WO2016026238A1 - 图像的边界判别方法、装置和显示面板 - Google Patents
图像的边界判别方法、装置和显示面板 Download PDFInfo
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- WO2016026238A1 WO2016026238A1 PCT/CN2014/092864 CN2014092864W WO2016026238A1 WO 2016026238 A1 WO2016026238 A1 WO 2016026238A1 CN 2014092864 W CN2014092864 W CN 2014092864W WO 2016026238 A1 WO2016026238 A1 WO 2016026238A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/12—Edge-based segmentation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
- G09G3/00—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes
- G09G3/001—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes using specific devices not provided for in groups G09G3/02 - G09G3/36, e.g. using an intermediate record carrier such as a film slide; Projection systems; Display of non-alphanumerical information, solely or in combination with alphanumerical information, e.g. digital display on projected diapositive as background
- G09G3/003—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes using specific devices not provided for in groups G09G3/02 - G09G3/36, e.g. using an intermediate record carrier such as a film slide; Projection systems; Display of non-alphanumerical information, solely or in combination with alphanumerical information, e.g. digital display on projected diapositive as background to produce spatial visual effects
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
- G09G3/00—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes
- G09G3/20—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
- G09G5/00—Control arrangements or circuits for visual indicators common to cathode-ray tube indicators and other visual indicators
- G09G5/02—Control arrangements or circuits for visual indicators common to cathode-ray tube indicators and other visual indicators characterised by the way in which colour is displayed
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20004—Adaptive image processing
Definitions
- the present disclosure relates to the field of display technologies, and in particular, to an image boundary determination method, apparatus, and display panel.
- the virtual algorithm is a new type of image processing method.
- the virtual algorithm can raise the lower physical resolution to a higher virtual resolution, optimize the display effect, and prompt the visual experience of the human eye.
- the virtual algorithm generally uses a filter to process the input signal, and the input signal is resampled and distributed by the filter to realize virtual display.
- the filter in the virtual algorithm can not accurately handle various image types. For example, some filters will have colored edges under the slash pattern, which is generally called color aliasing.
- color aliasing In order to reduce the color aliasing effect, on the one hand, it is necessary to continuously test the feedback, optimize the filter, and improve the display effect; on the other hand, it is necessary to use different filter processing for different image types or different positions of the same image, for example: for none
- the position of the boundary is usually a general-purpose filter, and for a boundaryd position, a boundary filter is usually used.
- the embodiments of the present disclosure provide a method and device for determining a boundary of an image, so as to effectively determine whether an image has a boundary and a direction of a boundary, and improve a display effect of the display panel.
- the present disclosure provides an image boundary determination method, including:
- the row-direction gradient value, the column-direction gradient value, and the first diagonal of the gray-scale parameter value corresponding to the sub-pixel unit to be processed a minimum gradient value of the line direction gradient value and the second diagonal direction gradient value, and a row direction standard deviation value, a column direction standard deviation value, a first diagonal direction standard deviation value, and a second diagonal standard deviation The minimum standard deviation in the value;
- the method for discriminating the boundary of the image provided by the present disclosure may be based on the luminance component or the chrominance component, and the received image information to be discriminated may be processed to obtain a grayscale parameter value corresponding to the sub-pixel unit to be processed.
- n ⁇ n matrix and (n + 2) ⁇ (n + 2) matrix using gradient, standard deviation and dispersion parameters for the n ⁇ n matrix and (n + 2) ⁇ (n + 2) matrix respectively corresponding to the image
- the discrimination can be performed to effectively determine whether the image to be discriminated has a boundary and the direction in which the boundary exists. According to the result of the discrimination, the correct filter can be selected to improve the processing effect of the filter on the image, thereby improving the display effect of the display panel.
- the determining is in a n ⁇ n matrix and an (n+2) ⁇ (n+2) matrix: a row-direction gradient in which a grayscale parameter value corresponding to the sub-pixel unit to be processed is located Value, column-to-gradient value, first diagonal direction gradient value, and minimum gradient value in the second diagonal direction gradient value, and row-to-standard deviation, column-to-standard deviation, first diagonal direction criterion
- the minimum standard deviation of the difference, and the second diagonal standard deviation specifically includes:
- n ⁇ n matrix and the (n+2) ⁇ (n+2) matrix the row-direction gradient value, the column-direction gradient value, and the first diagonal line where the gray-scale parameter values corresponding to the sub-pixel unit to be processed are located
- the minimum gradient value of the direction gradient value and the second diagonal direction gradient value, and the gradient value in each direction is the sum of the absolute values of two adjacent gray scale parameter values in the direction:
- G represents the gradient value
- x represents the direction
- Y xi represents the i-th gray-scale parameter value in the x-direction
- n represents the number of the row-direction or column-direction gray-scale parameter values
- the determining is in an n ⁇ n matrix and an (n+2) ⁇ (n+2) matrix: a row-to-standard deviation, a column-to-standard deviation, and a first diagonal.
- the direction standard deviation and the dispersion of the standard deviation of the second diagonal direction with respect to the minimum standard deviation, and the row direction gradient value, the column direction gradient value, the first diagonal direction gradient value, and the second diagonal direction specifically includes:
- A represents a second minimum gradient value
- Dif represents a third deviation.
- H, V, LD, RD represent the row-to-standard deviation, column-to-standard deviation, first diagonal direction standard deviation, and second in the (n+2) ⁇ (n+2) matrix, respectively
- A represents the second minimum standard deviation
- Dif represents the fourth dispersion.
- the first code value when the determined dispersion is greater than N times the minimum value corresponding thereto, the first code value is output, and when the determined dispersion is less than N times the corresponding minimum value, the output is a second code value, where the minimum value is a minimum standard deviation value or a minimum gradient value, specifically including:
- the first code value is output correspondingly, and if Dif ⁇ N ⁇ A, the second code value is correspondingly output, where: N is a constant greater than 1, and when Dif represents the first dispersion, A represents a first minimum gradient value; when Dif represents a second dispersion, A represents a first minimum standard deviation; when Dif represents a third dispersion, A represents a second minimum gradient value; when Dif represents a fourth dispersion, A represents the second minimum standard deviation.
- the N value is from 1.4 to 2.0.
- the N value is
- the determining, according to the output code value, whether the image to be discriminated has a boundary and the boundary direction that exists includes:
- the output code values corresponding to the first dispersion degree, the second dispersion degree, the third dispersion degree, and the fourth dispersion degree are all the first code values, determining that the image to be discriminated has a boundary, and the second gradient value and the There is a boundary in the direction corresponding to the smallest one of the two standard deviation values;
- the output code value corresponding to the first dispersion degree, the second dispersion degree, and the fourth dispersion degree is the first code value
- the output code value corresponding to the third dispersion degree is the second code value
- the output code value corresponding to the first dispersion degree, the second dispersion degree, and the third dispersion degree is the first code value
- the output code value corresponding to the fourth dispersion degree is the second code value
- the output code value corresponding to the first dispersion degree and the second dispersion degree is the first code value
- the third dispersion degree, and the output code value corresponding to the fourth dispersion degree is the second code value
- the first code value is 1 and the second code value is 0.
