KR20040062049A - Optical sheet and optimizing method for thereof - Google Patents

Abstract

PURPOSE: An optical sheet and a method of optimizing the optical sheet are provided to improve a viewing angle of an image display device. CONSTITUTION: A statistical specimen is extracted using transmissivity, haze and thickness data of various optical diffusion sheets and luminance data of the optical diffusion sheets. A regression model with respect to the extracted statistical specimen is accomplished. A regression factor minimizing an error with respect to the accomplished regression model is calculated and a regression expression is produced using the calculated regression factor. The transmissivity, haze and luminance of the optical diffusion sheets are predicted using the regression expression.

Description

Optical sheet and optimization method for

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image display apparatus, and more particularly, to an optical sheet and an optimization method thereof, which can optimize a condition for securing a viewing angle in a light diffusion sheet provided in an image display apparatus.

In general, CRT (or CRT: Cathode Ray Tube) among the image display apparatuses displaying image information on the screen has been the most used until now, because it is bulky and heavy compared to the display area, which is inconvenient to use. Followed.

Accordingly, a thin film type flat panel display device having a small display area that can be easily used at any place even though the display area is large has been developed, and is gradually replacing the CRT display device. In particular, liquid crystal displays (LCDs) exhibit superior display resolutions than other flat panel displays, and exhibit fast response speeds in comparison with CRTs when implementing moving images.

As is known, the driving principle of the liquid crystal display device utilizes the optical anisotropy and polarization properties of the liquid crystal. Since the liquid crystal is thin and long in structure, the direction of the molecular arrangement can be controlled by artificially applying an electromagnetic field to the liquid crystal molecules having directionality and polarization in the molecular arrangement. Therefore, if the alignment direction is arbitrarily adjusted, light may be transmitted or blocked according to the alignment direction of the liquid crystal molecules by optical anisotropy of the liquid crystal, thereby displaying colors and images.

The active matrix liquid crystal display device adds an active element to each pixel arranged in a matrix, and controls the operation of each pixel by using the switching characteristics of the element. Is an implementation of

However, a problem arises in that the contrast ratio changes according to the viewing angle of the active matrix liquid crystal display. Various solutions have been proposed to solve this viewing angle problem. For example, multidomain technology, retardation film, which divides one pixel into several regions so that the orientation of liquid crystal molecules in each region is different so that the characteristics of the pixels become the average value of the characteristics realized by the various regions contained therein. Phase compensation technology to reduce the phase difference with respect to the change in the viewing direction by using the device. In Plane Switch (IPS) mode in which the director of the liquid crystal is twisted in a plane parallel to the alignment film by applying a lateral electric field. There is a vertical alignment mode using a liquid crystal having a negative dielectric anisotropy. As such, solutions for solving the viewing angle by changing the liquid crystal mode employed in the liquid crystal display have been sought.

On the other hand, the liquid crystal display generally requires a backlight assembly such as a lamp, a light guide plate and sheets as a passive display device without a light source, which plays an important role in the display performance of the liquid crystal display device.

Such a backlight assembly is a device for supplying light to an LCD panel, and as shown in FIG. 1, a light source (Lamp) that emits light as its component, and reflects the light emitted from the light source upwards. There are a reflector, a light guide for propagating the light emitted from the light source, diffusers for diffusing the light transmitted through the upper part, and prisms for improving the light efficiency of the liquid crystal display. More). 1 is an exploded perspective view of a general LCD module (LCD Module). The backlight assembly may further include a lamp housing that supports the light source and reflects the light emitted from the light source to the light guide.

By the way, in the prior art, in order to improve the viewing angle of the liquid crystal display device, the view angle is mainly improved by changing the liquid crystal mode, and in the light diffusion sheet, attention has been paid only to the transmittance and luminance at the center point. . That is, in the related art, in the light diffusion sheet, the main focus was only on the improvement of the transmittance and the image quality by adjusting the thickness, and only the degree of luminance compared to the light source.

