# Color Image Landscape Photo Hand-Painted Effect Evaluation with PFA Algorithm

Authors: Shizhang Liu
Journal: Advances in Multimedia
 (2021)

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## Abstract

With the continuous development of society and economy, diversified presentation and publicity of landscape are becoming more and more popular among the public, such as using color pictures to display multimedia publicity videos or APP, WeChat public accounts, and other new media. There are various ways of expression. Against the limitations of color image landscape, this article introduced the PFA algorithm, during the experiment of hand-painted landscape pictures, the effect of color hand-painted images is evaluated by using the coordination degree of natural target detection scene, and the corresponding image evaluation index values are obtained for effective analysis, and the prediction model is established. The simulation results show that the PFA algorithm is effective and can support the evaluation of the hand-painted effect of color landscape photos.


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## Body

## 1. Introduction

With the continuous development of the social economy, the presentation and display of the scenery have become the focus of the work of scenic spots and local governments. For landscape publicity, multimedia methods such as color images and publicity videos can be used for publicity through WeChat public accounts, mobile apps, and official homepages [1–4]. There are many ways to acquire color images, which can be fused by imaging tools to form a color image that is visible to the naked eye [5, 6]. For different color image fusion effects, different algorithms can be used for fusion to achieve comprehensive fusion of various bands [7, 8].The evaluation of image scenery photos can often be divided into subjective and objective evaluations. The so-called subjective evaluation is to judge the quality of the photos based on the user’s subjective perception; the objective evaluation is to use the corresponding objective evaluation model to conduct human simulations to ensure objective and objective evaluation. However, it should be noted that since the human eye is the main receiver, the evaluation based on subjective perception has the advantages of intuition and visibility and can realize the validity of the inspection of image landscape photos [9, 10].Aiming at the limitations of the effect evaluation of image landscape photos, this paper introduces the PFA algorithm, sorts out the subjective evaluation test indicators of landscape photos, and uses multiple linear regression analysis methods to determine the hand-painted effect parameters of color image landscape photos, determine the influencing factors. The hand-drawn effect experiment of image landscape photos aims to explore the effectiveness of the PFA algorithm.


## 2. PFA Algorithm

For the PFA algorithm, it can be obtained by deriving the bunching of a single point target. Assuming that the rectangular coordinates of a point target in the ground area projected in the oblique plane are (x, y), then the time is between the start and the end. The distance is as shown in the following formula:(1)Rta=R0tacosθta−x2+R0tasinθta−y2.The undemodulated echo of the point target can be expressed as shown in the following formula:(2)st,ta=rectt−2Rta/cTpexpj2πfct−2Rtac+jπγt−2Rtac2,where t is the fast time, Tp is the pulse width, and γ is the distance modulation frequency. When dechirp, the spotlight mode selects the reference distance as Rc, and the reference echo expression is obtained as shown in the following formula:(3)st,Rc=rectt−2Rc/cTp+Tsexpj2πfct−2Rcc+jπγt−2Rcc2,where Ts represents the maximum value of the difference in echo delay as shown in the following formula:(4)sift,ta=st,ta⋅s∗t,Rc=rectt−2Rta/cTp⋅expj4πfc+γt−2Rc/ccRta−Rc⋅expj4πγc2Rta−Rc2,where expj4πγ/c2Rta−Rc2 is called the residual video residual term, and the existing method of eliminating residual video phase error (RVP) is used, as shown in Figure 1.Figure 1
Flow chart of eliminating residual video phase error.The reference functionsRVPf is shown in the following formula:(5)sRVPf=exp−jπf2γ,where f represents the distance frequency. The expression of the echo after eliminating RVP is shown in the following formula:(6)sttat,ta=rectt−2Rta/cTp⋅expj4πfc+γt−2Rc/ccRta−Rc.


