CN104378625B - Based on image details in a play not acted out on stage, but told through dialogues brightness JND values determination method, the Forecasting Methodology of area-of-interest - Google Patents

Abstract

The invention discloses a method for measuring a JND (Just? Noticeable? Difference: just noticeable difference) value of an image dark field brightness based on an area of interest. The method of the present invention firstly converts the original image into the xyY space, performs linear compression on the component Y according to different compression factors, and improves the dark field brightness of the original image to obtain a group of test images; then conducts a visual perception experiment to find out at least one test image The image is used as the JND critical image; then, according to the weight map of the region of interest of the original image, the JND value of the dark field brightness of the image based on the region of interest is calculated. The invention also discloses a method for predicting the JND value of the dark field brightness of the image based on the region of interest, using 0.20cd/ ^m2 as the predicted value of the JND of the dark field brightness of the image based on the region of interest. The present invention can quantitatively evaluate the impact on the dark field brightness of an image that can be perceived by human eyes when improving display parameters, especially the parameter design for dark field brightness and contrast ratio, so as to provide a basis for the design and research of display technology.

Description

Measurement method and prediction method of image dark field brightness JND value based on region of interest

technical field

The present invention relates to a method for measuring the JND (Just Noticeable Difference) value of dark field brightness of an image, in particular to a method for measuring the JND value of dark field brightness of an image based on an area of interest.

Background technique

Display technology can not only realize accurate, intuitive, clear and fast information output, but also provide a friendly interface for man and machine to exchange information for the information age. Market research shows that display image quality is one of the most important determinants for end users to purchase electronic display devices. Therefore, the evaluation of information display quality is an important content in the field of display technology. Since all displayed information is observed by the observer through human eyes, people's judgments on image quality are different for different environments and uses. Therefore, the human factor must be considered in the evaluation of display image quality. In recent years, with the rapid increase in the number and diversity of various display technologies, the research on display quality evaluation theory based on human visual system is very necessary.

With the advent of the information age, people's requirements for display quality are increasing day by day. New technologies and new applications may change observers' evaluation of display image quality. To this end, Engeldrum introduced the "image quality loop" model, which links the image display quality that consumers care about with the technical parameters of the display system through some intermediate steps. Consumers' subjective perception of image quality is the weighted sum of the observed image quality attributes. These image quality attributes include sharpness, color richness, brightness, image uniformity, etc., which are evaluated unconsciously by the observer. The subjective image quality attributes are then linked to the physical properties of the image. These image physical characteristics include optical and electrical characteristics that can be measured by measuring instruments, such as output brightness, color gamut size, display white point, gamma value, noise level, etc. Ultimately, with an in-depth understanding of display physics, these image physics can be linked to the technical parameters of the display system.

Existing display devices or related technologies such as compression coding cannot achieve perfect image display quality, and there are always unsatisfactory defects of one kind or another. Since there are often interactions between the various technical parameters that affect the final display quality, the cost required to improve each parameter is also different, and the cost is a problem that must be considered in actual production. Therefore, the research on improving the display quality needs to consider the weights and trade-offs of various factors. Sometimes some image quality impairments cannot be detected by the human eye, and even if efforts can be made to eliminate such defects, consumers will not actually feel the improvement in image quality. The introduction of JND can effectively solve these problems. Since the consumer's subjective perception of image quality is the weighted sum of the observed image quality attributes, the establishment of an image quality evaluation model using JND as a unified unit of image attributes can provide a theoretical basis for the industrial design of display systems to guide How to maximize the image quality perceived by consumers with limited investment.

Contrast is one of the important factors affecting the subjective display quality, expressed as the ratio of the white luminance of the display to the black luminance. Improving the contrast can be solved by increasing the brightness in the bright field and reducing the brightness in the dark field. However, increasing the brightness is very difficult and leads to increased power consumption. The easiest way to improve the contrast is to reduce the brightness of the dark field. However, the existing dark-field luminance JND measurement technology is difficult to apply to any natural image, for example, DICOM [Digital Imaging and Communications in Medicine (DICOM), Standard-PS3.14-2003 [S], Part14:GrayscaleStandardDisplayFunction, (2003), p21-27] standard The human eye's perception threshold of brightness changes under different brightness conditions is released, but it is carried out under strict observation conditions, and a test chart of uniform color blocks is used instead of a natural image. Xia Jun et al [Just-noticeable-difference of influential factors of subjective image quality, Xia Jun, Qin Shaoling, Liu Lu, Yin Hanchun, Journal of Southeast University (Natural Science Edition), 36(5), 2006 , p695-699] have also done a preliminary study on the JND of dark field brightness, only selected two natural images, and the experimental method used is the paired comparison method, which can only give a rough interval value of dark field brightness JND.

