# Research on Quality Inspection Method of Digital Aerial Photography Results

Xiaojun Wang, Yanling Li, Yong Liang, Yanwei Zeng

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Xiaojun Wang, Yanling Li, Yong Liang, Yanwei Zeng. Research on Quality Inspection Method of Digital Aerial Photography Results. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. pp.392-399, 10.1007/978-3-642-18336-2_48. hal-01562780

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# Research on Quality Inspection method of Digital Aerial Photography
## Results

WANG Xiaojun¹, LI Yanling¹, LIANG Yong¹, Zeng Yanwei²

1.School of Information Science & Engineering, Shandong Agricultural University,
Taian, Shandong 271018, P. R. China; 2.State Supervision and Testing Center of
Surveying and Mapping Product; 198 2nd Section Renmin Road; Chengdu 610081;
China
liyanling@sdau.edu.cn

**Abstract** : Photogrammetry is the main access to obtain geospatial information, the quality of the results will directly affect the follow-up results of the quality of surveying and mapping. Therefore, We need a comprehensive aerial photographic quality inspection results. Correct evaluation of the quality of digital images is an important but difficult to solve the research topic. Many of the current image quality evaluation mainly relies on the subjective judgments. With the rapid development of image acquisition and processing technology and people's increasing demand for image information, It has become increasingly important economic significance and practical value to research the objective evaluation method that can accurately reflect the subjective evaluation. In this paper, I analyzed the traditional methods and several cutting-edge methods of the digital image quality evaluation. According to the analysis and comparison of image quality evaluation methods, I forecast the development of the image quality evaluation methods. the methodology, based on modulation transfer function combined HVS model evaluation methods for image quality evaluation is expected to achieve breakthroughs in difficult problems. With the development of the image analysis and processing technology, especially the development of computer vision and intelligence technology, the difficulty of image quality evaluation also will be resolved.

**Keywords** : digital aerial photography results, quality evaluation, modulation transfer function, human visual system, gray prediction error, visual interests

## 1. Introduction

With the rapid development of surveying and mapping, computer technology and CCD technology progress, aviation digital camera has become the main technical means to control the high resolution and precision aerial remote sensing and the aerial technology without contral, digital aviation photography has obvious advantages. In the process of the informatization surveying and mapping system construction, as an important foundation geographical data source, digital aviation photography will be more and more. The real-time data processing and automation of informatization surveying and mapping requests the automation and rapid response of quality inspection and assessment, and it is a challenge to how to check digital aviation photography results.

The content of aerial photography results quality is extensive, and it needs for the 100% inspection of data quality, flight quality, image quality and annex quality. Now it needs for a long time to use manual inspection method to check aerial photography results quality, and the efficiency is low. Digital aviation photography results also provide print pictures, but the picture is small, the overlap is big, and the number is big. so the manual inspection efficiency is lower. But digital aviation photography provides data, so it makes it possible to use the computer to check the quality comprehensively and carefully.

To sum up above all, in order to adapt to the needs of informatization construction of surveying and mapping system, basic surveying and mapping and post-disaster reconstruction, it needs to study the quality inspection methods of digital aviation photography results. This project aims to research and
analysis aerial photography quality inspection results based on current technology, research and establish digital aviation photography results quality inspection methods, and explore the flight quality, computer automatic quality inspection method. So it lays the foundation for the development of practical "digital aviation photography results quality inspection system" software

# 2. The traditional image quality inspection methods

## 2.1 Subjective evaluation method

According to some observers' assessment scales or their experience, The quality scores are given. All scores take averages and it is the subjective quality evaluation. Two kinds of observers are included, some are experienced experts, others are normal, because their experience and angle of concern is different, finally comprehensive consideration of the evaluation result is more persuasive.

Subjective evaluation includes two kinds of measurement scales, namely absolute scale and relative scale. So-called absolute scale is giving a score according to the quality of images, and the relative scale is giving a score according to the comparison of some image.

<table><caption>Subjective quality assessment standards</caption><thead><tr><td>level</td><td>absolute scale</td><td>relative scale</td></tr></thead><tbody><tr><td>1</td><td>very good</td><td>the best</td></tr><tr><td>2</td><td>good</td><td>More than the average</td></tr><tr><td>3</td><td>general</td><td>the average</td></tr><tr><td>4</td><td>poor</td><td>Below the average</td></tr><tr><td>5</td><td>very poor</td><td>the worst</td></tr></tbody></table>

Subjective evaluation method can better reflect the quality of images, but it excessively affects the knowledge background, emotion, motive and fatigue etc. From engineering point of view, this kind of method is too time-consuming and is not described by mathematics model, so that it does not facilitate computer calculation.

## 2.2 Objective evaluation method

Using algorithm for image quality evaluation. This method is convenient, quick, easy to realize and combined with the application system, but it is different from subjective feelings. Below are some typical image evaluation methods.