### 2.1. Simple Color Fusion

For color fusion, it is the fusion of the color channels of the image, rather than any processing of the image, to achieve the fusion of the three primary colors ofR, G, and B.A large number of studies have shown that red is easy to recognize by the human eye. Therefore, based on this fact, the image of the image target can be mapped to the red channel first, so that the background can be easily recognized [11–13]. Since human eyes have different perceptions of the color brightness of monochromatic light, in the image spectrum of the three primary colors, it is easy to think that green is the brightest. Therefore, images containing more attribute information can be mapped to the green channel. For blue, it can reduce the visual fatigue of the human eye, so it can be followed for a longer period of time.


### 2.2. Color Fusion Based on Enhanced Processing and HSV Transformation

For enhancement processing, it is processed on the basis of the original feature attributes of the image. Its feature is to adjust the human eye's different comfort according to the cold and warm tones of the color, clarify the appropriate color image fusion method, and perform HSV at the same time. The spatial coordinates of color image are transformed to obtain the optimized value.For ordinary images, it provides a wealth of relevant background information. At the same time, the fusion of hand-drawn photos of color images can make the corresponding images relatively natural. For the image with blurred background, the eigenvalues of the original data of color image are analyzed mainly according to the original data, and the details of the infrared image are relatively clear. Different from the three primary colors ofR, G, and B, the HSV model is user-oriented, corresponding to hue, saturation, and brightness. Through reasonable analysis of the three parameters, the corresponding image fusion quality can be improved.


### 2.3. Color Fusion Based on Human Visual Characteristics

For users, the processing and mining of image information are very important, and it is extremely meaningful to use human visual characteristics to perform corresponding image fusion processing. First, the ON and OFF channels are used to enhance the three primary color images to form a neural network again. At the same time, the neural network and the RGB channel mapping are enhanced and postprocessed to form a near-natural color hand-drawn image observed by the human eye.


### 2.4. Color Fusion Based on YIQ Transform

For YIQ,Y is brightness, and I and Q, respectively, represent colors. The color vision of the human eye indicates that the human eye can distinguish the color changes of red and blue. Relying on the color fusion of the human eye neural network, the color fusion of the image can be carried out based on YIQ. It can further identify the target and locate [14–16].


## 2.1. Simple Color Fusion

For color fusion, it is the fusion of the color channels of the image, rather than any processing of the image, to achieve the fusion of the three primary colors ofR, G, and B.A large number of studies have shown that red is easy to recognize by the human eye. Therefore, based on this fact, the image of the image target can be mapped to the red channel first, so that the background can be easily recognized [11–13]. Since human eyes have different perceptions of the color brightness of monochromatic light, in the image spectrum of the three primary colors, it is easy to think that green is the brightest. Therefore, images containing more attribute information can be mapped to the green channel. For blue, it can reduce the visual fatigue of the human eye, so it can be followed for a longer period of time.


## 2.2. Color Fusion Based on Enhanced Processing and HSV Transformation

For enhancement processing, it is processed on the basis of the original feature attributes of the image. Its feature is to adjust the human eye's different comfort according to the cold and warm tones of the color, clarify the appropriate color image fusion method, and perform HSV at the same time. The spatial coordinates of color image are transformed to obtain the optimized value.For ordinary images, it provides a wealth of relevant background information. At the same time, the fusion of hand-drawn photos of color images can make the corresponding images relatively natural. For the image with blurred background, the eigenvalues of the original data of color image are analyzed mainly according to the original data, and the details of the infrared image are relatively clear. Different from the three primary colors ofR, G, and B, the HSV model is user-oriented, corresponding to hue, saturation, and brightness. Through reasonable analysis of the three parameters, the corresponding image fusion quality can be improved.


## 2.3. Color Fusion Based on Human Visual Characteristics

For users, the processing and mining of image information are very important, and it is extremely meaningful to use human visual characteristics to perform corresponding image fusion processing. First, the ON and OFF channels are used to enhance the three primary color images to form a neural network again. At the same time, the neural network and the RGB channel mapping are enhanced and postprocessed to form a near-natural color hand-drawn image observed by the human eye.


## 2.4. Color Fusion Based on YIQ Transform

For YIQ,Y is brightness, and I and Q, respectively, represent colors. The color vision of the human eye indicates that the human eye can distinguish the color changes of red and blue. Relying on the color fusion of the human eye neural network, the color fusion of the image can be carried out based on YIQ. It can further identify the target and locate [14–16].