- the present disclosure also provides an image boundary determining apparatus, including:
- a receiving module configured to receive grayscale parameter component value information of each sub-pixel unit in the image information to be discriminated to form a grayscale parameter value matrix, and use the grayscale parameter value corresponding to the sub-pixel unit to be processed as a center
- the grayscale parameter value matrix is divided into an n ⁇ n matrix and an (n+2) ⁇ (n+2) matrix, where n represents the number of rowwise or columnwise grayscale parameter values, and n is an odd number greater than 1,
- the gray scale parameter is a brightness parameter or a chromaticity parameter;
- a first determining module configured to determine, in the n ⁇ n matrix and the (n+2) ⁇ (n+2) matrix, the row-direction gradient value and the column direction where the gray-scale parameter values corresponding to the sub-pixel unit to be processed are located a gradient value, a first gradient direction gradient value, and a minimum gradient value in the second diagonal direction gradient value, and a row direction standard deviation value, a column direction standard deviation value, a first diagonal direction standard deviation value, and The minimum standard deviation of the second diagonal standard deviation;
- a second determining module configured to determine, respectively, in the n ⁇ n matrix and the (n+2) ⁇ (n+2) matrix: a row-to-standard deviation, a column-to-standard deviation, and a first diagonal direction standard deviation And a dispersion of a standard deviation value of the second diagonal direction with respect to a minimum standard deviation, and a gradient value of the row direction, a gradient value of the column direction, a gradient value of the first diagonal direction, and a gradient value of the second diagonal direction with respect to The dispersion of the minimum gradient value;
- An analysis module configured to output a first code value when the determined dispersion is greater than N times its corresponding minimum value, and output a second code value when the determined dispersion is less than N times its corresponding minimum value, wherein : the minimum value is a minimum standard deviation value or a minimum gradient value, and N is a constant greater than 1;
- the judging module is configured to determine, according to the output code value, whether the image to be discriminated has a boundary and a boundary direction existing.
- the present disclosure also provides a display panel including a boundary discriminating device of the above image.
- FIG. 1 is a flowchart of a method for determining a boundary of an image according to an embodiment of the present disclosure
- FIG. 2 is a first distribution diagram of an n ⁇ n matrix and an (n+2) ⁇ (n+2) matrix according to an embodiment of the present disclosure
- FIG. 3 is a schematic diagram of a second distribution of an n ⁇ n matrix and an (n+2) ⁇ (n+2) matrix according to an embodiment of the present disclosure
- FIG. 4 is a schematic diagram of a third distribution of an n ⁇ n matrix and an (n+2) ⁇ (n+2) matrix according to an embodiment of the present disclosure
- FIG. 5 is a schematic structural diagram of an apparatus for discriminating an image boundary according to an embodiment of the present disclosure.
- FIG. 1 is a flowchart of a method for discriminating an image boundary according to an embodiment of the present disclosure.
- the present disclosure provides a method for discriminating an image boundary, including:
- Step S101 Receive grayscale parameter component value information of each sub-pixel unit in the image information to be discriminated to form a grayscale parameter value matrix, and set the grayscale parameter value with the grayscale parameter value corresponding to the sub-pixel unit to be processed as a center.
- the matrix is divided into an n ⁇ n matrix and an (n+2) ⁇ (n+2) matrix, where n represents the number of rowwise or columnwise grayscale parameter values, and n is an odd number greater than 1, and the grayscale parameter is brightness.
- Parameter or chrominance parameter Parameter or chrominance parameter
- Step S102 determining, in the n ⁇ n matrix and the (n+2) ⁇ (n+2) matrix, the row-direction gradient value, the column-direction gradient value, and the corresponding gray-scale parameter value corresponding to the sub-pixel unit to be processed. a minimum gradient value of a pair of diagonal direction gradient values and a second diagonal direction gradient value, and a row direction standard deviation value, a column direction standard deviation value, a first diagonal direction standard deviation value, and a second diagonal angle The minimum standard deviation of the line standard deviations;
- Step S103 respectively determining in the n ⁇ n matrix and the (n+2) ⁇ (n+2) matrix: the row direction standard deviation value, the column direction standard deviation value, the first diagonal direction standard deviation value, and the second pair The dispersion of the standard deviation of the angular direction with respect to the minimum standard deviation, and the gradient of the row direction, the gradient of the column direction, the gradient value of the first diagonal direction, and the gradient value of the second diagonal direction with respect to the minimum gradient value Dispersion
- Step S104 output a first code value when the determined dispersion is greater than N times its corresponding minimum value; when the determined dispersion is less than N times its corresponding minimum value, output a second code value; wherein: the minimum The value is the minimum standard deviation or the minimum gradient value, and N is a constant greater than one;
- Step S105 determining, according to the output code value, whether there is a boundary and an existing image to be discriminated In the direction of the boundary.
- the method for discriminating the boundary of the image provided by the present disclosure may be based on the luminance component or the chrominance component, and the received image information to be discriminated may be processed to obtain a grayscale parameter value corresponding to the sub-pixel unit to be processed.
- n ⁇ n matrix and (n + 2) ⁇ (n + 2) matrix using gradient, standard deviation and dispersion parameters for the n ⁇ n matrix and (n + 2) ⁇ (n + 2) matrix respectively corresponding to the image
- the discrimination can be performed to effectively determine whether the image to be discriminated has a boundary and the direction in which the boundary exists. According to the result of the discrimination, the correct filter can be selected to improve the processing effect of the filter on the image, thereby improving the display effect of the display panel.
- FIG. 2 is a schematic diagram of a first distribution of an n ⁇ n matrix and an (n+2) ⁇ (n+2) matrix according to an embodiment of the present disclosure.
- the discriminating method provided by the present disclosure will be explained with reference to FIG. 2, which is illustrated as a 3 ⁇ 3 matrix and a 5 ⁇ 5 matrix, where: Y 3,3 represents the grayscale parameter value corresponding to the sub-pixel unit to be processed.
- Step S102 determining, in the n ⁇ n matrix and the (n+2) ⁇ (n+2) matrix, the row-direction gradient value, the column-direction gradient value, and the gradient value of the gray-scale parameter corresponding to the sub-pixel unit to be processed, a minimum gradient value of the first diagonal direction gradient value and the second diagonal direction gradient value, and a row direction standard deviation value, a column direction standard deviation value, a first diagonal direction standard deviation value, and a second pair
- the minimum standard deviation of the standard deviation of the corners including:
- n ⁇ n matrix and the (n+2) ⁇ (n+2) matrix the row-direction gradient value, the column-direction gradient value, and the first diagonal line where the gray-scale parameter values corresponding to the sub-pixel unit to be processed are located
- the minimum gradient value of the direction gradient value and the second diagonal direction gradient value, and the gradient value in each direction is the sum of the absolute values of two adjacent gray scale parameter values in the direction:
- G represents the gradient value
- x represents the direction (the direction in the scheme is the row direction or the column direction)
- Y xi represents the i-th gray-scale parameter value in the x direction
- n represents the row-direction or column-direction gray-scale parameter value.