### 2.2.1 variance

Variance is a statistics which reflects the whole image gray-scale distribution. The variance is larger, the contrast is larger, Conversely, if the variance is smaller, the contrast is smaller. Computation formula is as follows

$$ \sigma^2 = \frac{1}{M \cdot N} \sum_{i=0}^{M-1} \sum_{j=0}^{N-1} (f(i, j) - \mu)^2 . \quad (1) $$

M and N are the row and the column of the image, f(i, j) is the gray value, and μ is the grayscale average.

### 2.2.2 Average gradient
Average gradient refers to reflect the degree of contrast images subtle. Computation formula is as follows

$$ \sigma^2 = \frac{1}{M \cdot N} \sum_{i=0}^{M-1} \sum_{j=0}^{N-1} (f(i, j) - \mu)^2 . \qquad (2) $$

M and N are the row and the column of the image,

$f(i, j)$ is the gray value,

$\nabla_i f(i, j)$ is the gradient in row direction,

and $\nabla_j f(i, j)$ is the gradient in column direction. Generally speaking, the average gradient is more bigger, the image is more clearer and contrast is good, but gradient gradient is affected greatly by noise.

### 2.2.3 Information entropy

Information entropy reflects image information abundance extent from the angle of information. According to the Shannon information entropy principle, Computation formula is as follows

$$ H(X) = - \sum_{i=0}^{L-1} P_i \log(P_i) \qquad (3) $$

L is the biggest grayscale level and $P_i$ is the probability of the grayscale which value is i.

### 2.2.4 Mean-square error

MSE is a statistics which reflects image gray-scale between the original image and the transformation image. Computation formula is as follows

$$ MSE = \frac{1}{M \cdot N} \sum_{i=0}^{M-1} \sum_{j=0}^{N-1} (\hat{f}(i, j) - f(i, j))^2 . \qquad (4) $$

MSE is Mean-square error, $f(i, j)$ is the gray value of the original image, $\hat{f}(i, j)$ is the gray value of the results image.

### 2.2.5 Peak value signal-to-noise ratio

PSNR is mainly used in evaluating image quality changes through compression, transmission and treatment, its nature is like mean-square error. Computation formula is as follows

$$ PSNR = 10 \log_{10} \frac{256 \times 256}{MSE} . \qquad (5) $$

MSE is Mean-square error.

The information entropy can be used to compare information changes when images are compressed or
processed, but it is not appropriate to consider it as the standard of image quality. Because the information of the same remote sensing images in the same area but in different time is different.

MSE and PSNR can reflect the overall difference between the original image and the restored image, and can not reflect the local case like a bigger gray difference and a small gray difference among more pixels. Obviously all pixels can not be treated the same, thus it can not reflect the human visual characteristics.

# 3. Several new evaluation methods

## 3.1 Method based on modulation transfer function

Method based on modulation transfer function is objective, can transfer and is easy to measure, which is a comprehensive image quality evaluation method accord with evaluation standard. steps are as follows

Use edge extraction technology to obtain blade and determine the scope and direction of blade in the digital image.

Do re-sampling of blade and filter.

Get line diffusion function through derivation of blade brightness curve and obtain its equivalent width.

Obtain modulation transfer function through fourier transform of line diffusion function.

Generally speaking, the low frequency part of modulation function decides image contrast, the high frequency part besides image details and clear expression ability. Therefore, we always hope modulation transfer function curve containing area that is bigger is better, thus we can use envelope area of the transfer function to judge image quality.

## 3.2 Method based on human visual system

It is a method that meets human visual system. The method is added up to some stage results of human visual system and its evaluation results can reflect people's subjective feeling. The typical model includes three significant characteristics, namely the visual non-linear characteristics, the visual resolution sensitivity bandpass characteristics, the visual multi-channel structure and cover effect.

## 3.3 Method based on gray prediction error

DPCM (differential pulse code modulation) system is a classical image linear coding system. It includes two basic premises. One is that images can be considered to be a smooth airport and its correlation function has nothing to do with pixel position, another is that image has high correlation in the local area, so image grey value of a point can be estimated through adjacent pixel grey value. This evaluation method is that comparing real and estimated values to get the image evaluation results.

