Image quality evaluation system and image quality evaluation method

Abstract

To solve such a problem that previous image quality evaluation often relied on experienced professionals.SOLUTION: An image calibration device is placed in a scene and used for displaying a calibration pattern. An image capturing device captures a first contrast image of the scene and the calibration pattern. A processing device is configured to obtain the first contrast image and a reference image containing the calibration pattern and compare the first contrast image and the calibration pattern in the reference image to generate calibration information. A positioning and calibrating operation of the image capturing device is executed on the basis of the calibration information. After the positioning and calibrating operation of the image capturing device is finished, the image capturing device captures a second contrast image of the scene and the calibration pattern, and the processing device compares the second contrast image and the calibration pattern in the reference image to generate an image quality evaluation result.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an image processing system, method and device, and more particularly to an image quality evaluation system, an image quality evaluation method and an image calibration device. [Background technology]

[0002] In order to accurately evaluate the quality of the image captured by the image capture device, a complex environmental background is usually set up. For example, the environmental background may contain a variety of colorful figures, portraits, and objects. After the environmental background is set up, the image capture device is set up and the environmental background is photographed.

[0003] Generally, the quality evaluation items of an image captured by an image capture device usually include clarity, contrast, saturation, brightness, noise level, and white balance. Previously, image quality evaluation often relied on experienced experts. However, training experts takes time, and personal preferences can lead to subjective evaluation of image quality. Summary of the Invention [Problem to be solved by the invention]

[0004] The technical problem that the present invention aims to solve is to address the shortcomings of existing techniques and provide an image quality assessment system, an image quality assessment method, and an image calibration device. [Means for solving the problem]

[0005] In order to solve the above technical problems, one technical solution according to the present invention provides an image quality assessment system, which includes an image calibration device, an image capture device, and a processing device. The image calibration device is located within a scene and is used to display a calibration pattern. The image capture device photographs the scene and the calibration pattern to capture a first contrast image. The processing device acquires a reference image including the first contrast image and the calibration pattern, compares the first contrast image with the calibration pattern in the reference image to generate a comparison result, and generates calibration information based on the comparison result. A positioning and calibration operation of the image capture device is performed based on the calibration information. After completing the positioning and calibration operation of the image capture device, the image capture device photographs the scene and the calibration pattern to capture a second contrast image. The processing device is further configured to compare the second contrast image with the calibration pattern in the reference image to generate an image quality assessment result.

[0006] In order to solve the above technical problems, another technical solution according to the present invention provides an image quality assessment method, which includes: using an image capture device to capture a scene and a calibration pattern displayed by an image calibration device located in the scene to capture a first contrast image; using a processing device to obtain a reference image including the first contrast image and the calibration pattern, and comparing the first contrast image with the calibration pattern in the reference image to generate a comparison result, generating calibration information based on the comparison result, and performing a positioning calibration operation of the image capture device based on the calibration information; after completing the positioning calibration operation of the image capture device, using the image capture device to capture a second contrast image of the scene and the calibration pattern; and using a processing device to compare the second contrast image with the calibration pattern in the reference image to generate an image quality assessment result.

[0007] In order to solve the above technical problems, another technical solution according to the present invention is to provide an image calibration device, which includes a calibration pattern, which is composed of a background block, an analysis block, and a positioning block, where the analysis block and background block provide image quality information, and the positioning block provides position information, which is different from the analysis block.

[0008] One beneficial effect of the present invention is that the image quality assessment system, image quality assessment method, and image calibration device provided by the present invention can perform precise positioning and calibration operations and generate quantitative assessment scores for image quality for images captured by an image capture device in complex scenes, making image quality assessment more objective and solving the problem of camera development cycles being too long.

