CN-114255681-B - Apparatus and method for evaluating degradation of display panel and display driver using degradation evaluation value

CN114255681BCN 114255681 BCN114255681 BCN 114255681BCN-114255681-B

Abstract

The application discloses an apparatus and a method for evaluating degradation of a display panel, for evaluating a degradation state such as non-uniformity of the display panel. The method of evaluating degradation of a display panel may be implemented by generating mutual information by using a first histogram distribution vector of a reference frame having a target gray level and a second histogram distribution vector of an evaluation frame displayed on the display panel in response to the target gray level, generating normalized mutual information of the mutual information, providing weights of cognitive characteristics of gray level distribution of pixels incorporating the evaluation frame, and outputting an evaluation value obtained by multiplying the normalized mutual information and the weights.

Inventors

LI MINZHI
PU JUNYONG
LI ZHIYUAN
Kang Xizhu
XU YOULIN
JIN ZHENGXIAN

Assignees

硅工厂股份有限公司

Dates

Publication Date: 20260512
Application Date: 20210917
Priority Date: 20200923

Claims (20)

1. An apparatus for evaluating degradation of a display panel, comprising: A first histogram analysis unit configured to output a first histogram distribution vector of a reference frame having a target gray level; a second histogram analysis unit configured to output a second histogram distribution vector of an evaluation frame displayed on a display panel in response to the target gray level; a correlation analysis unit configured to generate mutual information using the first histogram distribution vector and the second histogram distribution vector, and generate normalized mutual information of the mutual information; A weight providing unit configured to provide weights of cognitive features of gray level distribution of pixels incorporated in the evaluation frame, and And an output unit configured to output an evaluation value obtained by calculating the normalized mutual information and the weight.
2. The apparatus of claim 1, wherein pixels of the reference frame are configured to have the same target gray level.
3. The apparatus of claim 1, wherein: The first histogram analysis unit generates a first histogram representing the number of pixels per gray level in the reference frame and outputs the first histogram distribution vector corresponding to the first histogram, and The second histogram analysis unit generates a second histogram representing the number of pixels per gray level in the evaluation frame, and outputs the second histogram distribution vector corresponding to the second histogram.
4. The apparatus of claim 1, wherein the correlation analysis unit comprises: A mutual information generator configured to utilize the equation To generate the mutual information, and A normalized mutual information generator configured to convert the mutual information into the normalized mutual information having a preset range, Where MI (X, Y) represents the mutual information between the first histogram distribution vector X and the second histogram distribution vector Y, X is a discrete probability variable of the first histogram distribution vector X, Y is a discrete probability variable of the second histogram distribution vector Y, p (X, Y) is a joint probability distribution of the discrete probability variable X and the discrete probability variable Y, p (X) is a surrounding probability distribution of the discrete probability variable X, and p (Y) is a surrounding probability distribution of the discrete probability variable Y.
5. The apparatus of claim 4, wherein the normalized mutual information generator is configured to generate the normalized mutual information by using an equation Converting said mutual information into said normalized mutual information, Wherein H (X) is calculated by sigma x p (X) logp (X), H (Y) is calculated by sigma y p (Y) logp (Y), p (X) is the probability distribution of the first histogram distribution vector X, p (Y) is the probability distribution of the second histogram distribution vector Y, X is the discrete probability variable of the first histogram distribution vector X, and Y is the discrete probability variable of the second histogram distribution vector Y.
6. The apparatus of claim 1, wherein the correlation analysis unit comprises: a mutual information generator configured to generate the mutual information by using the equation MI (X, Y) =H (X) +H (Y) -H (X, Y), and A normalized mutual information generator configured to convert the mutual information into the normalized mutual information having a preset range, Where MI (X, Y) represents the mutual information between the first histogram distribution vector X and the second histogram distribution vector Y, H (X) is the surrounding entropy of the first histogram distribution vector X and represents the gray level distribution of the reference frame, H (Y) is the surrounding entropy of the second histogram distribution vector Y and represents the gray level distribution of the evaluation frame, and H (X, Y) is the joint entropy and represents the sum of the gray level distribution of the reference frame that does not overlap with the gray level of the evaluation frame and the gray level distribution of the evaluation frame that does not overlap with the gray level of the reference frame.
7. The apparatus of claim 6, wherein the normalized mutual information generator is configured to generate the normalized mutual information by using an equation Converting said mutual information into said normalized mutual information, Wherein H (X) is calculated by sigma x p (X) logp (X), H (Y) is calculated by sigma y p (Y) logp (Y), p (X) is the probability distribution of the first histogram distribution vector X, p (Y) is the probability distribution of the second histogram distribution vector Y, X is the discrete probability variable of the first histogram distribution vector X, and Y is the discrete probability variable of the second histogram distribution vector Y.
8. The apparatus of claim 1, wherein the weight providing unit comprises: A just noticeable difference generator configured to generate a just noticeable difference of the evaluation frame for each pixel; a just-noticeable-difference average generator configured to generate an average of the just-noticeable differences of the evaluation frame, and A weight generator configured to provide a weight corresponding to the average of the just noticeable differences.