## 3. Experimental Design of Hand-Painted Effects of Color Image Landscape Photos

For the experimental analysis of the hand-drawn effect of color image landscape photos, the analysis methods can be divided into subjective and objective aspects. Subjective methods have certain arbitrariness and limitations, but their methods are relatively simple and fast. Therefore, for those with low demand, you can use it directly; the objective evaluation method is to use objective parameters for evaluation, which is necessary.


### 3.1. Observers of Subjective Evaluation Experiments

Set the corresponding number of subjective evaluation observers. The number of subjects in this experiment is 50 people, including 24 girls and 26 boys. During the analysis and evaluation, the students were subjected to a relevant image discrimination test to ensure that they have normal vision and discrimination. It is convenient and clear to distinguish the meaning of the index, mainly from three aspects: target recognition, detailed information, and the background color of the image.The so-called target recognition is based on the more obvious the target color difference of the image, the more obvious the target, and the higher the recognition effect, efficiency, and accuracy; the detailed information of the image is mainly related to the contrast, edge, and texture of the image; the background color of the image is mainly affected by the color [17, 18].


### 3.2. Image of Subjective Evaluation Experiment

The color hand-drawn images used in the subjective evaluation test mainly include three scenic areas: green, sea and sky, and towns. There are a total of 200 pictures of all the scenic areas.


### 3.3. Subjective Evaluation Index

The subjective evaluation indicators mainly include 2 major categories and 4 secondary categories.For the same image, the evaluation results of the image hand-drawn effect obtained by different visual tasks are different, according to different image perception quality indicators:(1)
Evaluation of the Hand-Painted Effect of Color Image Landscape Photos. According to the target detection method, the better the target detection, the higher the evaluation score for the hand-painted effect.(2)
Evaluation of the Hand-Drawn Effect of Color Image Landscape Photos. According to the image definition, color coordination, and other indicators, we can quickly understand the image content of hand-painted content and then increase the image evaluation score.In order to evaluate the single performance of color hand-drawn images, four secondary categories are designed.


#### 3.3.1. Perceived Contrast

Perceived contrast is one of the basic characteristics of hand-painted photos. It is mainly to find the degree of easy discovery of the target. It has a greater impact on the quality of the hand-painted effect. If the value is too large or too small, the details of the hand-painted image will be unclear and the image quality will decrease.


#### 3.3.2. Clarity

Sharpness is an indicator for users to evaluate the quality of hand-drawn images, reflecting the clarity of the details and textures of the hand-drawn images themselves.


#### 3.3.3. Color Coordination

The color coordination is the result of the user’s satisfaction according to the color matching and proportion, which is related to the number of colors, the area occupied, and the relative position.


#### 3.3.4. The Natural Sense of Color

The naturalness of the color is the contrast degree of the fused hand-drawn image obtained by the corresponding fusion algorithm, so that the color is consistent with the color of the real scene.


### 3.4. Subjective Evaluation Experiment Process

In order to avoid visual fatigue caused by students observing images for a long time, take a break of 5 minutes every 30 minutes and divide the different secondary classifications into different parts. Each student will give a corresponding score for different hand-drawn images.


## 3.1. Observers of Subjective Evaluation Experiments

Set the corresponding number of subjective evaluation observers. The number of subjects in this experiment is 50 people, including 24 girls and 26 boys. During the analysis and evaluation, the students were subjected to a relevant image discrimination test to ensure that they have normal vision and discrimination. It is convenient and clear to distinguish the meaning of the index, mainly from three aspects: target recognition, detailed information, and the background color of the image.The so-called target recognition is based on the more obvious the target color difference of the image, the more obvious the target, and the higher the recognition effect, efficiency, and accuracy; the detailed information of the image is mainly related to the contrast, edge, and texture of the image; the background color of the image is mainly affected by the color [17, 18].


## 3.2. Image of Subjective Evaluation Experiment

The color hand-drawn images used in the subjective evaluation test mainly include three scenic areas: green, sea and sky, and towns. There are a total of 200 pictures of all the scenic areas.