[0009] To further understand the features and technical contents of the present invention, please refer to the following detailed description and illustrations of the present invention, however, the illustrations provided are for reference and explanation only and are not intended to limit the present invention. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a functional block diagram of an embodiment of an image quality assessment system provided by the present invention; [Figure 2] FIG. 2 is a schematic diagram showing the positional arrangement of the image calibration device, the reference image capture device, and the processing device of FIG. 1; [Figure 3] 2 is a schematic diagram showing the positional arrangement of the image proofing device, the evaluation target image capture device, and the processing device shown in FIG. 1. FIG. [Figure 4] 1 is a schematic diagram of an embodiment of an image proofing device of the present invention; [Figure 5] 5 is a flowchart of an embodiment of an image quality evaluation method for the image proofing device applied in FIG. 4. [Figure 6] 10 is a schematic diagram of calibration information regarding the amount of center point displacement generated by the processing device. FIG. [Figure 7]FIG. 10 is a schematic diagram of calibration information relating to a rotation angle generated by the processing device. [Figure 8] FIG. 10 is a schematic diagram of calibration information relating to a scale ratio generated by the processing device. [Figure 9] FIG. 10 is a schematic diagram of calibration information regarding a camera tilt angle generated by a processing device. [Figure 10] 1 is a method flow diagram of one embodiment of a quantitative assessment of sharpness performed by a processing unit. [Figure 11] 1 is a method flow diagram of one embodiment of a quantitative assessment of white balance performed by a processing device. [Figure 12] 1 is a method flow diagram of one embodiment of a quantitative assessment of contrast performed by a processing unit. [Figure 13] 1 is a method flow diagram of one embodiment of a quantitative assessment of saturation performed by a processing unit. [Figure 14] 1 is a method flowchart of one embodiment of a quantitative assessment of image brightness performed by a processing device. [Figure 15] 1 is a method flow diagram of one embodiment of a quantitative assessment of noise level performed by a processing device. [Figure 16] This is a radar chart showing a quantitative evaluation of the image quality of two different cameras. DETAILED DESCRIPTION OF THE INVENTION

[0011] The following describes the embodiments of the "image quality evaluation system and image quality evaluation method" disclosed in the present invention through specific examples. Those skilled in the art can understand the merits and advantages of the present invention from the disclosure of this specification. The present invention can be implemented or applied in other different embodiments. Each detail in this specification can also be modified and changed equivalently based on various aspects or applications without departing from the spirit of the present invention. Furthermore, the drawings of the present invention are for simple and schematic illustration only and do not represent actual dimensions. The following embodiments will further explain the technical matters related to the present invention, but the disclosed contents do not limit the present invention.

[0012] In addition, although various components or signals may be described using terms such as "first," "second," and "third" in this specification, these components or signals are not limited by these terms. It should be understood that these terms are primarily used to distinguish one component from another component or one signal from another signal. Furthermore, the term "or" used in this specification may include any one or more combinations of related items depending on actual circumstances.

[0013] Figure 1 is a functional block diagram of an embodiment of an image quality assessment system provided by the present invention. Referring to Figure 1, the image quality assessment system includes an image calibration device 1, a reference image capture device 2, an evaluation target image capture device 3, and a processing device 4. The reference image capture device 2 and the evaluation target image capture device 3 are configured to capture images of the image calibration device 1, and the processing device 4 is electrically connected to the reference image capture device 2 and the evaluation target image capture device 3 at different time points.

[0014] The image calibration device 1 is, for example, a polygonal plate, the image capture devices 2 and 3 are, for example, monocular cameras, binocular cameras, or depth cameras, and the processing device 4 is, for example, a personal computer, a server, or a mobile terminal, but the present invention is not limited thereto.

[0015] FIG. 2 is a schematic diagram showing the positional arrangement of the image calibration device, reference image capture device, and processing device shown in FIG. 1. Referring to FIG. 2, the image calibration device 1 is placed approximately at the center of scene A. The image calibration device 1 is a rectangular plate with a calibration pattern B displayed on its surface. The calibration pattern B is composed of a background block 11, an analysis block 12, and a positioning block 13. The background block 11 and the analysis block 12 provide image quality information, such as clarity, white balance, contrast, saturation, brightness, and noise level. At least one of the color, shape, and pattern of the positioning block 13 differs from that of the analysis block 12, and the positioning block 13 provides position information, such as the coordinates and area of ​​the center point.

[0016] The reference image capture device 2 maintains a certain distance from the scene A and the image calibration device 1, and its lens is directed toward the scene A. The reference image capture device 2 is configured to photograph the scene A and the calibration pattern B to capture a reference image, which includes the scene A, the image calibration device 1, and the calibration pattern B displayed on the image calibration device 1. The processing device 4 is configured to acquire the reference image. It should be noted that the reference image capture device 2 is calibrated, and the image quality of the acquired reference image complies with specific image quality evaluation items (e.g., sharpness, contrast, saturation, brightness, noise level, white balance), and can be used as a standard for image quality evaluation.