Set $f'(m,n)$ as the estimate of pixel, $e(m,n)$ as estimated deviation. If use third-order prediction, then

$$ \hat{f}(m,n)=a_{1}f(m,n-1)+a_{2}f(m-1,n-1)+a_{3}(m-1,n). \qquad (6) $$

$$ e(m,n) = f(m,n) - \hat{f}(m,n). \qquad (7) $$

According to the principle of least squares estimate and separation characteristics of image correlation function, we can get coefficients of gray estimate function.
<figure><img src="image_2.png" /></figure>

$$a_1 = \frac{R(0,1)}{R(0,0)} \cdot a_2 = \frac{R(1,1)}{R(0,0)} \cdot a_3 = \frac{R(1,0)}{R(0,0)} \qquad (8)$$

If coefficient values can be obtained, we can get the gray prediction deviation of images.

$$\hat{\sigma}_e = \sqrt{\frac{1}{M \cdot N} \sum_{m=0}^{M-1} \sum_{n=0}^{N-1} [e(m, n)]^2} \qquad (9)$$

Average variance of deviation accumulation can be used to evaluate the quality.

### 3.4 Method based on fuzzy measure

The uncertainty of digital images contains both randomness of imaging process and ambiguity of their own. In gray fuzzy measure, using the method of fuzzy entropy, and fuzzy entropy reflects the overall image information and pixel gray average. In space fuzzy measure, method to calculate the area, perimeter, a degree of index and the image information of fuzzy sets, which reflects the geometrical characteristics of the image's fuzzy degree.

Such as gray measure method, at present the most influential method is the fuzzy entropy algorithm. It can reflect the average fuzzy degree of fuzzy sets pixel. A

two-dimensional image which grayscale is L and size is M x N can be considered to be a fuzzy array, and the membership of each element stands for the brightness degree compared to brightness levels.

Research shows that we can use S function to take the gray value of image space domain to mapping the eigenvalues of the fuzzy feature plane, and S function is defined as follows
$$P_{i,j} = S(x_{i,j}, a, b, c) = \begin{cases} 0 & x_{i,j} \le a \\ 2\left(\frac{x_{i,j} - a}{c-a}\right)^2 & a \le x_{i,j} \le b \\ 1 - 2\left(\frac{c - x_{i,j}}{c-a}\right)^2 & b \le x_{i,j} \le c \\ 1 & x_{i,j} \ge c \end{cases} \quad (10)$$

$b = (a+c)/2$ is A boundary, and if $x_{i,j} = b$, then $P_{i,j} = 0.5$. $\Delta = c - b = b - a$ is bandwidth of mapping function.

### 3.5 Method based on visual interest

People in the observation and understanding the image are often interested in some areas, and these areas is called areas of interest. The whole image visual quality often depends on the quality of areas of interest and sometimes the drop quality of the area that is not interested in is not perceived. Method based on visual interest reflects interest degree of the area of interest through the weighted average for different areas.

Set only one interest area can be included in test image which is A1, and its area is S1. Areas that are not to be interested which is A2, and its area is S2. The total area is $S=S1+S2$. Computation formula is as follows

$$IMSE = \frac{1}{S} \left[ \lambda_1 \sum_{(i,j) \in A_1} (f_{ij} - f'_{ij})^2 + \lambda_2 \sum_{(i,j) \in A_2} (f_{ij} - f'_{ij})^2 \right] \quad (11)$$

$\lambda_1$ is the weighted value of A1 and $\lambda_2$ is the weighted value of A2, and meet the equation $\lambda_1 * S_1 + \lambda_2 * S_2 = S$. The weighted value is bigger and the interest degree of the area is bigger. The formula of $\lambda_1$ and $\lambda_2$ as follows

$$\lambda_2 = 1 - \frac{2k}{S} \sqrt{S_1(S - S_1)}. \quad (12)$$

$$\lambda_1 = \frac{S}{S_1}(1 - \lambda_2) + \lambda_2. \quad (13)$$

$k \in [0,1]$ is adjustment factor, and it reflects the emphasis of the human eye to those areas that are not to be interested. We can see that if $S1=0$ then $\lambda_2 = 1$ and if $S2=0$ then $\lambda_1 = 1$ from the equation, at this time IMSE degenerate into MSE. Traditional objective evaluation methods are the exception of this method. It can better reflect the subjective visual.

Method based on visual interest can better reflect the subjective visual, but this method is still in the initial stage of study, there are still many problems to rearsh. Such as, determine the areas of interest, how to do if the test image contains multiple areas of interest, and how to determine the weights of some areas etc.
## 4. Conclusions

Firstly, the progress of visual perception biological psychology experiment is the basic research of image quality evaluation, the human visual system (HVS) is based on this. A large number of experiments show that people's knowledge in visual perception is very poor.

Secondly, Continue to study the HVS model. The mature HVS model makes us not to find an analytical formula to simulate human visual process. But common evaluation model means easy to calculate like square error. It requires us to fully consider visual physiological and psychological tests and to use the latest mathematical tools for research.

Thirdly, method based on modulation transfer function combined with the HVS model is expected to solve problems of image quality evaluation. With the development of image analysis and processing technology, especially the development of computer visual and intelligent technology, the image quality evaluation difficulties will become a breakthrough.

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