## 3.3. Subjective Evaluation Index

The subjective evaluation indicators mainly include 2 major categories and 4 secondary categories.For the same image, the evaluation results of the image hand-drawn effect obtained by different visual tasks are different, according to different image perception quality indicators:(1)
Evaluation of the Hand-Painted Effect of Color Image Landscape Photos. According to the target detection method, the better the target detection, the higher the evaluation score for the hand-painted effect.(2)
Evaluation of the Hand-Drawn Effect of Color Image Landscape Photos. According to the image definition, color coordination, and other indicators, we can quickly understand the image content of hand-painted content and then increase the image evaluation score.In order to evaluate the single performance of color hand-drawn images, four secondary categories are designed.


### 3.3.1. Perceived Contrast

Perceived contrast is one of the basic characteristics of hand-painted photos. It is mainly to find the degree of easy discovery of the target. It has a greater impact on the quality of the hand-painted effect. If the value is too large or too small, the details of the hand-painted image will be unclear and the image quality will decrease.


### 3.3.2. Clarity

Sharpness is an indicator for users to evaluate the quality of hand-drawn images, reflecting the clarity of the details and textures of the hand-drawn images themselves.


### 3.3.3. Color Coordination

The color coordination is the result of the user’s satisfaction according to the color matching and proportion, which is related to the number of colors, the area occupied, and the relative position.


### 3.3.4. The Natural Sense of Color

The naturalness of the color is the contrast degree of the fused hand-drawn image obtained by the corresponding fusion algorithm, so that the color is consistent with the color of the real scene.


## 3.3.1. Perceived Contrast

Perceived contrast is one of the basic characteristics of hand-painted photos. It is mainly to find the degree of easy discovery of the target. It has a greater impact on the quality of the hand-painted effect. If the value is too large or too small, the details of the hand-painted image will be unclear and the image quality will decrease.


## 3.3.2. Clarity

Sharpness is an indicator for users to evaluate the quality of hand-drawn images, reflecting the clarity of the details and textures of the hand-drawn images themselves.


## 3.3.3. Color Coordination

The color coordination is the result of the user’s satisfaction according to the color matching and proportion, which is related to the number of colors, the area occupied, and the relative position.


## 3.3.4. The Natural Sense of Color

The naturalness of the color is the contrast degree of the fused hand-drawn image obtained by the corresponding fusion algorithm, so that the color is consistent with the color of the real scene.


## 3.4. Subjective Evaluation Experiment Process

In order to avoid visual fatigue caused by students observing images for a long time, take a break of 5 minutes every 30 minutes and divide the different secondary classifications into different parts. Each student will give a corresponding score for different hand-drawn images.


## 4. Simulation Experiment Results and Data Analysis

First, divide the color image landscape hand-drawn photos in the same landscape range into the same group (including different data processing methods), and standardize the data. The specific calculation can be obtained by the following formula:(7)zij=yij−yjminyjmax−yjmin.Among them,RGB=R+G+B/3 is the original score, yjmin and yjmax are the minimum and maximum values in the set of data, and zij is the transformed score. The scores of 52 observers are converted from the standard 0–1 and then added and averaged to obtain the final scores of the 6 evaluation indicators of the corresponding image. Then, the linear relationship between the 4 single indicators and the regression equation between the composite indicator and the 4 single indicators based on this comprehensive score are analyzed.Figure2 shows the four Pearson correlations of the FPA algorithm. It can be seen that TM and S have a strong correlation; CH and CN have a strong correlation.Figure 2
Pearson correlation coefficient of the PFA algorithm.


### 4.1. Prediction Model of Comprehensive Perceptual Quality of Plant Background

The results of TM regression analysis of plant background are shown in Figure3. At the same time, the TM regression model of plant background can be calculated by the following formula:(8)PQTD=0.907PCTB+0.129CH−0.022R2=0.958.Figure 3
TM regression model of plant background.The SE regression analysis result of plant background is shown in Figure4. It can be seen that the significance level of the evaluation index CH is less than 0.05, and the specific calculation of the SE regression model of plant background is shown in the following formula:(9)PQSU=1.044CH−0.022R2=0.980.Figure 4
The regression model of plant background for SE.