Accordingly, the present invention focuses on the role of the light diffusion sheet (diffusion plate), which is a component of the backlight assembly, and analyzes the correlation in which haze, transmittance, thickness, etc. of the light diffusion sheet affect luminance. The present invention proposes a method for improving the viewing angle of a liquid crystal display.

It is an object of the present invention to provide an optical sheet optimization method capable of improving the viewing angle of an image display apparatus through an optimal design of a light diffusion sheet provided in a backlight assembly for supplying light to the image display apparatus.

1 is an exploded perspective view of a typical LCD module (LCD Module).

2 is a diagram showing an example of a screen for predicting the performance of an optical sheet to be manufactured using a program produced by the method for optimizing an optical sheet according to the present invention.

Figure 3 is a view showing the predicted value and the actual measurement value of the optical sheet by the optimization method of the optical sheet according to the present invention.

In order to achieve the above object, the optical sheet optimization method according to the present invention,

Extracting a statistical sample using transmittance, haze, thickness data of various light diffusion sheets, and luminance data of light diffusion sheets corresponding to the data;

Establishing a regression model for the extracted statistical sample;

Calculating a regression coefficient that minimizes an error term (residual) for the established regression model, and deriving a regression equation using the calculated regression coefficient; And

Predicting the brightness of the light diffusion sheet by the change in transmittance, haze and thickness of the light diffusion sheet using the derived regression equation; Its features are to include.

According to the present invention, the regression model is established as L = a + bT + cH + dD + e _i , where L is brightness, T is transmittance, H is haze, D is thickness, and a, b, c and d are regression coefficients for each item, and e _i is an error term (residual).

In addition, according to the present invention, the regression equation is established as L = a + bT + cH + dD, where L is brightness, T is transmittance, H is haze, D is thickness, respectively, a, b, c, d are Its characteristics are that it is a regression coefficient for each item.

In addition, according to the present invention, in calculating a regression coefficient for minimizing the error term (residual) with respect to the established regression model, it is characterized in that to calculate the regression coefficient for minimizing the error term (residual) using the least-squares method. .

In addition, according to the present invention, in calculating the regression coefficient for minimizing the error term (residual) with respect to the established regression model, the regression coefficient is calculated for each angle inclined relative to the line perpendicular to the plane of the light diffusion sheet It has that feature.

According to the present invention, there is an advantage that the viewing angle of the image display device can be improved through the optimal design of the light diffusion sheet provided in the backlight assembly for supplying light to the image display device.

In the present invention, the transmittance, haze, thickness, and the like of the light diffusion sheet provided in the backlight assembly for supplying light for displaying an image without changing the liquid crystal mode employed in the liquid crystal display device are used. Through this analysis, a method for improving the viewing angle of the liquid crystal display device is proposed.

In general, when discussing a viewing angle in a liquid crystal display, a review is performed on a contrast ratio (CR) and a luminance viewing angle. Here, the correlation between light transmittance, haze, and thickness of the light diffusion sheet affecting the viewing angle is analyzed. .

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

In the present invention, in order to obtain the optimum design conditions of the light diffusion sheet, the transmittance, haze, thickness data of the various light diffusion sheets manufactured in the prior art and obtained by using the luminance data of the light diffusion sheet corresponding to the data Based on the statistical sample, regression and correlation analysis methods were used to analyze the relationship between each variable.

As is known, regression and correlation methods are used to analyze the relationships between variables based on statistics from a sample. Here, regression analysis is used to represent the mathematical relationship between the result and the variables, and is used to represent the relationship between each variable. Correlation analysis, on the other hand, is a simple value (not a formula) that indicates how much the relationship between two variables is.

Then, the general regression method and correlation analysis method will be briefly described.

Once we know the mathematical functional relationships between certain variables, we can use the formula to estimate the results with just a few variables. Finding this mathematical relationship between variables is a regression method. Such regression analysis includes simple regression analysis with one independent variable and one dependent variable, and multiple regression analysis analyzing the relationship between multiple independent variables and one dependent variable.

In the regression model, there are definitive models in which independent and dependent variables have an accurate one-to-one relationship, and probabilistic models in which dependent variable values are determined as stochastic values. In general, the probabilistic model can be expressed as the sum of the deterministic and the probabilistic.