Contents of the invention

The technical problem to be solved by the present invention is to overcome the deficiencies in the prior art, and provide a method for measuring the JND value of the dark field brightness of an image based on the region of interest. When the JND value is measured, the brightness influence of the region of interest is fully considered, and it is a quantitative evaluation method. The effect on the dark field brightness of the image that can be detected by the human eye provides a new way.

The present invention specifically adopts the following technical solutions to solve the above technical problems:

The method for determining the JND value of image dark field brightness based on the region of interest comprises the following steps:

Step A, process the original image according to the following method to obtain the test image:

Step A1, converting the original image to a linear space through gamma correction;

Step A2, converting the image processed in step A1 from RGB space to xyY space through XYZ space;

Step A3, in the xyY space, keeping other components unchanged, linearly compressing the component Y according to a group of different compression coefficients distributed between 0.983-1;

Step A4, converting the linearly compressed series of images from xyY space back to RGB space to obtain a set of test images with different compression factors;

Step B, using the group of test images with different compression factors to carry out visual perception experiments, find out at least one test image as the JND critical image; meanwhile, according to the area of interest of the JND critical image by the subjects of the visual perception experiment, Create a new binarized image of the same size as the JND critical image, assign the pixel of the region of interest to 1, and assign the pixel of the remaining region to 0, to obtain the binarized image of the subject's region of interest;

Step C, repeat step B for the subjects of different visual perception experiments, and average the binarized images of the regions of interest obtained by each subject to obtain the weight map of the region of interest of the original image;

Step D, according to the gamma curve of the display, calculate the image dark field brightness JND value of this original image according to the following formula:

,

Among them, M and N are the number of rows and columns of the original image respectively, is the actual display brightness of the pixel in row i and column j of the original image, is the actual display luminance average value of the i-th row and j-th column pixel of each JND critical image, is the value of the pixel in row i and column j in the weight map of the region of interest.

The group of different compression coefficients distributed between 0.983-1 can be distributed linearly or non-linearly. In order to ensure the difference between pixels at the same position of different test images, the present invention preferably takes values according to the following formula:

, where, Indicates the compression factor, is the gamma value of the display, A set of values equally spaced from 1 to 40. Preferably, A set of values equally spaced by 1 from 1 to 40.

Preferably, the visual perception experiment uses a ladder method combined with a binomial forced selection method.

The present invention further uses the above method to measure the JND value of the dark field brightness of a large number of original images with different contents, and takes the obtained JND value of the dark field brightness as the dependent variable, the image content as the independent variable, and the subject as a random variable, Do analysis of variance. The results show that the image content has no significant influence on the JND value of the dark field brightness based on the region of interest, and the JND values of the image dark field brightness measured by images with different contents are all about 0.20cd/ ^m2 . According to this rule, the present invention based on Prediction method of JND value of image dark field brightness in the region of interest: 0.20cd/m ² is used as the JND prediction value of dark field brightness of the image based on the region of interest. Therefore, the JND value can be directly used in the design and research process of the display technology, greatly saving manpower and time costs.

The present invention overcomes the disadvantage of being unable to quantitatively evaluate the impact on image display quality when improving the dark field brightness and contrast related parameters of the display. The present invention is based on the method for measuring the JND value of the dark field brightness of the image in the region of interest, and can quantitatively evaluate the parameters of the improved display. The impact on the dark field brightness of the image that can be perceived by the human eye provides a basis for the design and research of display technology, and points out a new direction for the JND value measurement.

Description of drawings

Figure 1 is a schematic diagram of the process of generating a test image;

Fig. 2 is a schematic diagram of adjusting the dark field brightness of the test image;

Fig. 3 is the mean value and its 95% confidence interval of dark field brightness JND measurement based on the region of interest in the present invention.

detailed description

The technical scheme of the present invention is described in detail below in conjunction with accompanying drawing:

The present invention is based on the method for determining the JND value of the image dark field brightness of the region of interest, comprising the following steps:

Step A, process the original image according to the following method to obtain the test image:

Step A1, converting the original image to a linear space through gamma correction, in this specific embodiment, γ=2.2;

Step A2, converting the image processed in step A1 from RGB space to XYZ space;

The conversion of RGB space to XYZ space is a prior art, and its conversion formula is as follows:

(1)

in,

(Xr, Yr, Zr), (Xg, Yg, Zg) and (Xb, Yb, Zb) are the coordinates of the three primary colors of red, green and blue in XYZ space respectively, (Xw, Yw, Zw) is the white field point in XYZ space coordinate of;

Transform from XYZ space to xyY space, where the Y component does not need to be recalculated, and the x and y components are obtained according to formula (2):

x=X/(X+Y+Z),y=Y/(X+Y+Z)(2)

Step A3, in the xyY space, keeping other components unchanged, linearly compressing the component Y according to a group of different compression coefficients distributed between 0.983-1;

Each pixel of the original image is linearly compressed to adjust the brightness of the dark field of the image. The adjustment range is determined by the compression coefficient of linear compression; the expression of linear compression is shown in formula (2), where Y _i represents The Y component of Y, Y ₀ is the Y component of the original image, is the compression factor,

Y _i = Y ₀ +1- (3)

In this specific embodiment, in order to ensure that the difference between pixels at the same position in different test images is greater than or equal to grayscale 1, The value is a non-linear distribution, , It is the gamma value of the display, and the value of β is from 1-40, with 1 as the interval of equal interval distribution;

Step A4, converting the linearly compressed series of images from the XYZ space back to the RGB space to obtain a set of test images with different compression factors.

The process of generating the test image above is shown in FIG. 1 . The dark field brightness adjustment process of the image is shown in Fig. 2 .

Step B, using the group of test images with different compression factors to carry out visual perception experiments, find out at least one test image as the JND critical image; meanwhile, according to the area of interest of the JND critical image by the subjects of the visual perception experiment, Create a new binarized image of the same size as the JND critical image, assign the pixel of the region of interest to 1, and assign the pixel of the remaining region to 0, to obtain the binarized image of the subject's region of interest;

When carrying out JND research, need utilize the visual perception experiment based on psychophysics method to determine JND critical image, the present invention preferably adopts the most commonly used ladder method in conjunction with binomial forced selection method, the basic process of this method is as follows:

Display the test chart and the original picture side by side on the screen at the same time, and let the subjects judge whether they can perceive the difference between the brightness of the test chart and the original picture; each time two pictures are displayed on the monitor at the same time, one is the reference picture (that is, the original picture ), and the other is a test chart. According to the provisions of the binomial forced choice method, the subjects need to choose which picture they think is brighter among the two pictures. At first, there was a large difference in brightness between the test picture and the reference picture, and the subjects could easily distinguish the two, that is, to correctly answer which picture was brighter. If the subject answers correctly, the difference between the next test image and the reference image will be reduced. Once the subjects answered incorrectly, they increased the brightness difference between the test picture and the reference picture. Adopt following experimental method in this embodiment mode:

Each time two images are displayed on the monitor at the same time, one is the original image and the other is the test picture, and the subject chooses which picture he thinks is brighter among the two pictures; if the subject chooses correctly, replace A test chart with a small compression coefficient; once the subject chooses a wrong test chart, replace the test chart with a large compression coefficient; repeat the above process; the initial change step of the experiment is 8, and the step length is halved to 4 after 2 inflection points , and then after 4 inflection points, the step size becomes 2, and after 6 inflection points, the change step size is reduced to 1; when the total number of inflection points when the step size is 1 reaches 6, stop; the test chart corresponding to the last 6 inflection points That is the JND critical image.

For the determined JND critical image, find out the subjective region of interest of the current subject, and then create a binarized image with the same size as the original image and the critical image according to the found region of interest, and convert it to The pixel of the region of interest is assigned a value of 1, and the pixels of the rest of the region are assigned a value of 0 to obtain a binarized image of the subject's region of interest.

Step C. Repeat step B for the subjects of different visual perception experiments, and average the obtained binarized images of the region of interest of each subject, that is, the average value of the pixel values at the same position of all binarized images of the region of interest Assign the pixel at the corresponding position in a new binarized image to obtain the weight map of the region of interest of the original image.