### 4.2. Comprehensive Perceived Quality Prediction Model of Haitian Background

The TM regression analysis result of the sea and sky background is shown in Figure5, and the TM regression equation of the sea and sky background is shown in the following formula:(10)PQTD=0.340PCTB+0.8385S−0.090R2=0.846.Figure 5
The regression model of Haitian background for TM.The results of SE regression analysis of Haitian background are shown in Figure6.Figure 6
The regression model of Haitian background for SE.The SE regression equation of the sea and sky background is shown in the following formula:(11)PQSU=1.010CH−0.008R2=0.985.


### 4.3. Comprehensive Perceived Quality Prediction Model for Urban Scenes

The results of TM regression analysis of urban background are shown in Figure7.Figure 7
Regression model for TM in urban context.The regression coefficients in model 2 are statistically significant, and there is no multicollinearity problem. The determination coefficientR2 indicates that PCTB and S can explain 77.7% of the TM changes. The TM regression equation of the urban scene is shown in the following formula:(12)PQTD=0.601PCTB+0.440S−0.027R2=0.777.The SE regression analysis results of the urban background are shown in Figure8, and the TM regression equation of the urban scene is shown in the following formula:(13)PQSU=0.885CH+0.150S−0.024R2=0.990.Figure 8
The regression model of urban context against SE.From the results of the above simulation experiments, the analysis of the color coordination of the color hand-drawn images shows that PFA is effective. In addition, in different landscape environments, this article differs from traditional methods in plants, sea, and sky scenes and basically consistent with traditional results in urban scenes.From the analysis results of the three types of scenes (Figure8), it can be seen that the target background perception contrast occupies a large proportion in the image perception quality TM prediction model based on target detection, and the definition also occupies a certain proportion, but the impact is small.


### 4.4. Comparison and Evaluation of Color Hand-Drawn Image and Source Image

At present, there is no ideal evaluation index between the color hand-drawn image and the source gray image. Therefore, this section compares the grayscale information of the color hand-drawn image with the source grayscale image.


#### 4.4.1. Evaluation Method Based on YIQ Transformation

YIQ transform can well separate the brightness part and the color part of a color image. The specific conversion process from RGB color space to YIQ color space is shown in the following formula:(14)YIQ=0.2990.5870.1140.596−0.275−0.3210.212−0.523−0.311RGB.Therefore, it can be obtained as shown in the following formula:(15)Y=0.299R+0.587G+0.114B.Use YIQ transform to separate the brightness information of the four color hand-drawn images in Figure1, and compare them objectively with the source image. The parameters are shown in Table 1. The fusion results 1, 2, 3, and 4 in Table 1 are a, b, c, and d in Figure 1, respectively.Table 1
Comparison of gray information between source image and YIQ transformed color hand-drawn image.
EntropyAverage target background contrastRoughnessVisible light image7.030.339.93Midwave infrared image6.855.225.72Longwave infrared image7.252.037.83Brightness part of fusion result 16.853.238.41Brightness part of fusion result 27.745.149.84Brightness part of fusion result 37.011.348.01Brightness part of fusion result 46.440.897.91


#### 4.4.2. Evaluation Method Based on Weighted Average

TheR, G, and B channels of the color hand-drawn image are processed by weighted average, and the processed results are compared objectively with the source image. The specific processing process is shown in the following formula:(16)RGB=R+G+B3.RGB1, RGB2, RGB3, and RGB4 in Figure9, respectively, represent the images after the weighted average of the R, G, and B channels of the 4 color hand-drawn images.Figure 9
Comparison of the source image and the average weighted result.It can be seen from the results that the entropy of the four fusion results is smaller than that of the source image, and the amount of information is lost to varying degrees; the average target background contrast of RGB2 is similar to that of midwave infrared, and the target is more obvious. The contrast of RGB1 and LWIR is similar, RGB3 and RGB4 are between visible light and LWIR, and the target is not obvious; the roughness of RGB2 is close to the visible light image, and the details are richer.