[0017] Figure 3 is a schematic diagram showing the positional arrangement of the image calibration device, evaluation target image capture device, and processing device shown in Figure 1. Referring to Figure 3, image calibration device 1 is a rectangular plate placed approximately in the middle of scene A. The position, aperture setting, and shutter setting of evaluation target image capture device 3 are the same as those of reference image capture device 2. However, since the image quality of the image captured by evaluation target image capture device 3 may differ from the image quality of the image captured by reference image capture device 2, it is necessary to perform image quality evaluation and calibrate evaluation target image capture device 3 as necessary.

[0018] The lens of the evaluation target image capture device 3 is aimed at scene A, and the evaluation target image capture device 3 is configured to photograph scene A and calibration pattern B to capture a first contrast image. The first contrast image includes scene A, image calibration device 1, and calibration pattern B displayed on image calibration device 1. The processing device 4 is configured to acquire the first contrast image.

[0019] The calibration pattern B is composed of a background block 11, an analysis block 12, and a positioning block 13. The analysis block 12 is located approximately in the center of the calibration pattern B and includes at least three different patterns. The positioning blocks 13 are located on the periphery of the calibration pattern B, and any color displayed in the positioning block 13 is different from the color displayed in the background block 11. With this design of the calibration pattern B, after the processing device 4 acquires the reference image and the first contrast image, the processing device 4 compares the positioning block 13 of the calibration pattern B in the first contrast image with the positioning block 13 of the calibration pattern B in the reference image to generate a comparison result, and generates calibration information based on this comparison result. The calibration information may include center translation, rotation, scaling, and camera tilt. Next, based on the calibration information provided by the processing device 4, the user manually performs a positioning calibration operation on the image capture device 3 to be evaluated.

[0020] After performing the positioning and calibration operation of the evaluation object image capture device 3, the evaluation object image capture device 3 again photographs the scene A and the calibration pattern B to capture a second contrast image, and the processing device 4 acquires the second contrast image.

[0021] After the processing device 4 acquires the second contrast image, the processing device 4 compares the calibration pattern B in the second contrast image with the calibration pattern B in the reference image to generate an image quality evaluation result for the second contrast image.

[0022] If necessary, the image quality evaluation system may also include a movement mechanism 5. The movement mechanism 5 may include moving parts, rotating parts, or any combination thereof. The movement mechanism 5 is connected to the evaluation target image capture device 3. Based on the calibration information generated by the processing device 4, the processing device 4 controls the movement mechanism 5 to change the orientation and position of the evaluation target image capture device 3, and the movement mechanism 5 performs a positioning calibration operation for the evaluation target image capture device 3. This automates the positioning calibration operation and achieves higher accuracy than human operation.

[0023] If necessary, there may be a plurality of image calibration devices 1. For example, there may be five image calibration devices 1, which are arranged at the four corners and the center of scene A. This can improve the accuracy of the positioning calibration operation and image quality evaluation.

[0024] 4 is a schematic diagram of an embodiment of the image calibration device of the present invention. Referring to FIG. 4, the analysis block 12 is located near the center of the calibration pattern B, and any color displayed in the analysis block 12 is different from the color displayed in the background block 11. The analysis block 12 includes at least three different patterns, where different patterns mean that they are different in at least one of color and shape.

[0025] The analysis block 12 includes a first sub-analysis block 121, a second sub-analysis block 122, and a third sub-analysis block 123, and the first sub-analysis block 121, the second sub-analysis block 122, and the third sub-analysis block 123 differ in at least one of color and shape.