Step D, calculate the image dark field brightness JND value of this original image according to the gamma curve of the display;

By testing the gamma curve of the display used, the actual display brightness on the display corresponding to each gray value can be obtained. Assuming that the pixels of the original image are M×N, is the actual display brightness of the pixel in row i and column j of the original image, is the actual display luminance average value of the i-th row and j-th column pixel of each JND critical image, is the value of the pixel in row i and column j in the weight map of the region of interest, then the image dark field brightness JND value of the original image can be calculated by the following formula:

(4)

The present invention further uses the above-mentioned method to measure the JND value of dark field brightness for a large number of original images with different contents, and takes the obtained JND value of dark field brightness based on the region of interest as the dependent variable and the image content as the independent variable. As a random variable, analysis of variance was performed. The results show that the image content has no significant influence on the JND value of the dark field brightness, and the JND value of the dark field brightness of the image measured by the method provided by the present invention is about 0.20cd/m ² . Therefore, 0.20cd/m ² can be directly used as the JND prediction value of the dark field brightness of the image based on the region of interest. Therefore, the JND value can be directly used in the design and research process of the display technology, greatly saving manpower and time costs.

In order to verify the effect of the image dark field brightness JND value determination method of the present invention, carry out following experiment:

Using a Philips 19-inch LCD monitor, the white point of the display is adjusted to D65, the peak brightness of the display screen is 264cd/m ² , and the dark field brightness is 0.33cd/m ² . The observation distance during the process is 4 times the height of the screen, about 1.2m. The ambient light in the test room is set to 20lx in the vertical direction in front of the screen, and the illuminance behind the display is about 10-20lx, which is close to the actual ambient light setting of a home TV. Considering that the image content may have an impact on the JND of the image brightness, the test images used are representative to a certain extent, including images with different brightness distributions, as well as skin colors, plants, animals and other content. The method of the invention is used to measure the JND value of the image dark field brightness. The number of subjects participating in the experiment was 22, aged between 22 and 73, including 11 males and 11 females. The mean value of JND of dark field luminance JND of images with different content measured by experiment is about 0.20cd/m ² , and the variance is about 0.15cd/m ² , the value and 95% confidence interval are shown in Fig. 3 . A variance analysis was performed on the JND mean value of the dark field brightness, in which the image content was an independent variable and the subjects were random variables. The results showed that the image content had no significant influence (significance=0.20>0.05).

Claims (4)

1. The method for measuring the image dark field brightness JND value based on the region of interest is characterized by comprising the following steps of:

step A, processing an original image according to the following method to obtain a test image:

step A1, converting the original image into linear space through gamma correction;

step A2, converting the image processed in the step A1 from an RGB space to an XYZ space;

step A3, keeping other components unchanged in the xyY space, and respectively performing linear compression on the components Y according to a set of different compression coefficients distributed between 0.983-1;

step A4, converting the series of images after linear compression from the xyz space to the RGB space, obtaining a group of test images with different compression coefficients;

b, performing a visual perception experiment by using the group of test images with different compression coefficients to find out at least one test image as a JND critical image; meanwhile, according to the region of interest of the JND critical image to be tested in the visual perception experiment, a new binarization image with the same size as the JND critical image is established, the pixel of the region of interest is assigned to be 1, and the pixels of the rest regions are assigned to be 0, so that the binarization image of the region of interest to be tested is obtained;

step C, repeating the step B for the tested regions of different visual perception experiments, averaging the obtained binarized images of the regions of interest of each tested region to obtain a region of interest weight map of the original image;

d, calculating an image dark field brightness JND value of the original image according to a gamma curve of the display and the following formula:

$JND=\frac{\underset{i=1}{\overset{M}{\Σ}}\underset{j=1}{\overset{N}{\Σ}}H(i,j)\×|L_{ori}(i,j)-\stackrel{\‾}{L_{threshold}(i,j)}|}{\underset{i=1}{\overset{M}{\Σ}}\underset{j=1}{\overset{N}{\Σ}}H(i,j)},$

m, N represents the number of rows and columns, L, respectively, of the original image_ori(i, j) is the actual display brightness of the ith row and jth column pixels of the original image,and H (i, j) is the value of the ith row and jth column pixel in the interested area weight map.

2. The method for determining the dark field brightness JND value of the image based on the region of interest according to claim 1, wherein the set of different compression coefficients distributed between 0.983 and 1 is non-linearly distributed, and is specifically calculated according to the following formula:

$\mathrm{\α}=1-{\left(\frac{\mathrm{\β}}{255}\right)}^{\mathrm{\γ}},$

where α represents a compression coefficient, γ is a gamma value of the display, and β is a set of values distributed at equal intervals from 1 to 40.

3. The method for determining the dark field luminance JND value of the image based on the region of interest according to claim 2, wherein β is a set of values distributed at equal intervals of 1 from 1 to 40.

4. The method for determining the dark field brightness JND value of the image based on the region of interest according to any one of claims 1 to 3, wherein the visual perception experiment uses a staircase method in combination with a binomial approach.