- the number of in fact, the number of grayscale parameter values in the x direction, but the number of rows and columns in the present disclosure is the same);
- the first diagonal direction gradient value is the first diagonal direction gradient value
- the first diagonal direction gradient value is the first diagonal direction gradient value
- the result of the comparison is that the first minimum gradient value is G V9 and the second minimum gradient value is G LD25 .
- step S103 respectively determining in the n ⁇ n matrix and the (n+2) ⁇ (n+2) matrix: the row direction standard deviation value, the column direction standard deviation value, the first diagonal direction standard deviation value, and the second The dispersion of the standard deviation of the diagonal direction with respect to the minimum standard deviation, and the gradient of the row direction, the gradient of the column direction, the gradient value of the first diagonal direction, and the gradient value of the second diagonal direction with respect to the minimum gradient value
- the dispersion including:
- A represents a second minimum gradient value
- Dif represents a third deviation.
- H, V, LD, RD represent the row-to-standard deviation, column-to-standard deviation, first diagonal direction standard deviation, and second in the (n+2) ⁇ (n+2) matrix, respectively
- A represents the second minimum standard deviation
- Dif represents the fourth dispersion.
- step S104 when the determined dispersion is greater than N times the corresponding minimum value, the output
- the first code value when the determined dispersion is less than N times the corresponding minimum value, the second code value is output, and the minimum value is the minimum standard deviation value or the minimum gradient value, which specifically includes:
- the first code value is output correspondingly, and if Dif ⁇ N ⁇ A, the second code value is correspondingly output, where: N is a constant greater than 1, and when Dif represents the first dispersion, A represents a first minimum gradient value; when Dif represents a second dispersion, A represents a first minimum standard deviation; when Dif represents a third dispersion, A represents a second minimum gradient value; when Dif represents a fourth dispersion, A represents the second minimum standard deviation.
- the discriminating process is: the first dispersion degree Dif>NG V9 , correspondingly outputting the first code value, and the first dispersion degree Dif ⁇ NG V9 , correspondingly outputting the second code value;
- a second dispersion degree Dif>NS H9 correspondingly outputting a first code value
- a second dispersion degree Dif ⁇ NS H9 correspondingly outputting a second code value
- the third dispersion degree Dif>NG LD25 correspondingly outputs the first code value
- the third dispersion degree Dif ⁇ NG LD25 correspondingly outputs the second code value
- the fourth dispersion degree Dif>NS RD25 correspondingly outputs the first code value
- the fourth dispersion degree Dif ⁇ NS RD25 correspondingly outputs the second code value.
- the value of N is At this time, the image boundary discrimination method provided by the present disclosure has the highest accuracy.
- step S105 it is determined whether the image to be discriminated has a boundary according to the output code value and the boundary direction of the boundary includes:
- the output code values corresponding to the first dispersion degree, the second dispersion degree, the third dispersion degree, and the fourth dispersion degree are all the first code values, determining that the image to be discriminated has a boundary, and the second gradient value and the There is a boundary in the direction corresponding to the smallest one of the two standard deviation values;
- the output code value corresponding to the first dispersion degree, the second dispersion degree, and the fourth dispersion degree is the first code value
- the output code value corresponding to the third dispersion degree is the second code value
- the output code value corresponding to the first dispersion degree, the second dispersion degree, and the third dispersion degree is the first code value
- the output code value corresponding to the fourth dispersion degree is the second code value
- the output code value corresponding to the first dispersion degree and the second dispersion degree is the first code value
- the third dispersion degree, and the output code value corresponding to the fourth dispersion degree is the second code value
- the output code values corresponding to the first dispersion degree, the second dispersion degree, the third dispersion degree, and the fourth dispersion degree are all the first code values, determining that the image to be discriminated has a boundary, if G LD25 ⁇ S RD25 , then There is a boundary in the first diagonal direction of the 5 ⁇ 5 matrix; if G LD25 >S RD25 , there is a boundary in the second diagonal direction of the 5 ⁇ 5 matrix;
- the output code value corresponding to the first dispersion degree, the second dispersion degree, and the fourth dispersion degree is the first code value
- the output code value corresponding to the third dispersion degree is the second code value
- the output code value corresponding to the first dispersion degree, the second dispersion degree, and the third dispersion degree is the first code value
- the output code value corresponding to the fourth dispersion degree is the second code value
- the first code value is 1 and the second code value is 0.
- the first code value and the second code value may also be other values as long as the first code and the second code can be distinguished.
- FIG. 3 is a schematic diagram of a second distribution of an n ⁇ n matrix and an (n+2) ⁇ (n+2) matrix according to an embodiment of the present disclosure
- the first diagonal direction gradient value is the first diagonal direction gradient value
- the minimum first gradient value is a second diagonal direction gradient value.
- the first diagonal direction gradient value is the first diagonal direction gradient value
- the minimum second gradient value is a vertical gradient value.
- the first minimum standard deviation is the standard deviation of the second diagonal direction.
- the second minimum standard deviation is the vertical standard deviation.
- FIG. 4 is a schematic diagram of a third distribution of an n ⁇ n matrix and an (n+2) ⁇ (n+2) matrix according to an embodiment of the present disclosure.
- the first diagonal direction gradient value is the first diagonal direction gradient value
- the minimum first gradient value is the first diagonal direction gradient value.
- the first diagonal direction gradient value is the first diagonal direction gradient value
- the minimum second gradient value is the first diagonal direction gradient value.
- the first minimum standard deviation is the standard deviation of the first diagonal direction.
- the second minimum standard deviation is the standard deviation of the first diagonal direction.