[0026] The first sub-analysis block 121 has an oblique angle and includes a pair of a first color block 1211 and a second color block 1212 of complementary colors. The first color block 1211 and the second color block 1212 are quadrilaterals. Different degrees of sharpness are generated on the hypotenuse of the first sub-analysis block 121. The alternating portions of the first color block 1211 and the second color block 1212 generate a frequency of jaggies, which allows the sharpness of the first sub-analysis block 121 to be calculated. The second sub-analysis block 122 includes third color blocks 1221A, 1221B, 1221C, 1221D of different grayscale values ​​and fourth and fifth color blocks 1222 and 1223 of complementary colors. The third sub-analysis block 123 includes sixth color blocks 1231A, 1231B, 1231C, 1231D, 1231E, and 1231F of at least six colors.

[0027] The first and second color blocks in the first sub-analysis block 121 are rectangular and are black and white, respectively. The third, fourth, and fifth color blocks in the second sub-analysis block 122 are also rectangular, with the third color blocks being different shades of gray and the fourth and fifth color blocks being black and white, respectively. The sixth color block in the third sub-analysis block 123 is also rectangular, with the sixth color blocks being pink, blue, purple, green, yellow, and orange, respectively.

[0028] Optionally, the first and second color blocks may be circular, triangular, or other polygonal, and the first and second color blocks may be red and green or yellow and purple, respectively. The fourth and fifth color blocks may also be circular, triangular, or other polygonal, and the fourth and fifth color blocks may be red and green or yellow and purple, respectively. The sixth color block may be six or more in number and six or more in color.

[0029] Through the design of the analysis block 12 in FIG. 4, image comparison of complex scenes can be realized.

[0030] The positioning block 13 is located around the calibration pattern B, and any color displayed in the positioning block 13 is different from the color displayed in the background block 11. The positioning block 13 includes a first sub-positioning block 131, a second sub-positioning block 132, a third sub-positioning block 133, and a fourth sub-positioning block 134, which are located at the upper left corner, upper right corner, lower right corner, and lower left corner of the calibration pattern B, respectively, and surround the analysis block 12. In this embodiment, the calibration pattern B, the first sub-positioning block 131, the second sub-positioning block 132, the third sub-positioning block 133, and the fourth sub-positioning block 134 are rectangular areas, but the present invention is not limited thereto.

[0031] Each of the first sub-positioning block 131, the second sub-positioning block 132, the third sub-positioning block 133, and the fourth sub-positioning block 134 includes a plurality of color blocks of different colors, and the color of any of the color blocks of the first sub-positioning block 131, the second sub-positioning block 132, the third sub-positioning block 133, and the fourth sub-positioning block 134 is different from the color of the background block 11, and the arrangement patterns of the color blocks of any of the color blocks of the first sub-positioning block 131, the second sub-positioning block 132, the third sub-positioning block 133, and the fourth sub-positioning block 134 do not overlap.

[0032] Specifically, the first sub-positioning block 131 is located at the upper left corner of the calibration pattern B, and the upper half of the first sub-positioning block 131 has two color blocks from left to right, which respectively have a first color and a second color, and the lower half of the first sub-positioning block 131 has two color blocks from left to right, which respectively have a third color and a fourth color.

[0033] The second sub-positioning block 132 is located in the upper right corner of the calibration pattern B. The upper half of the second sub-positioning block 132 has two color blocks from left to right, which respectively have the second color and the first color, and the lower half of the second sub-positioning block 132 has two color blocks from left to right, which respectively have the fourth color and the third color.

[0034] The third sub-positioning block 133 is located in the lower right corner of the calibration pattern B. The upper half of the third sub-positioning block 133 has two color blocks from left to right, which are the fourth color and the third color, respectively. The lower half of the third sub-positioning block 133 has two color blocks from left to right, which are the first color and the second color, respectively.

[0035] The fourth sub-positioning block 134 is located in the lower left corner of the calibration pattern B, and the two color blocks from left to right in the upper half of the fourth sub-positioning block 134 have the third color and the fourth color, respectively, and the two color blocks from left to right in the lower half of the fourth sub-positioning block 134 have the second color and the first color, respectively.

[0036] The first color, second color, third color, and fourth color are, for example, red, green, blue, and black, but the present invention is not limited to this and can be any color different from the color of the background block 11.

[0037] Through the design of the positioning block 13 in FIG. 4, image positioning of complex scenes can be realized.

