JP2877372B2 - Color image signal evaluation method - Google Patents

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for evaluating a color image signal obtained from a color imaging device made of, for example, a semiconductor.

"Prior art" A mosaic color filter or a stripe color filter is mounted on the front surface of an imaging device made of a semiconductor,
Various color imaging devices capable of obtaining a color image signal have been put to practical use.

An image sensor made of a semiconductor is made by an integrated circuit technology, but various defects occur due to defects in the manufacturing process. Especially when assembled as a color image sensor
Color shading such as shading shown in FIG. 7, large diagonals shown in FIGS. 8 and 9, and vertical stripes shown in FIG.

In other words, the shading shown in FIG. 7 refers to color unevenness in a state where a screen which should be white is colored over a wide area of the screen (this is indicated by reference numeral 1).

The large diagonal shown in FIGS. 8 and 9 refers to a phenomenon in which stripes 2 and 3 colored diagonally occur on a screen that should be white.

The vertical stripes shown in FIG. 10 indicate a phenomenon in which stripes 4 occur in the vertical direction.

Conventionally, a color image signal is projected on a color cathode-ray tube, and this screen is visually monitored and detected.

[Problems to be Solved by the Invention] Conventionally, color unevenness occurring in a color image sensor is artificially determined, so that the efficiency is low. Particularly, in a mass production factory, a large number of inspectors must be arranged, which is disadvantageous in that it goes against labor saving and does not lead to cost reduction.

In addition, if the color unevenness clearly appears among the defects, it can be detected without individual difference, but if the color unevenness appears only thinly, it may be overlooked due to the individual difference of the inspector.

An object of the present invention is to provide a color image signal evaluation method capable of automatically detecting color unevenness occurring in a color image signal obtained from a color image sensor or the like.

[Means for Solving the Problems] The present invention is characterized in that the following processing is performed to evaluate color unevenness of a color image signal.

A process of converting color image data into chroma image data, a process of obtaining a histogram of chroma image data to obtain the highest frequency value of the most frequent portion, and a process wherein the value of the chroma image data is a predetermined ratio of the highest frequency value A process of extracting a color at the same position as a pixel position having a value that is twice or more, and setting a value of a corresponding pixel position to 1 and a process of obtaining a color identification matrix by storing other values as 0; Histogram flattening processing, and integration for each array in the X and Y directions on the color identification matrix to obtain an integration count and XL / YL when the image size is XL or YL with respect to the X direction integration count. A process of normalizing by multiplying by YL; a process of weighting the integration counts in the X and Y directions of the color identification matrix of each color with emphasis on the center of the screen; A process of integrating the values of the pixel positions after the above histogram flattening for each color in each direction, and obtaining the result as an integrated saturation, removing the noise by applying the integrated saturation to a low-pass filter and removing the center of the screen. Weighting processing, processing for obtaining the corresponding integral saturation value at the position where the integral count is maximum as P, and processing of the value of P toward both sides of the position where the integral saturation value is P. A process of obtaining a position at which the ratio is multiplied by a predetermined ratio and obtaining a distance therebetween as d; a process of adding the integrated saturation between the obtained positions and obtaining the added value as S; calculated as S _dp and process for obtaining the S _dp in a state other than a predetermined length at a time, in a state other than a predetermined length at a time standard deviation from both sides about the location of the integration count memory from obtaining a count value at that time as S _dpc And management, and calculation processing for obtaining the _{A = S × S d ÷ S} dp ÷ S dpc, the process of calculating the ^{A = A ÷ d 2 × {} 0.5 (1 + cos (dh · d))} 2, S dpc predetermined When the value is equal to or less than A, A = A {0.5 (1-cos ( _dc.Sdpc )} ^{J. When} d is equal to or less than a predetermined value, A = A ｛0.5 (1-cos (dw D)) a process of calculating｝ ^K , and this process is performed for each color, and the average of all the colors is set as a center stripe pattern degree, and the quality of a color image signal is evaluated according to the magnitude of the average value. It proposes an image signal evaluation method.

According to the color image signal evaluation method of the present invention, it is possible to quantitatively calculate a color unevenness stripe pattern that appears particularly in the center of a color image.

As a result, a uniform inspection without variation can be performed as compared with the inspection for visual color unevenness.

Embodiment An embodiment of the color image signal evaluation method of the present invention will be described with reference to FIGS.

The color image signal can be separated into monochromatic signals of red, green and blue. 1A, 1B and 1C show respective monochromatic image data sources.

The monochromatic image data output from the monochromatic image data sources 1A, 1B, 1C is converted by the converting means 2 into hue image data H
, Lightness image data L and saturation image data S. Well-known HLS is used as the conversion means 2 for performing this conversion.
A conversion method or an HSV conversion method can be used.