- FIG. 5 is a schematic structural diagram of an apparatus for determining a boundary of an image according to an embodiment of the present disclosure
- the receiving module 1 is configured to receive grayscale parameter component value information of each sub-pixel unit in the image information to be discriminated to form a grayscale parameter value matrix, and set the grayscale parameter value corresponding to the sub-pixel unit to be processed to be gray
- the order parameter value matrix is divided into an n ⁇ n matrix and an (n+2) ⁇ (n+2) matrix, where n rows or columns represent the number of grayscale parameter values, and n is an odd number greater than 1, gray scale
- the parameter is a brightness parameter or a chromaticity parameter;
- a first determining module 2 configured to determine, in the n ⁇ n matrix and the (n+2) ⁇ (n+2) matrix, the row-direction gradient value and the column where the gray-scale parameter value corresponding to the sub-pixel unit to be processed is located a gradient value, a first gradient direction gradient value, and a minimum gradient value in the second diagonal direction gradient value, and a row direction standard deviation value, a column direction standard deviation value, a first diagonal direction standard deviation value, And a minimum standard deviation of the second diagonal standard deviation;
- the second determining module 3 is configured to respectively determine the n ⁇ n matrix and the (n+2) ⁇ (n+2) matrix: the row direction standard deviation value, the column direction standard deviation value, and the first diagonal direction standard deviation a value and a dispersion of a standard deviation of the second diagonal direction with respect to a minimum standard deviation, and a row gradient value, a column gradient value, a first diagonal direction gradient value, and a second diagonal direction gradient value The dispersion of the minimum gradient value;
- the analyzing module 4 is configured to output a first code value when the determined dispersion is greater than N times its corresponding minimum value, and output a second when the determined dispersion is less than N times its corresponding minimum value Code value, where: the minimum value is the minimum standard deviation value or the minimum gradient value, and N is a constant greater than 0;
- the determining module 5 is configured to determine, according to the output code value, whether the image to be discriminated has a boundary and a boundary direction that exists.
- the present disclosure also provides a display panel including the above-described image boundary discriminating device.
- the display panel provided by the present disclosure has a better display effect based on the advantages of the discriminating device.
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Abstract
一种图像的边界判别方法、装置和显示面板,用以有效地判别图像是否有边界以及边界的方向。其中判别方法包括接收待判别的图像信息以形成灰阶参数值矩阵,并以待处理的子像素单元对应的灰阶参数值为中心将灰阶参数值矩阵划分为n×n矩阵和(n+2)×(n+2)矩阵(S101);分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:待处理的子像素单元对应的灰阶参数值所在的行向、列向、第一对角线方向和第二对角线方向的最小梯度值、最小标准差值、以及相对于最小标准差值的离散度、相对于最小梯度值的离散度(S102;S103);当确定的离散度大于N倍的与其对应的最小值时,输出第一代码值,当确定的离散度小于N倍的与其对应的最小值时,输出第二代码值(S104),以及根据输出的代码值确定待判别的图像是否存在边界以及所存在的边界方向(S105)。
Description
本公开涉及显示技术领域,尤其涉及一种图像的边界判别方法、装置和显示面板。
虚拟算法是一种新型的图像处理手段,针对特定的子像素排列方式,虚拟算法能够将较低的物理分辨率提升到较高的虚拟分辨率,优化显示效果,提示人眼的视觉体验。
虚拟算法一般采用滤波器来处理输入信号,通过滤波器对输入信号重新采样分配,实现虚拟显示。但虚拟算法中的滤波器并不能准确处理各种图像类型,比如一些滤波器在斜线图形下会出现彩色边缘,一般把这种现象叫做颜色混叠。为了减少颜色混叠效应,一方面需要不断测试反馈,优化滤波器,改善显示效果;另一方面,需要针对不同的图像类型,或者同一图像的不同位置采用不同的滤波器处理,例如:对于无边界的位置通常采用通用滤波器,而对于有边界的位置通常采用边界滤波器。