### 4.5. Evaluation Method among Color Hand-Drawn Images

The average target background contrast of theR component is calculated to compare whether the target is obvious, the roughness of the G component is calculated to indicate the amount of detailed information, and the entropy of the B component is calculated to indicate whether the background is rich.The data in theR, G, and B columns in Figure 10, respectively, represent the average target background contrast, roughness, and entropy of the R, G, and B three channels. Considering the contrast, roughness, and entropy, each component of the hand-drawn result 2 is larger than the other three, and the effect is ideal.Figure 10
Comparison between color fusion results.


## 4.1. Prediction Model of Comprehensive Perceptual Quality of Plant Background

The results of TM regression analysis of plant background are shown in Figure3. At the same time, the TM regression model of plant background can be calculated by the following formula:(8)PQTD=0.907PCTB+0.129CH−0.022R2=0.958.Figure 3
TM regression model of plant background.The SE regression analysis result of plant background is shown in Figure4. It can be seen that the significance level of the evaluation index CH is less than 0.05, and the specific calculation of the SE regression model of plant background is shown in the following formula:(9)PQSU=1.044CH−0.022R2=0.980.Figure 4
The regression model of plant background for SE.


## 4.2. Comprehensive Perceived Quality Prediction Model of Haitian Background

The TM regression analysis result of the sea and sky background is shown in Figure5, and the TM regression equation of the sea and sky background is shown in the following formula:(10)PQTD=0.340PCTB+0.8385S−0.090R2=0.846.Figure 5
The regression model of Haitian background for TM.The results of SE regression analysis of Haitian background are shown in Figure6.Figure 6
The regression model of Haitian background for SE.The SE regression equation of the sea and sky background is shown in the following formula:(11)PQSU=1.010CH−0.008R2=0.985.


## 4.3. Comprehensive Perceived Quality Prediction Model for Urban Scenes

The results of TM regression analysis of urban background are shown in Figure7.Figure 7
Regression model for TM in urban context.The regression coefficients in model 2 are statistically significant, and there is no multicollinearity problem. The determination coefficientR2 indicates that PCTB and S can explain 77.7% of the TM changes. The TM regression equation of the urban scene is shown in the following formula:(12)PQTD=0.601PCTB+0.440S−0.027R2=0.777.The SE regression analysis results of the urban background are shown in Figure8, and the TM regression equation of the urban scene is shown in the following formula:(13)PQSU=0.885CH+0.150S−0.024R2=0.990.Figure 8
The regression model of urban context against SE.From the results of the above simulation experiments, the analysis of the color coordination of the color hand-drawn images shows that PFA is effective. In addition, in different landscape environments, this article differs from traditional methods in plants, sea, and sky scenes and basically consistent with traditional results in urban scenes.From the analysis results of the three types of scenes (Figure8), it can be seen that the target background perception contrast occupies a large proportion in the image perception quality TM prediction model based on target detection, and the definition also occupies a certain proportion, but the impact is small.


## 4.4. Comparison and Evaluation of Color Hand-Drawn Image and Source Image

At present, there is no ideal evaluation index between the color hand-drawn image and the source gray image. Therefore, this section compares the grayscale information of the color hand-drawn image with the source grayscale image.


### 4.4.1. Evaluation Method Based on YIQ Transformation

YIQ transform can well separate the brightness part and the color part of a color image. The specific conversion process from RGB color space to YIQ color space is shown in the following formula:(14)YIQ=0.2990.5870.1140.596−0.275−0.3210.212−0.523−0.311RGB.Therefore, it can be obtained as shown in the following formula:(15)Y=0.299R+0.587G+0.114B.Use YIQ transform to separate the brightness information of the four color hand-drawn images in Figure1, and compare them objectively with the source image. The parameters are shown in Table 1. The fusion results 1, 2, 3, and 4 in Table 1 are a, b, c, and d in Figure 1, respectively.Table 1
Comparison of gray information between source image and YIQ transformed color hand-drawn image.
EntropyAverage target background contrastRoughnessVisible light image7.030.339.93Midwave infrared image6.855.225.72Longwave infrared image7.252.037.83Brightness part of fusion result 16.853.238.41Brightness part of fusion result 27.745.149.84Brightness part of fusion result 37.011.348.01Brightness part of fusion result 46.440.897.91