Reference numerals 3H, 3L, and 3S denote memories for storing hue image data H, lightness image data L, and saturation image data S, respectively.

“Hue” is stored as a numerical value in the hue image memory 3H. That is, the hue image data is given as an angle with a numerical value from 0 to 360. The relationship between the color name and the number is red 0-3
0, 330-360, yellow 30-90, green 90-150, cyan 150-210,
Blue 210-270, magenta 270-330.

“Color vividness” is stored in the saturation image memory 3S, and “brightness” is numerically stored in the brightness image memory 3L.

The saturation image data S obtained in the saturation image memory 3S is read out by the arithmetic means 4, and the arithmetic means 4 obtains a histogram to determine the value of the most frequent part (hereinafter referred to as mode).
Get.

The color at the same position as the position of the pixel where the value of the saturation image data is greater than or equal to a predetermined ratio (for example, 0.5 or more) of the mode is checked, and the corresponding color identification matrices 6R, 6G, 6B, 6
The value of the same position on Y, 6C, and 6M is set to 1. Others are set to 0. Each of the color identification matrices 6R, 6G, 6B, 6Y, 6C, and 6M is configured by a memory having a storage capacity corresponding to the number of imaging elements.

The histogram obtained by the arithmetic means 4 is provided to the flattening means 5, where the histogram is flattened. The flattening process indicates that the process is performed such that the average number of pixels per unit saturation is constant over the entire screen. This flattening process enhances the contrast by vivid colors.

The saturation image data flattened and enhanced by the flattening means 5 is stored in the enhancement saturation image memories 7R, 7G, 7B, 7
Written to Y, 7C, 7M. This enhanced saturation image memory 7R-7
Similarly to the color identification matrices 6R to 6M, M is configured by a memory having a storage capacity equal to or greater than the number of pixels of the image sensor.

The color identification matrices 6R to 6M are integrated in the X direction and the Y direction, and are taken into registers 8X and 8Y shown in FIG. Register 8X
And the integral value taken into 8Y is called an integral count. Change the size of the image to the result of the integration count in the X direction.
If XL and YL are used, XL / YL is multiplied, and the result is normalized to remove the influence of the difference in image size.

X direction and Y on color identification matrices 6R-6M for each color
The directional integration count is weighted as shown in FIG. For this weighting, for example, a 1/8 portion of the length from both ends of the integral count taken in the registers 8X and 8Y is weighted with a part of the cos function, and the center of the screen is emphasized.

The position where the maximum value of the integral count in the X direction and the Y direction of each color identification matrix 6R to 6M is obtained.

Registers 9X and 9Y are provided in the emphasized saturation image memories 7R to 7M, and the values after the flattening of the pixel positions in which the values of the color identification matrix stored in the emphasized saturation image memories 7R to 7M are 1 are provided in the registers 9X and 9Y. Is integrated in the X and Y directions for each of the six colors, and the result is stored. The integral values stored in the registers 9X and 9Y will be referred to as integral saturation. The X-direction integral saturation stored in the register 9X is multiplied by XL / YL to perform normalization to eliminate the influence of the screen size.

Let P be the corresponding integral saturation value at the position where the integral count in the registers 8X and 8Y is at a maximum. Here, the integral saturation of each color and each direction is weighted as shown in FIG. This weighting curve is stored in each enhanced saturation image memory.
This is a modification of the cos function with the length of 7R to 7M as one cycle.

Further, the integrated chroma data stored in the registers 9X and 9Y is subjected to a low-pass filter having a length of 3, for example, to reduce noise. (The low-pass filter of length 3 is the integrated chroma data stored in registers 9X and 9Y as shown in Fig. 5.
Calculation of three adjacent saturation data D ₋₁ , D ₀ , D ₊₁ among D ₁ , D ₂ , D _3, ..., D _n , for example, 0.25D ₋₁ + 0.5D ₀ + 0.25D ₁ , And that value is defined as one data D _AVM, and this is continuously performed for all data D _{1 to} D _n . ) Data D _{AVL to} D _AVN obtained by filtering in this manner are data in which values extremely different from other data values are erased, and become equivalent to data passed through a low-pass filter.

A position where the value of the maximum value P is multiplied by a predetermined ratio, for example, 0.5 times, is detected toward both sides of the position where the value of the integrated saturation is the maximum value P. If a position that becomes 0.5 times this value cannot be detected, the ends of the registers 9X and 9Y are set as the position.

The distance between two positions that is 0.5 times the maximum value P is the sixth
Let d be as shown in the figure.

The integrated saturation between the two positions is added, and this added value is represented by S
And

 If d is d = 0, S = 0.