为了实现分情况滤波,需要对输入数据的亮度信号进行处理,设计判别流程,识别其中的锐化边界,对特定的横线、竖线、左对角线、右对角线进行分类,从而采用不同的滤波器。
因此,如何有效地判别图像的边界,是本领域技术人员亟需解决的技术问题。
发明内容
有鉴于此,本公开实施例提供了一种图像的边界判别方法和装置,用以有效地判别图像是否有边界以及边界的方向,提高显示面板的显示效果。
相应地,本公开提供如下技术方案:
本公开提供了一种图像的边界判别方法,包括:
接收待判别的图像信息中每个子像素单元的灰阶参数分量值信息以形成灰阶参数值矩阵,并以待处理的子像素单元对应的灰阶参数值为中心将所述灰阶参数值矩阵划分为n×n矩阵和(n+2)×(n+2)矩阵,其中n代表行向或列向
灰阶参数值的个数,且n为大于1的奇数,所述灰阶参数为亮度参数或色度参数;
分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:待处理的子像素单元对应的灰阶参数值所在的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值中的最小梯度值,以及行向标准差值、列向标准差值、第一对角线方向标准差值、和第二对角线标准差值中的最小标准差值;
分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:行向标准差值、列向标准差值、第一对角线方向标准差值以及第二对角线方向标准差值相对于最小标准差值的离散度,以及行向梯度值、列向梯度值、第一对角线方向梯度值以及第二对角线方向梯度值相对于最小梯度值的离散度;
当确定的离散度大于N倍的与其对应的最小值时,输出第一代码值;当确定的离散度小于N倍的与其对应的最小值时,输出第二代码值,其中:所述最小值为最小标准差值或最小梯度值,N为大于1的常数;
根据输出的代码值确定待判别的图像是否存在边界以及所存在的边界方向。
本公开提供的图像的边界判别方法,可以基于亮度分量也可以基于色度分量,对接收到的待判别的图像信息进行处理,得到以待处理的子像素单元对应的灰阶参数值为中心的n×n矩阵和(n+2)×(n+2)矩阵,采用梯度、标准差以及离散度等参数对n×n矩阵和(n+2)×(n+2)矩阵分别对应的图像进行判别,能够有效的确定待判别图像是否存在边界,以及边界存在的方向。可以根据判别的结果,选择正确的滤波器,提高滤波器对图像的处理效果,进而提高显示面板的显示效果。
在一些可选的实施方式中,所述分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:待处理的子像素单元对应的灰阶参数值所在的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值中的最小梯度值,以及行向标准差值、列向标准差值、第一对角线方向标准差值、和第二对角线标准差值中的最小标准差值,具体包括:
确定在n×n矩阵和(n+2)×(n+2)矩阵中:待处理的子像素单元对应的灰阶参数值所在的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值中的最小梯度值,每一方向上梯度值为该方向上相邻两个灰阶参数值的绝对值之和:其中:G代表梯度值,x代表方向,
Yxi代表x方向上的第i个灰阶参数值,n代表行向或列向灰阶参数值的个数;
比较n×n矩阵中的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值,得到第一最小梯度值,比较(n+2)×(n+2)矩阵中的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值得到第二最小梯度值;
根据公式:确定在n×n矩阵和(n+2)×(n+2)矩阵中:待处理的子像素单元对应的灰阶参数值所在的行向标准差值、列向标准差值、第一对角线方向标准差值和第二对角线方向标准差值,其中:S代表标准差值,x代表方向,Yxi代表x方向上的第i个灰阶参数值,代表x方向上的灰阶参数值的平均值,n代表行向或列向灰阶参数值的个数,
比较n×n矩阵中的行向标准差值、列向标准差值、第一对角线方向标准差值和第二对角线方向标准差值,得到第一最小标准差值,比较(n+2)×(n+2)矩阵中的行向标准差值、列向标准差值、第一对角线方向标准差值和第二对角线方向标准差值得到第二最小标准差值。
在一些可选的实施方式中,所述分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:行向标准差值、列向标准差值、第一对角线方向标准差值以及第二对角线方向标准差值相对于最小标准差值的离散度,以及行向梯度值、列向梯度值、第一对角线方向梯度值以及第二对角线方向梯度值相对于最小梯度值的离散度,具体包括:
根据公式: 确定在n×n矩阵和(n+2)×(n+2)矩阵中:行向标准差值、列向标准差值、第一对角线方向标准差值以及第二对角线方向标准差值相对于最小标准差值的离散度,以及行向梯度值、列向梯度值、第一对角线方向梯度值以及第二对角线方向梯度值相对于最小梯度值的离散度,其中:当H、V、LD、RD分别代表n×n矩阵中的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值时,A代表第一最小梯度值,Dif代表第一离散度;当H、V、LD、RD分别代表n×n矩阵中的行向标准差值、列向标准差值、第一对角线方向标准差值和第二对角线方向标准差值时,A代表第一最小标准差值,Dif代表第二离散度;当H、V、LD、RD分别代表(n+2)×(n+2)矩阵中的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值时,A代表第二最小梯度值,Dif代表第三离散度;当H、V、LD、RD分别代表(n+2)×(n+2)矩阵中的行向标准差值、列向标准差值、第一对角线方向标准差值和第二对角线
方向标准差值时,A代表第二最小标准差值,Dif代表第四离散度。
在一些可选的实施方式中,所述当确定的离散度大于N倍的与其对应的最小值时,输出第一代码值,当确定的离散度小于N倍的与其对应的最小值时,输出第二代码值,所述最小值为最小标准差值或最小梯度值,具体包括:
若Dif>N×A,对应地输出第一代码值,若Dif<N×A,对应地输出第二代码值,其中:N为大于1的常数,当Dif代表第一离散度时,A代表第一最小梯度值;当Dif代表第二离散度时,A代表第一最小标准差值;当Dif代表第三离散度时,A代表第二最小梯度值;当Dif代表第四离散度时,A代表第二最小标准差值。
在一些可选的实施方式中,所述N值为1.4~2.0。
在一些可选的实施方式中,所述根据输出的代码值确定待判别的图像是否存在边界以及所存在的边界方向具体包括:
当第一离散度、第二离散度、第三离散度以及第四离散度对应的输出代码值均为第二代码值时,则确定待判别的图像无边界;
当第一离散度、第二离散度、第三离散度以及第四离散度对应的输出代码值均为第一代码值时,则确定待判别的图像存在边界,且在第二梯度值和第二标准差值中最小的一个值对应的方向上存在边界;
当第一离散度、第二离散度以及第四离散度对应的输出代码值为第一代码值、第三离散度对应的输出代码值为第二代码值时,则确定待判别的图像存在边界,且在第一梯度值和第一标准差值中最小的一个值对应的方向上存在边界;
当第一离散度、第二离散度以及第三离散度对应的输出代码值为第一代码值、第四离散度对应的输出代码值为第二代码值时,则确定待判别的图像存在边界,且在第一梯度值和第一标准差值中最小的一个值对应的方向上存在边界;
当第一离散度和第二离散度对应的输出代码值为第一代码值、第三离散度和第四离散度对应的输出代码值为第二代码值时,则确定待判别的图像存在边界,且在第一梯度值和第一标准差值中最小的一个值对应的方向上存在边界。
在一些可选的实施方式中,所述第一代码值为1,所述第二代码值为0。
本公开还提供了一种图像的边界判别装置,包括:
接收模块,用于接收待判别的图像信息中每个子像素单元的灰阶参数分量值信息以形成灰阶参数值矩阵,并以待处理的子像素单元对应的灰阶参数值为中心将所述灰阶参数值矩阵划分为n×n矩阵和(n+2)×(n+2)矩阵,其中n代表行向或列向灰阶参数值的个数,且n为大于1的奇数,所述灰阶参数为亮度参数或色度参数;