9. The apparatus of claim 8, wherein: A just noticeable difference generator calculates, for each pixel, an average luminance value of local pixels surrounding said each pixel, and provides said just noticeable difference corresponding to said average luminance value, Wherein, based on the middle of the range of average luminance values, the just noticeable difference has a value that increases with increasing average luminance values along a first curve and has a value that increases with decreasing average luminance values along a second curve, an The second curve has a higher growth rate than the first curve.
10. The apparatus of claim 8, wherein the weight generator: dividing a range forming the average value into a plurality of weighted ranges; Assigning a preset weight to each of the weighted ranges, and In response to the average value, the weight having the weighting range corresponding to the average value is output.
11. A method of evaluating degradation of a display panel, comprising: Outputting a first histogram distribution vector of a reference frame having a target gray level; Outputting a second histogram distribution vector of the evaluation frame displayed on the display panel in response to the target gray level; generating mutual information by using the first histogram distribution vector and the second histogram distribution vector; Generating normalized mutual information of the mutual information; providing weights incorporating cognitive features of gray level distribution of pixels of the evaluation frame, and And outputting an evaluation value obtained by multiplying the normalized mutual information by the weight.
12. The method according to claim 11, wherein: Outputting the first histogram distribution vector includes generating a first histogram representing the number of pixels per gray level in the reference frame, and outputting the first histogram distribution vector corresponding to the first histogram, and Outputting the second histogram distribution vector includes generating a second histogram representing the number of pixels per gray level in the evaluation frame, and outputting the second histogram distribution vector corresponding to the second histogram.
13. The method of claim 11, wherein the mutual information is obtained by using an equation To be generated and to be used in the process of the invention, Where MI (X, Y) represents the mutual information between the first histogram distribution vector X and the second histogram distribution vector Y, X is a discrete probability variable of the first histogram distribution vector X, Y is a discrete probability variable of the second histogram distribution vector Y, p (X, Y) is a joint probability distribution of the discrete probability variable X and the discrete probability variable Y, p (X) is a surrounding probability distribution of the discrete probability variable X, and p (Y) is a surrounding probability distribution of the discrete probability variable Y.
14. The method of claim 11, wherein the mutual information is generated by using the equation MI (X, Y) =h (X) +h (Y) -H (X, Y), Where MI (X, Y) represents the mutual information between the first histogram distribution vector X and the second histogram distribution vector Y, H (X) is the surrounding entropy of the first histogram distribution vector X and represents the gray level distribution of the reference frame, H (Y) is the surrounding entropy of the second histogram distribution vector Y and represents the gray level distribution of the evaluation frame, and H (X, Y) is the joint entropy and represents the sum of the gray level distribution of the reference frame that does not overlap with the gray level of the evaluation frame and the gray level distribution of the evaluation frame that does not overlap with the gray level of the reference frame.
15. The method of claim 11, wherein the normalized mutual information is obtained by using an equation To be generated and to be used in the process of the invention, Where MI (X, Y) represents the mutual information between the first histogram distribution vector X and the second histogram distribution vector Y, H (X) is calculated by the equation Σ x p (X) logp (X), H (Y) is calculated by the equation Σ y p (Y) logp (Y), p (X) is the probability distribution of the first histogram distribution vector X, p (Y) is the probability distribution of the second histogram distribution vector Y, X is the discrete probability variable of the first histogram distribution vector X, and Y is the discrete probability variable of the second histogram distribution vector Y.
16. The method of claim 11, wherein providing the weights comprises: generating, for each pixel, a just noticeable difference for the evaluation frame; generating an average of the just noticeable differences of the assessment frames, and A weight corresponding to the average of the just noticeable differences is provided.
17. The method of claim 16, wherein generating the just noticeable difference comprises: calculating, for each pixel, an average luminance value of local pixels around the each pixel, and Providing said just noticeable difference corresponding to said average luminance value, Based on the middle of the range of average luminance values, the just noticeable difference has a value that increases with increasing average luminance values along a first curve and has a value that increases with decreasing average luminance values along a second curve, an The second curve has a higher growth rate than the first curve.
18. The method of claim 16, wherein providing the weights comprises: dividing a range forming the average value into a plurality of weighted ranges; Assigning a preset weight to each of the weighted ranges, and In response to the average value, the weight having the weighting range corresponding to the average value is output.
19. A display driver, comprising: an evaluation value storage unit configured to store an evaluation value, and A degradation compensator configured to receive the evaluation value and compensate for degradation by converting display data based on the evaluation value, Wherein the evaluation value corresponds to a value obtained by calculating normalized mutual information obtained by evaluating correlation between a reference frame corresponding to a target gray level and a histogram distribution vector of an evaluation frame, and a weight in which cognitive characteristics of gray level distribution of pixels of the evaluation frame are incorporated.
20. The display driver of claim 19, wherein the normalized mutual information is generated by normalizing mutual information generated by using a first histogram distribution vector of the reference frame having the target gray level and a second histogram distribution vector of the evaluation frame displayed on a display panel in response to the target gray level.