On the other hand, most statistics in the regression model have a probabilistic model. Probabilistic models do not have exact values and are accompanied by errors. This error is caused by the following factors.

1) Accidental error: This is an unexplainable part. Strictly speaking, there is no such thing in the world. That is, a slight difference occurs.

2) Measurement error: Measurement is essential for statistical processing. However, an error necessarily occurs in the measurement of numerical values. No matter how accurate the measuring equipment can be, absolute measurements cannot be made.

3) Function error: This error occurs when the function relationship between the independent variable and the dependent variable is incorrectly selected.

In this regard, the analysis of the regression model is to make certain assumptions about the error terms in these probabilistic models and to find the average relationship between the dependent and independent variables.

In addition, regression coefficients must be obtained to complete the regression equation. Since these coefficients cannot be obtained for the entire population, these coefficients should be estimated from the sample. In this case, the regression model of the sample can be expressed as follows, and the error in the sample is called a residual and is denoted by e _i . For the convenience of explanation, the description will be based on a simple regression model.

Regression Model of Samples: Equation Representing Actual Value

Y _i = a + bX _i + e _i

Regression of Samples: A formula for calculating the predicted (estimated) value

Y _i '= a + bX _i

Here, the residual e _i can be obtained as

e _i = Y _i -Y _i '

At this time, since the regression coefficient of the sample is used as an estimate of the regression coefficient of the population, the larger the residual, the larger the error of the regression equation of the population. In addition, the regression model includes an error term as a formula representing a method of expressing each measured value, and the regression equation represents a relational expression between independent and dependent variables obtained from the measured values, and does not include an error term.

Such a regression can have multiple regressions. However, the regression line that best explains the relationship between the dependent and independent variables will be the line with the smallest sum of each residual. From this, we choose the regression line that minimizes the residuals.

In addition, there may be a case where the positive residual and the negative residual are canceled with each other so that the magnitude of the individual residual is not reflected in the total residual. Therefore, a method of finding a straight line in which the sum of squares of the residuals is minimum is used. This is called the least square method.

If Y _i '= a + bX _i is inserted in the above equation,

In order to complete the regression equation, constants a and b satisfying the above expressions are obtained. In this case, in order to obtain the constants a and b, the partial expressions of the equations may be obtained by performing partial differentials on a and b, and arranging the partial differential expressions.

Then, a process of deriving the optimization method of the light diffusion sheet according to the present invention using the regression analysis method and the correlation analysis method will be described.

In the present invention, in order to obtain the optimum design conditions of the light diffusion sheet, a sample using the transmittance, haze, thickness data of the various light diffusion sheets manufactured in the past and luminance data of the light diffusion sheet corresponding to the data are used. From this, a regression model was established.

L = a + bT + cH + dD + e _i

Where L is luminance, T is transmittance, H is haze, and D is thickness, a, b, c, and d are regression coefficients for each item, and e _i is an error term (residual). .

From these regression models, let's find the regression coefficients a, b, c, and d to minimize the error term for the optimization of the target prediction equation. As described above, the equation for calculating the regression coefficient using the least square method is as follows.

In order to calculate a, b, c, and d values that minimize the square of the error term in the above equation, a partial differential is performed on each of a, b, c, and d, and a constant is set by setting the expression to 0 and performing the theorem. The values of a, b, c, and d can be obtained.

In the present invention, in obtaining the constants a, b, c, and d, the angles are changed with respect to each angle (for example, in 5 degree angle units) while changing the angle about the line perpendicular to the surface of the light diffusion sheet. Constants a, b, c, d were derived. Thus, in order to calculate the constant a, b, c, d values in each angle unit, in preparing a statistical sample, luminance was measured for each angle with respect to one light diffusion sheet. The light diffusion sheet used for the measurement measured transmittance, haze and thickness for each sheet, and the constants a, b, c, and d were calculated using the measured data.