第一确定模块,用于分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:待处理的子像素单元对应的灰阶参数值所在的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值中的最小梯度值,以及行向标准差值、列向标准差值、第一对角线方向标准差值、和第二对角线标准差值中的最小标准差值;
第二确定模块,用于分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:行向标准差值、列向标准差值、第一对角线方向标准差值以及第二对角线方向标准差值相对于最小标准差值的离散度,以及行向梯度值、列向梯度值、第一对角线方向梯度值以及第二对角线方向梯度值相对于最小梯度值的离散度;
分析模块,用于在确定的离散度大于N倍的与其对应的最小值时,输出第一代码值,当确定的离散度小于N倍的与其对应的最小值时,输出第二代码值,其中:所述最小值为最小标准差值或最小梯度值,N为大于1的常数;
判断模块,用于根据输出的代码值确定待判别的图像是否存在边界以及所存在的边界方向。
本公开还提供了一种显示面板,包括上述图像的边界判别装置。
图1为本公开实施例提供的图像的边界判别方法流程图;
图2为本公开实施例提供的n×n矩阵和(n+2)×(n+2)矩阵第一种分布示意图;
图3为本公开实施例提供的n×n矩阵和(n+2)×(n+2)矩阵第二种分布示意图;
图4为本公开实施例提供的n×n矩阵和(n+2)×(n+2)矩阵第三种分布示意图;
图5为本公开实施例提供的图像边界的判别装置结构示意图。
下面将结合本公开实施例中的附图,对本公开实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本公开一部分实施例,而不是全部的实施例。基于本公开中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本公开专利保护的范围。
为了实现分情况滤波,需要对输入数据的亮度信号进行处理,设计判别流程,识别其中的锐化边界,对特定的横线、竖线、左对角线、右对角线进行分类,从而采用不同的滤波器。
为了便于对图像边界进行判别,如图1所示,图1为本公开实施例提供的图像的边界判别方法流程图,本公开提供了一种图像的边界判别方法,包括:
步骤S101:接收待判别的图像信息中每个子像素单元的灰阶参数分量值信息以形成灰阶参数值矩阵,并以待处理的子像素单元对应的灰阶参数值为中心将灰阶参数值矩阵划分为n×n矩阵和(n+2)×(n+2)矩阵,其中n代表行向或列向灰阶参数值的个数,且n为大于1的奇数,灰阶参数为亮度参数或色度参数;
步骤S102:分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:待处理的子像素单元对应的灰阶参数值所在的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值中的最小梯度值,以及行向标准差值、列向标准差值、第一对角线方向标准差值、和第二对角线标准差值中的最小标准差值;
步骤S103:分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:行向标准差值、列向标准差值、第一对角线方向标准差值以及第二对角线方向标准差值相对于最小标准差值的离散度,以及行向梯度值、列向梯度值、第一对角线方向梯度值以及第二对角线方向梯度值相对于最小梯度值的离散度;
步骤S104:当确定的离散度大于N倍的与其对应的最小值时,输出第一代码值;当确定的离散度小于N倍的与其对应的最小值时,输出第二代码值;其中:最小值为最小标准差值或最小梯度值,N为大于1的常数;
步骤S105:根据输出的代码值确定待判别的图像是否存在边界以及所存
在的边界方向。
本公开提供的图像的边界判别方法,可以基于亮度分量也可以基于色度分量,对接收到的待判别的图像信息进行处理,得到以待处理的子像素单元对应的灰阶参数值为中心的n×n矩阵和(n+2)×(n+2)矩阵,采用梯度、标准差以及离散度等参数对n×n矩阵和(n+2)×(n+2)矩阵分别对应的图像进行判别,能够有效的确定待判别图像是否存在边界,以及边界存在的方向。可以根据判别的结果,选择正确的滤波器,提高滤波器对图像的处理效果,进而提高显示面板的显示效果。
如图2所示,图2为本公开实施例提供的n×n矩阵和(n+2)×(n+2)矩阵第一种分布示意图,为了便于理解本公开提供的判别方法,下面将结合图2对本公开提供的判别方法进行解释说明,图示为3×3矩阵和5×5矩阵,其中:Y3,3代表待处理的子像素单元对应的灰阶参数值。
上述步骤S102:分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:待处理的子像素单元对应的灰阶参数值所在的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值中的最小梯度值,以及行向标准差值、列向标准差值、第一对角线方向标准差值、和第二对角线标准差值中的最小标准差值,具体包括:
确定在n×n矩阵和(n+2)×(n+2)矩阵中:待处理的子像素单元对应的灰阶参数值所在的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值中的最小梯度值,每一方向上梯度值为该方向上相邻两个灰阶参数值的绝对值之和:其中:G代表梯度值,x代表方向(本方案中的方向为行向或列向),Yxi代表x方向上的第i个灰阶参数值,n代表行向或列向灰阶参数值的个数(实际上是x方向上的灰阶参数值的个数,但本公开中行向和列向个数相同);
以图2所示矩阵为例,在3×3矩阵中:
在5×5矩阵中:
水平方向梯度值:
竖直方向梯度值:
第一对角线方向梯度值:
第二对角线方向梯度值:
比较n×n矩阵中的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值,得到第一最小梯度值,比较(n+2)×(n+2)矩阵中的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值得到第二最小梯度值;
如图2中,假设比较后的结果为第一最小梯度值为GV9,第二最小梯度值为GLD25。
根据公式:确定在n×n矩阵和(n+2)×(n+2)矩阵中:待处理的子像素单元对应的灰阶参数值所在的行向标准差值、列向标准差值、第一对角线方向标准差值和第二对角线方向标准差值,其中:S代表标准差值,x代表方向,Yxi代表x方向上的第i个灰阶参数值,代表x方向上的灰阶参数值的平均值,n代表行向或列向灰阶参数值的个数,
以图2所示矩阵为例,在3×3矩阵中:
水平方向标准差值:
竖直方向标准差值:
第一对角线方向标准差值:
第二对角线方向标准差值:
在5×5矩阵中:
水平方向标准差值:
竖直方向标准差值:
第一对角线方向标准差值:
第二对角线方向标准差值:
比较n×n矩阵中的行向标准差值、列向标准差值、第一对角线方向标准差值和第二对角线方向标准差值,得到第一最小标准差值,比较(n+2)×(n+2)矩阵中的行向标准差值、列向标准差值、第一对角线方向标准差值和第二对角线方向标准差值得到第二最小标准差值。
以图2所示矩阵为例,假设比较后的结果为第一最小标准差值为SH9,第二最小标准差值为SRD25。
在步骤S103:分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:行向标准差值、列向标准差值、第一对角线方向标准差值以及第二对角线方向标准差值相对于最小标准差值的离散度,以及行向梯度值、列向梯度值、第一对角线方向梯度值以及第二对角线方向梯度值相对于最小梯度值的离散度,具体包括:
根据公式: 确定在n×n矩阵和(n+2)×(n+2)矩阵中:行向标准差值、列向标准差值、第一对角线方向标准差值以及第二对角线方向标准差值相对于最小标准差值的离散度,以及行向梯度值、列向梯度值、第一对角线方向梯度值以及第二对角线方向梯度值相对于最小梯度值的离散度,其中:当H、V、LD、RD分别代表n×n矩阵中的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值时,A代表第一最小梯度值,Dif代表第一离散度;当H、V、LD、RD分别代表n×n矩阵中的行向标准差值、列向标准差值、第一对角线方向标准差值和第二对角线方向标准差值时,A代表第一最小标准差值,Dif代表第二离散度;当H、V、LD、RD分别代表(n+2)×(n+2)矩阵中的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值时,A代表第二最小梯度值,Dif代表第三离散度;当H、V、LD、RD分别代表(n+2)×(n+2)矩阵中的行向标准差值、列向标准差值、第一对角线方向标准差值和第二对角线方向标准差值时,A代表第二最小标准差值,Dif代表第四离散度。
以图2所示矩阵为例,第一离散度:
第二离散度:
第三离散度:
第四离散度:
步骤S104中,当确定的离散度大于N倍的与其对应的最小值时,输出
第一代码值,当确定的离散度小于N倍的与其对应的最小值时,输出第二代码值,最小值为最小标准差值或最小梯度值,具体包括:
若Dif>N×A,对应地输出第一代码值,若Dif<N×A,对应地输出第二代码值,其中:N为大于1的常数,当Dif代表第一离散度时,A代表第一最小梯度值;当Dif代表第二离散度时,A代表第一最小标准差值;当Dif代表第三离散度时,A代表第二最小梯度值;当Dif代表第四离散度时,A代表第二最小标准差值。
以图2所示矩阵为例,判别过程为:第一离散度Dif>NGV9,对应地输出第一代码值,第一离散度Dif<NGV9,对应地输出第二代码值;
第二离散度Dif>NSH9,对应地输出第一代码值,第二离散度Dif<NSH9,对应地输出第二代码值;
第三离散度Dif>NGLD25,对应地输出第一代码值,第三离散度Dif<NGLD25,对应地输出第二代码值;
第四离散度Dif>NSRD25,对应地输出第一代码值,第四离散度Dif<NSRD25,对应地输出第二代码值。
通过多次试验可知,N值为1.4~2,0范围内的值时,本公开提高的图像边界判别方法的准确度较高。
在步骤S105中:根据输出的代码值确定待判别的图像是否存在边界以及所存在的边界方向具体包括:
当第一离散度、第二离散度、第三离散度以及第四离散度对应的输出代码值均为第二代码值时,则确定待判别的图像无边界;
当第一离散度、第二离散度、第三离散度以及第四离散度对应的输出代码值均为第一代码值时,则确定待判别的图像存在边界,且在第二梯度值和第二标准差值中最小的一个值对应的方向上存在边界;
当第一离散度、第二离散度以及第四离散度对应的输出代码值为第一代码值、第三离散度对应的输出代码值为第二代码值时,则确定待判别的图像存在边界,且在第一梯度值和第一标准差值中最小的一个值对应的方向上存在边界;
当第一离散度、第二离散度以及第三离散度对应的输出代码值为第一代码值、第四离散度对应的输出代码值为第二代码值时,则确定待判别的图像
存在边界,且在第一梯度值和第一标准差值中最小的一个值对应的方向上存在边界;
当第一离散度和第二离散度对应的输出代码值为第一代码值、第三离散度和第四离散度对应的输出代码值为第二代码值时,则确定待判别的图像存在边界,且在第一梯度值和第一标准差值中最小的一个值对应的方向上存在边界。
以图2为例,当第一离散度、第二离散度、第三离散度以及第四离散度对应的输出代码值均为第二代码值时,则确定待判别的图像无边界;
当第一离散度、第二离散度、第三离散度以及第四离散度对应的输出代码值均为第一代码值时,则确定待判别的图像存在边界,若GLD25<SRD25,则在5×5矩阵的第一对角线方向上存在边界;若GLD25>SRD25,则在5×5矩阵的第二对角线方向上存在边界;
当第一离散度、第二离散度以及第四离散度对应的输出代码值为第一代码值、第三离散度对应的输出代码值为第二代码值时,则确定待判别的图像存在边界,若GV9<SH9,则在3×3矩阵的竖直方向上存在边界;若GV9>SH9,则在3×3矩阵的水平方向上存在边界;
当第一离散度、第二离散度以及第三离散度对应的输出代码值为第一代码值、第四离散度对应的输出代码值为第二代码值时,则确定待判别的图像存在边界,若GV9<SH9,则在3×3矩阵的竖直方向上存在边界;若GV9>SH9,则在3×3矩阵的水平方向上存在边界;
当第一离散度和第二离散度对应的输出代码值为第一代码值、第三离散度和第四离散度对应的输出代码值为第二代码值时,则确定待判别的图像存在边界,若GV9<SH9,则在3×3矩阵的竖直方向上存在边界;若GV9>SH9,则在3×3矩阵的水平方向上存在边界。
可选的,第一代码值为1,第二代码值为0。当然第一代码值和第二代码值也可以为其它数值,只要可以区分开第一代码和第二代码即可。
下面将结合具体亮度参数值对本公开提供的图像的边界判别方法进行详细说明:
如图3所示,图3为本公开实施例提供的n×n矩阵和(n+2)×(n+2)矩阵第二种分布示意图;
在3×3矩阵中:
比较上述梯度值可知,最小第一梯度值为第二对角线方向梯度值。
在5×5矩阵中:
水平方向梯度值:
竖直方向梯度值:
第一对角线方向梯度值:
第二对角线方向梯度值:
比较上述梯度值可知,最小第二梯度值为竖直方向梯度值。
在3×3矩阵中:
水平方向标准差值:
竖直方向标准差值:
第一对角线方向标准差值:
第二对角线方向标准差值:
比较上述标准差可知,第一最小标准差为第二对角线方向标准差。
在5×5矩阵中:
水平方向标准差值:
竖直方向标准差值:
第一对角线方向标准差值:
第二对角线方向标准差值:
比较上述标准差可知,第二最小标准差为竖直方向标准差。
第一离散度:
第二离散度:
第三离散度:
第四离散度:
图4为本公开实施例提供的n×n矩阵和(n+2)×(n+2)矩阵第三种分布示意图。
在3×3矩阵中:
比较上述梯度值可知,最小第一梯度值为第一对角线方向梯度值。
在5×5矩阵中:
水平方向梯度值:
竖直方向梯度值:
第一对角线方向梯度值:
第二对角线方向梯度值:
比较上述梯度值可知,最小第二梯度值为第一对角线方向梯度值。
在3×3矩阵中:
水平方向标准差值:
竖直方向标准差值:
第一对角线方向标准差值:
第二对角线方向标准差值:
比较上述标准差可知,第一最小标准差为第一对角线方向标准差。
在5×5矩阵中:
水平方向标准差值:
竖直方向标准差值:
第一对角线方向标准差值:
第二对角线方向标准差值:
比较上述标准差可知,第二最小标准差为第一对角线方向标准差。
第一离散度:
第二离散度:
第三离散度:
第四离散度:
需要说明的是,上述方法适用于任何子像素排布的图像。
如图5所示,图5为本公开实施例提供的图像的边界判别装置结构示意图;本公开还提供的图像的边界判别装置,包括:
接收模块1,用于接收待判别的图像信息中每个子像素单元的灰阶参数分量值信息以形成灰阶参数值矩阵,并以待处理的子像素单元对应的灰阶参数值为中心将灰阶参数值矩阵划分为n×n矩阵和(n+2)×(n+2)矩阵,其中n行向或列向代表灰阶参数值的个数,且n为大于1的奇数,灰阶参数为亮度参数或色度参数;
第一确定模块2,用于分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:待处理的子像素单元对应的灰阶参数值所在的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值中的最小梯度值,以及行向标准差值、列向标准差值、第一对角线方向标准差值、和第二对角线标准差值中的最小标准差值;
第二确定模块3,用于分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:行向标准差值、列向标准差值、第一对角线方向标准差值以及第二对角线方向标准差值相对于最小标准差值的离散度,以及行向梯度值、列向梯度值、第一对角线方向梯度值以及第二对角线方向梯度值相对于最小梯度值的离散度;
分析模块4,用于在确定的离散度大于N倍的与其对应的最小值时,输出第一代码值,当确定的离散度小于N倍的与其对应的最小值时,输出第二
代码值,其中:最小值为最小标准差值或最小梯度值,N为大于0的常数;
判断模块5,用于根据输出的代码值确定待判别的图像是否存在边界以及所存在的边界方向。
本公开还提供了一种显示面板,包括上述图像的边界判别装置,基于上述判别装置的优点,本公开提供的显示面板,具有较好的显示效果。
显然,本领域的技术人员可以对本公开进行各种改动和变型而不脱离本发明的精神和范围。这样,倘若本公开的这些修改和变型属于本发明权利要求及其等同技术的范围之内,则本发明也意图包含这些改动和变型在内。