Description

Apparatus and method for evaluating degradation of display panel and display driver using degradation evaluation value Technical Field The present disclosure relates to evaluation of degradation of a display panel, and more particularly, to an apparatus and method for evaluating degradation of a display panel, a degradation state such as unevenness (mura) of a display panel, and a display driver using the degradation evaluation value. Background An ideal display panel outputs an image having the same gray level as a target gray level in response to display data having the target gray level. However, when degradation such as unevenness occurs in the display panel, an image having a gray level different from the target gray level is output through the degraded pixel or region of the display panel. In most cases, the degree of degradation such as unevenness may be determined using a method of directly visually evaluating the display panel by a worker. When there is degradation in the display panel, the display system adopts a compensation technique to solve degradation such as unevenness. Accordingly, the display panel outputs an image having a reduced degree of degradation in response to the display data corrected by the compensation technique. In order to more accurately compensate for the degradation, it is necessary to quantitatively evaluate the degradation state of the display panel. Further, although the degree of degradation has been reduced by applying the compensation technique, it is still necessary to quantitatively evaluate the degradation state of the display panel having the reduced degree of degradation. Therefore, it is necessary to develop a technique capable of quantitatively evaluating the degradation state of the display panel or the degradation state having a reduced degradation degree. Disclosure of Invention Embodiments relate to providing an apparatus and method of evaluating degradation of a display panel, such as a non-uniform degradation state, which can quantitatively evaluate the display panel. Further, embodiments relate to an apparatus and method for providing degradation of an evaluation display panel that evaluates a degree of degradation by analyzing a correlation between a reference frame of a reference image and an evaluation frame of an evaluation image. Further, embodiments relate to providing a display driver capable of solving degradation of a display panel by compensating display data using an evaluation value obtained by evaluating a degradation state. In an embodiment, an apparatus for evaluating degradation of a display panel includes a first histogram analysis unit configured to output a first histogram distribution vector of a reference frame having a target gray level, a second histogram analysis unit configured to output a second histogram distribution vector of an evaluation frame displayed on the display panel in response to the target gray level, a correlation analysis unit configured to generate mutual information by using the first histogram distribution vector and the second histogram distribution vector and to generate normalized mutual information of the mutual information, a weight providing unit configured to provide weights of cognitive characteristics of gray level distribution of pixels incorporated with the evaluation frame, and an output unit configured to output an evaluation value obtained by calculating the normalized mutual information and the weights. In an embodiment, a method of evaluating degradation of a display panel includes outputting a first histogram distribution vector of a reference frame having a target gray level, outputting a second histogram distribution vector of an evaluation frame displayed on the display panel in response to the target gray level, generating mutual information by using the first histogram distribution vector and the second histogram distribution vector, generating normalized mutual information of the mutual information, providing weights of cognitive characteristics of gray level distribution of pixels incorporating the evaluation frame, and outputting an evaluation value obtained by multiplying the normalized mutual information and the weights. In an embodiment, a display driver includes an evaluation value storage unit configured to store an evaluation value, and a degradation compensator configured to receive the evaluation value and compensate for degradation by converting display data based on the evaluation value. The evaluation value corresponds to a value obtained by calculating normalized mutual information and weight. An advantage of the present disclosure is that it can calculate a quantitative evaluation value in response to a degradation state such as unevenness of a display panel. Further, the present disclosure is advantageous in that it can determine the degree of degradation of the display panel based on a quantitative evaluation value calculated by analyzing the correlation between the ref