For example, in the present invention, the constants a, b, c, d obtained for a 55 degree angle are as follows, from which the regression equation is derived as follows.

a = 20.3

b = 1.13

c = -0.38

d = -0.0193

L (55 degrees) = 1.13 × T-0.38 × H-0.0193 × D + 20.3

In this way, by calculating the constants a, b, c, and d for each angle, a regression equation for each angle can be derived, and based on this, changes in transmittance, haze, thickness, etc. of the light diffusion sheet can be obtained in luminance. Analyze the impact.

That is, as shown in Figure 2, by performing the simulation using the regression equation derived through such a process, the light is employed without producing a light diffusion sheet or complete the backlight assembly provided in the image display device The viewing angle of the image display device by the diffusion sheet can be predicted. 2 is a view showing an example of a screen for predicting the performance of an optical sheet to be manufactured using a program produced by the method for optimizing the optical sheet according to the present invention.

Fig. 2 shows one example of performing the prediction. When the haze of the light diffusion sheet is 98%, the transmittance is 92%, and the thickness is selected to 160 µm, a line perpendicular to the surface of the light diffusion sheet is shown. From this point, the expected luminance ratio at 55 degrees will be 91.16, and the accuracy will be 97.7%. The expected luminance ratio here represents the luminance relative to the luminance of the light emitted from the light source of the backlight assembly.

According to the optimization method of the optical sheet according to the present invention, it is possible to perform such a simulation, it is possible to design in advance the conditions of the optical sheet that satisfies the desired characteristics, the time required for the design / manufacturing process of the optical sheet and The cost can be reduced.

3 is a view showing the predicted value and the actual measured value of the optical sheet by the optical sheet optimization method according to the present invention.

As shown in FIG. 3, when comparing the actual measured value and the predicted value for each light diffusion sheet, it can be seen that the error rate and the deviation are not large. For example, in the case of the light diffusion sheet according to Example 2, the luminance estimated using the transmittance, haze, and thickness information of the light diffusion sheet is 83.652, and the measured luminance is 82.91, and the error rate is 0.00513.

According to the optical sheet and the optimization method according to the present invention as described above, through the optimal design of the light diffusion sheet provided in the backlight assembly for supplying light to the image display device can improve the viewing angle of the image display device There is this.

In addition, according to the optical sheet and the optimization method according to the present invention, it is possible to design the conditions of the optical sheet that satisfies the desired characteristics in advance, thereby reducing the time and cost of the optical sheet design / manufacturing process There is this.

Claims (7)

Extracting a statistical sample using transmittance, haze, thickness data of various light diffusion sheets, and luminance data of light diffusion sheets corresponding to the data; Establishing a regression model for the extracted statistical sample; Calculating a regression coefficient that minimizes an error term (residual) for the established regression model, and deriving a regression equation using the calculated regression coefficient; And Predicting the brightness of the light diffusion sheet by the change in transmittance, haze and thickness of the light diffusion sheet using the derived regression equation; Optimization method of an optical sheet comprising a. The method of claim 1, The regression model is established as L = a + bT + cH + dD + e _i , where L is luminance, T is transmittance, H is haze, D is thickness, and a, b, c, d are each item Is a regression coefficient for and e _i means an error term (residual). The method of claim 1, The regression equation is established as L = a + bT + cH + dD, where L is luminance, T is transmittance, H is haze, and D is thickness, and a, b, c, d are regression coefficients for each item. The optimization method of the optical sheet characterized by the above-mentioned. The method of claim 1, In calculating a regression coefficient for minimizing an error term (residual) with respect to the established regression model, using the least-squares method to calculate a regression coefficient for minimizing the error term (residual). The method of claim 1, In calculating a regression coefficient for minimizing an error term (residual) with respect to the established regression model, the regression coefficient is calculated for each inclination angle based on a line perpendicular to the plane of the light diffusion sheet Optimization method. An optical sheet manufactured according to the transmittance, haze, and thickness selected by the conditions of the light diffusion sheet with reference to the result predicted by the method of any one of claims 1 to 5. An image display comprising an optical sheet manufactured according to the transmittance, haze, and thickness selected by the conditions of the light diffusing sheet with reference to the result predicted by the method of any one of claims 1 to 5. Device.