本申请要求于2014年8月21日递交的中国专利申请第201410415432.9号的优先权,在此全文引用上述中国专利申请公开的内容以作为本申请的一部分。
Claims (10)
- 一种图像的边界判别方法,包括:接收待判别的图像信息中每个子像素单元的灰阶参数分量值信息以形成灰阶参数值矩阵,并以待处理的子像素单元对应的灰阶参数值为中心将所述灰阶参数值矩阵划分为n×n矩阵和(n+2)×(n+2)矩阵,其中n代表行向或列向灰阶参数值的个数,且n为大于1的奇数,所述灰阶参数为亮度参数或色度参数;分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:待处理的子像素单元对应的灰阶参数值所在的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值中的最小梯度值,以及行向标准差值、列向标准差值、第一对角线方向标准差值、和第二对角线标准差值中的最小标准差值;分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:行向标准差值、列向标准差值、第一对角线方向标准差值以及第二对角线方向标准差值相对于最小标准差值的离散度,以及行向梯度值、列向梯度值、第一对角线方向梯度值以及第二对角线方向梯度值相对于最小梯度值的离散度;当确定的离散度大于N倍的与其对应的最小值时,输出第一代码值;当确定的离散度小于N倍的与其对应的最小值时,输出第二代码值,其中:所述最小值为最小标准差值或最小梯度值,N为大于1的常数;根据输出的代码值确定待判别的图像是否存在边界以及所存在的边界方向。
- 如权利要求1所述的图像的边界判别方法,其中,所述分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:待处理的子像素单元对应的灰阶参数值所在的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值中的最小梯度值,以及行向标准差值、列向标准差值、第一对角线方向标准差值、和第二对角线标准差值中的最小标准差值,包括:确定在n×n矩阵和(n+2)×(n+2)矩阵中:待处理的子像素单元对应的灰阶参数值所在的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值中的最小梯度值,每一方向上梯度值为该方向上相邻两个灰阶参数值的绝对值之和:其中:G代表梯度值,x代表方向,Yxi代表x方向上的第i个灰阶参数值,n代表行向或列向灰阶参数值的个数;比较n×n矩阵中的行向梯度值、列向梯度值、第一对角线方向梯度值和 第二对角线方向梯度值,得到第一最小梯度值,比较(n+2)×(n+2)矩阵中的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值得到第二最小梯度值;根据公式:确定在n×n矩阵和(n+2)×(n+2)矩阵中:待处理的子像素单元对应的灰阶参数值所在的行向标准差值、列向标准差值、第一对角线方向标准差值和第二对角线方向标准差值,其中:S代表标准差值,x代表方向,Yxi代表x方向上的第i个灰阶参数值,代表x方向上的灰阶参数值的平均值,n代表行向或列向灰阶参数值的个数,比较n×n矩阵中的行向标准差值、列向标准差值、第一对角线方向标准差值和第二对角线方向标准差值,得到第一最小标准差值,比较(n+2)×(n+2)矩阵中的行向标准差值、列向标准差值、第一对角线方向标准差值和第二对角线方向标准差值得到第二最小标准差值。
- 如权利要求2所述的图像的边界判别方法,其中,所述分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:行向标准差值、列向标准差值、第一对角线方向标准差值以及第二对角线方向标准差值相对于最小标准差值的离散度,以及行向梯度值、列向梯度值、第一对角线方向梯度值以及第二对角线方向梯度值相对于最小梯度值的离散度,包括:根据公式:
- 如权利要求3所述的图像的边界判别方法,其中,所述当确定的离 散度大于N倍的与其对应的最小值时,输出第一代码值,当确定的离散度小于N倍的与其对应的最小值时,输出第二代码值,所述最小值为最小标准差值或最小梯度值,包括:若Dif>N×A,对应地输出第一代码值,若Dif<N×A,对应地输出第二代码值,其中:N为大于1的常数,当Dif代表第一离散度时,A代表第一最小梯度值;当Dif代表第二离散度时,A代表第一最小标准差值;当Dif代表第三离散度时,A代表第二最小梯度值;当Dif代表第四离散度时,A代表第二最小标准差值。
- 如权利要求4所述的图像的边界判别方法,其中,所述N值为1.4~2.0。
- 如权利要求4~6任一项所述的图像的边界判别方法,其中,所述根据输出的代码值确定待判别的图像是否存在边界以及所存在的边界方向包括:当第一离散度、第二离散度、第三离散度以及第四离散度对应的输出代码值均为第二代码值时,则确定待判别的图像无边界;当第一离散度、第二离散度、第三离散度以及第四离散度对应的输出代码值均为第一代码值时,则确定待判别的图像存在边界,且在第二梯度值和第二标准差值中最小的一个值对应的方向上存在边界;当第一离散度、第二离散度以及第四离散度对应的输出代码值为第一代码值、第三离散度对应的输出代码值为第二代码值时,则确定待判别的图像存在边界,且在第一梯度值和第一标准差值中最小的一个值对应的方向上存在边界;当第一离散度、第二离散度以及第三离散度对应的输出代码值为第一代码值、第四离散度对应的输出代码值为第二代码值时,则确定待判别的图像存在边界,且在第一梯度值和第一标准差值中最小的一个值对应的方向上存在边界;当第一离散度和第二离散度对应的输出代码值为第一代码值、第三离散度和第四离散度对应的输出代码值为第二代码值时,则确定待判别的图像存在边界,且在第一梯度值和第一标准差值中最小的一个值对应的方向上存在边界。
- 如权利要求7所述的图像的边界判别方法,其中,所述第一代码值 为1,所述第二代码值为0。
- 一种图像的边界判别装置,包括:接收模块,用于接收待判别的图像信息中每个子像素单元的灰阶参数分量值信息以形成灰阶参数值矩阵,并以待处理的子像素单元对应的灰阶参数值为中心将所述灰阶参数值矩阵划分为n×n矩阵和(n+2)×(n+2)矩阵,其中n代表行向或列向灰阶参数值的个数,且n为大于1的奇数,所述灰阶参数为亮度参数或色度参数;第一确定模块,用于分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:待处理的子像素单元对应的灰阶参数值所在的行向梯度值、列向梯度值、第一对角线方向梯度值和第二对角线方向梯度值中的最小梯度值,以及行向标准差值、列向标准差值、第一对角线方向标准差值、和第二对角线标准差值中的最小标准差值;第二确定模块,用于分别确定在n×n矩阵和(n+2)×(n+2)矩阵中:行向标准差值、列向标准差值、第一对角线方向标准差值以及第二对角线方向标准差值相对于最小标准差值的离散度,以及行向梯度值、列向梯度值、第一对角线方向梯度值以及第二对角线方向梯度值相对于最小梯度值的离散度;分析模块,用于当确定的离散度大于N倍的与其对应的最小值时,输出第一代码值,当确定的离散度小于N倍的与其对应的最小值时,输出第二代码值,其中:所述最小值为最小标准差值或最小梯度值,N为大于1的常数;判断模块,用于根据输出的代码值确定待判别的图像是否存在边界以及所存在的边界方向。
- 一种显示面板,包括:如权利要求9所述的图像的边界判别装置。
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US9607396B2 (en) | 2017-03-28 |
EP3185238B1 (en) | 2020-08-19 |
CN104200793A (zh) | 2014-12-10 |
EP3185238A4 (en) | 2018-01-24 |
CN104200793B (zh) | 2016-09-07 |
US20160371848A1 (en) | 2016-12-22 |
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