### 4.4.2. Evaluation Method Based on Weighted Average

TheR, G, and B channels of the color hand-drawn image are processed by weighted average, and the processed results are compared objectively with the source image. The specific processing process is shown in the following formula:(16)RGB=R+G+B3.RGB1, RGB2, RGB3, and RGB4 in Figure9, respectively, represent the images after the weighted average of the R, G, and B channels of the 4 color hand-drawn images.Figure 9
Comparison of the source image and the average weighted result.It can be seen from the results that the entropy of the four fusion results is smaller than that of the source image, and the amount of information is lost to varying degrees; the average target background contrast of RGB2 is similar to that of midwave infrared, and the target is more obvious. The contrast of RGB1 and LWIR is similar, RGB3 and RGB4 are between visible light and LWIR, and the target is not obvious; the roughness of RGB2 is close to the visible light image, and the details are richer.


## 4.4.1. Evaluation Method Based on YIQ Transformation

YIQ transform can well separate the brightness part and the color part of a color image. The specific conversion process from RGB color space to YIQ color space is shown in the following formula:(14)YIQ=0.2990.5870.1140.596−0.275−0.3210.212−0.523−0.311RGB.Therefore, it can be obtained as shown in the following formula:(15)Y=0.299R+0.587G+0.114B.Use YIQ transform to separate the brightness information of the four color hand-drawn images in Figure1, and compare them objectively with the source image. The parameters are shown in Table 1. The fusion results 1, 2, 3, and 4 in Table 1 are a, b, c, and d in Figure 1, respectively.Table 1
Comparison of gray information between source image and YIQ transformed color hand-drawn image.
EntropyAverage target background contrastRoughnessVisible light image7.030.339.93Midwave infrared image6.855.225.72Longwave infrared image7.252.037.83Brightness part of fusion result 16.853.238.41Brightness part of fusion result 27.745.149.84Brightness part of fusion result 37.011.348.01Brightness part of fusion result 46.440.897.91


## 4.4.2. Evaluation Method Based on Weighted Average

TheR, G, and B channels of the color hand-drawn image are processed by weighted average, and the processed results are compared objectively with the source image. The specific processing process is shown in the following formula:(16)RGB=R+G+B3.RGB1, RGB2, RGB3, and RGB4 in Figure9, respectively, represent the images after the weighted average of the R, G, and B channels of the 4 color hand-drawn images.Figure 9
Comparison of the source image and the average weighted result.It can be seen from the results that the entropy of the four fusion results is smaller than that of the source image, and the amount of information is lost to varying degrees; the average target background contrast of RGB2 is similar to that of midwave infrared, and the target is more obvious. The contrast of RGB1 and LWIR is similar, RGB3 and RGB4 are between visible light and LWIR, and the target is not obvious; the roughness of RGB2 is close to the visible light image, and the details are richer.


## 4.5. Evaluation Method among Color Hand-Drawn Images

The average target background contrast of theR component is calculated to compare whether the target is obvious, the roughness of the G component is calculated to indicate the amount of detailed information, and the entropy of the B component is calculated to indicate whether the background is rich.The data in theR, G, and B columns in Figure 10, respectively, represent the average target background contrast, roughness, and entropy of the R, G, and B three channels. Considering the contrast, roughness, and entropy, each component of the hand-drawn result 2 is larger than the other three, and the effect is ideal.Figure 10
Comparison between color fusion results.


## 5. Conclusions

The hand-painting of color images and landscape photos is one of the important ways and means to publicize landscapes and local recommendations. Therefore, more and more attention is paid to the effect and quality of its effects. In view of the existing limitations, this paper introduces the PFA algorithm, constructs a color image landscape photo effect evaluation model by combing the corresponding evaluation indicators, uses 50 students to evaluate the hand-painted effect experiment, and analyzes it through subjective and objective aspects. The simulation results show that the PFA algorithm is effective and can support the evaluation of the hand-painted effect of color image landscape photos.



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