The standard deviation of the integrated chroma data stored in the registers 9X and 9Y is obtained in a state where, for example, a length of 1/8 is removed from both ends. This result is defined as _Sd .

The standard deviation relating to the position of the integration count stored in the registers 8X and 8Y is obtained in a state where, for example, a length of 1/16 is removed from both sides, and this result is set as S _dp . The count at that time is _defined as S _dpc .

S × S _d ÷ S _dp ÷ S _dpc is calculated, and the result is set to A.

A = A ÷ d ² × ｛0.5 (1 + cos (dh ・ d))｝ ² is calculated. _Dh = 2π / XL Here, if S _dpc is, for example, S _dpc <2000, then A = A ｛0.5 (1−cos (dc · S _dpc )｝ ^J is calculated.
However, dc = π / 2000, and J can be set to, for example, J = 5.

Further, if d is, for example, d <10, A = A {0.5 (1-cos (dw.d))} ^K is calculated.

However, dw = 2π / 2 × 10 and K can be, for example, K = 10.

Are performed for the X direction, the Y direction, the integral count, and the integral saturation for each of the six colors.

12 results are obtained. The average value of the twelve data is defined as the center stripe pattern degree.

[Effects of the Invention] As described above, according to the present invention, a striped pattern degree can be obtained quantitatively.

Therefore, for example, the quality of the color image sensor can be determined mechanically, and a uniform product can be obtained.

Also, since no manpower is required, labor saving can be achieved and cost reduction can be expected.

Although a low-pass filter having a length of 3 is used in the above-described embodiment, the length can be appropriately selected. Although a part of cos is used as the weighting function, a linear function or another function can be used.

[Brief description of the drawings]

FIG. 1 is a block diagram for explaining an embodiment of the present invention, FIG. 2 is a block diagram for explaining an example of a color identification matrix shown in FIG. 1, and FIGS. FIG. 5 is a graph illustrating an example of a weighting function used in the embodiment of FIG. 5, FIG. 5 is a diagram illustrating the configuration of a filter used in the present invention, FIG. 6 is a graph illustrating an operation of the present invention, FIG. FIG. 10 to FIG. 10 are front views for explaining the types of color unevenness. 1A, 1B, 1C: monochromatic image data source, 2: HLS conversion means, 3H: hue image memory, 3L: brightness image memory, 3S: saturation image memory, 4: arithmetic means, 5: flattening means, 6R ~ 6M: Color identification matrix, 7R-7M: Enhanced saturation image memory, 8X, 8Y, 9
X, 9Y: Register

Claims (1)

(57) [Claims] 1. A process for converting color image data into chroma image data; B. a process for obtaining a histogram of chroma image data to obtain the highest frequency value of the most frequent portion; A color identification matrix is extracted by extracting a color at the same position as a pixel position where the value of the data is equal to or more than a predetermined ratio of the highest frequency value, setting the value of the corresponding pixel position to 1 and setting the others to 0. D. a process of performing histogram flattening on the chroma image data; and E. integration for each array in the X and Y directions on the color identification matrix to obtain an integration count and integration in the X direction. When the image size is XL or YL with respect to the count, the image size is normalized by multiplying by XL / YL; and F. Weighting that emphasizes the center of the screen with respect to the X and Y direction integration counts of the color identification matrix of each color Processing to be performed and G. each Integrating the values after the histogram flattening at the pixel position where the value of the identification matrix is 1 in each direction for each color, and obtaining the result as integrated saturation; H. applying the integrated saturation to a low-pass filter; A process of removing noise and performing weighting with emphasis on the center of the screen; I. a process of obtaining the corresponding integral saturation value at the position where the above integral count is maximum as P; A process of obtaining a position that is a predetermined ratio of the value of P toward both sides of the position of P and obtaining a distance therebetween as d; K. Adding the integrated saturation between the obtained positions, and calculating the added value Processing to obtain S; L. processing to obtain S _dp with the standard deviation of the integral saturation memory removed from both ends by a predetermined length; and M. standard deviation relating to the position of the integral count memory by a predetermined length from both sides. determined as the S _dp in the state The count value at that time as the processing obtained by the _{S dpc, NA = S × S} d ÷ S dp ÷ S and arithmetic processing for obtaining the ^{_{dpc, OA = A ÷ d 2}} × {0.5 (1 + cos (dh · d))} 2 And P. When S _dpc is less than or equal to a predetermined value, A = A {0.5 (1−cos (dc · S _dpc )} ^J. In the case of, A = A {0.5 (1-cos (dw · d))} ^k , and the above processing is performed for each color, and the average of all of them is set as the center stripe pattern degree. Characteristic color image signal evaluation method.