divided region of the backlight module for each subframe, A correction step is performed to correct the first image to a second image based on the luminescence brightness generated in the generation step, A holding step for maintaining the luminescence brightness generated by the above generation step, A backlight control step that controls the luminous brightness of each divided region of the backlight module based on the luminous brightness generated by the generation means, A liquid crystal panel process that controls the transmittance of the liquid crystal panel based on the data of the second image, It has, The control method is characterized in that the generation step involves inputting the data generated from the first image and the luminescence intensity held in the retention step into a neural network to generate luminescence intensity.

5. A program for causing a computer to function as one of the means of a liquid crystal display device according to any one of claims 1 to 3.

6. The input process involves inputting data from the input image, A first generation step generates the luminous intensity of the backlight module for each subframe, A holding step for maintaining the luminescence brightness generated by the first generation step, A second generation step generates panel features based on the data generated from the input image and the luminescence brightness generated in the generation step, A third generation step generates LD features based on the responsiveness of the liquid crystal panel, using the panel features generated in the second generation step. An accumulation step for accumulating the LD features generated in the above-mentioned third generation step, A fourth generation step involves generating target features based on data generated from the aforementioned input image, A first calculation step of calculating the difference between the LD feature and the target feature, A second calculation step of calculating the difference between the LD feature and the target feature, An update step which updates the parameters of the neural network based on the difference between the first calculation step and the second calculation step, It has, The method for generating a trained model is characterized in that the generation step involves inputting the data generated from the input image and the luminescence intensity held in the retention step into a neural network to generate luminescence intensity.

7. The method for generating a trained model according to claim 6, characterized in that, in the first generation step and the second generation step, the data generated from the input image is the feature quantities of the input image.

8. The method for generating a trained model according to claim 6, characterized in that, in the third generation step, the data generated from the input image is the feature quantities of the input image.

9. The method for generating a trained model according to claim 6, characterized in that, in the third generation step, the data generated from the input image is the input image.

10. The method for generating a trained model according to claim 6, characterized in that, in the second generation step, the panel features are generated based on the data generated from the input image, the luminescence generated in the generation step, and the contrast of the liquid crystal panel.

11. The method for generating a trained model according to claim 10, characterized in that the contrast of the liquid crystal panel can be changed in the second generation step.

12. The method for generating a trained model according to claim 6, characterized in that, in the third generation step, the panel features are generated based on the data generated from the input image and the target contrast.

13. The method for generating a trained model according to claim 12, characterized in that the target contrast can be changed in the third generation step.

14. LCD panel and An input means for inputting data from the first image, A backlight module that illuminates the aforementioned liquid crystal panel with light, and whose luminescence brightness can be changed for each of the multiple divided regions, A generation means for generating the luminous intensity of each divided region of the backlight module for each subframe, Correction means for correcting the first image to a second image based on the luminescence brightness generated by the generation means, A holding means for holding data generated from the aforementioned image data, A backlight control means that controls the luminous brightness of each divided region of the backlight module based on the luminous brightness generated by the generation means, Liquid crystal panel means for controlling the transmittance of the liquid crystal panel based on the data of the second image, Equipped with, The liquid crystal display device is characterized in that the generation means inputs the data generated from the first image and the data generated from the first image held by the holding means into a neural network to generate luminous intensity.

15. LCD panel and An input means for inputting data from the first image, A backlight module that illuminates the aforementioned liquid crystal panel with light, and whose luminescence brightness can be changed for each of the multiple divided regions, A method for controlling a liquid crystal display device having the following characteristics: A generation step for generating the luminous intensity of each divided region of the backlight module for each subframe, A correction step is performed to correct the first image to a second image based on the luminescence brightness generated in the generation step, A holding step for holding data generated from the aforementioned image data, A backlight control step that controls the luminous brightness of each divided region of the backlight module based on the luminous brightness generated by the generation means, A liquid crystal panel process that controls the transmittance of the liquid crystal panel based on the data of the second image, It has, The control method is characterized in that the generation step involves inputting the data generated from the first image and the data generated from the first image that was held in the retention step into a neural network to generate luminescence brightness.

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