[0038] 5 is a flowchart of an embodiment of the image quality evaluation method for the image calibration device 1 applied in FIG. 4. Referring to FIG. 5, in step S501, the processing device 4 positions the reference image capture device 2 to photograph scene A and calibration pattern B to capture the reference image, so as to obtain a reference image. In step 502, the processing device 4 positions the evaluation target image capture device 3 to photograph scene A and calibration pattern B to capture the first contrast image, so as to obtain a first contrast image.

[0039] In step S503, the processing device 4 is configured to analyze the positioning block 13 in the reference image and the first contrast image to obtain the reference position information and the first position information, respectively. Specifically, the processing device 4 performs a binarization process on the reference image and the first contrast image to obtain the contours of the positioning block 13 in the reference image and the first contrast image.

[0040] In step S504, the processing device 4 is configured to compare the first position information with the reference position information to generate a comparison result, and to generate calibration information for the image capture device 3 to be evaluated based on the comparison result.

[0041] In step S505, the processing device 4 is configured to perform a positioning and calibration operation of the evaluation target image capture device 3 based on the calibration information. Specifically, the positioning and calibration operation of the evaluation target image capture device 3 is performed by the movement mechanism 5.

[0042] In step S506, the processing device 4 is configured to determine whether the positioning calibration operation has been successfully performed. If successful, proceed to step S507. If not, return to step S503. Specifically, the success or failure of the positioning calibration operation is determined based on a user-defined tolerance range. If the difference in distance between the center position of the calibration pattern in the first contrast image after calibration and the center position of the calibration pattern in the reference image is within the tolerance range, the positioning calibration operation is deemed successful; otherwise, it is deemed unsuccessful.

[0043] In step S507, the processing device 4 arranges the evaluation target image capture device 3 to capture the second contrast image by photographing the scene A and the calibration pattern B. In step S508, the processing device 4 arranges the processing device 4 to compare the analysis block 12 in the second contrast image with the analysis block 12 in the reference image and obtain a comparison result.

[0044] In step S509, the processing device 4 is arranged to perform a quantitative analysis of the image quality of the second contrast image based on the comparison between the second contrast image and the reference image.

[0045] In step S510, the processing device 4 is configured to determine whether the quantitative analysis of image quality has been successfully performed. If successful, the process proceeds to step S511. If not, the process returns to step S503. Specifically, the success or failure of the quantitative analysis of image quality is determined based on a user-defined lower limit of the evaluation score. If the evaluation score is equal to or greater than the lower limit of the evaluation score, the quantitative analysis of image quality is deemed successful; otherwise, it is deemed unsuccessful.

[0046] In step S511, the processing device 4 is arranged to generate an image quality assessment result of the second contrast image.

[0047] Through the image quality evaluation method of Figure 5, even in complex scenes, accurate positioning and calibration operations can be performed on the image capture device 3 to be evaluated, and accurate quantitative evaluation can be made of the image quality of the image captured by the image capture device 3 to be evaluated.

[0048] FIG. 6 is a schematic diagram illustrating how the processing device 4 generates calibration information for the center point shift amount. As shown in FIG. 6, the intersection of the first line L1 and the second line L2 is the first center point P1 of the calibration pattern B in the reference image K, and the intersection of the third line L3 and the fourth line L4 is the second center point P2 of the calibration pattern B in the first contrast image M. The processing device 4 acquires the coordinates of the first center point P1 and the second center point P2, calculates the center point shift amount between the first center point P1 and the second center point P2, and provides the calibration information for the center point shift amount. This calibration information for the center point shift amount includes an indication of the movement distance and movement direction D1 of the image capture device 3 to be evaluated.

[0049] Specifically, the processing device 4 performs a binarization process on the reference image K and the first contrast image M to find the contours of rectangular objects in the reference image K and the first contrast image M. Next, the processing device 4 determines whether each rectangular object contains color blocks of the four defined colors. If so, the rectangular object is marked as a sub-positioning block. Accordingly, the processing device 4 marks the first sub-positioning block 131, the second sub-positioning block 132, the third sub-positioning block 133, and the fourth sub-positioning block 134 in the reference image K and the first contrast image M, and finds the positioning center points of the first sub-positioning block 131, the second sub-positioning block 132, the third sub-positioning block 133, and the fourth sub-positioning block 134. Next, the first line L1 and the second line L2 are defined based on the four positioning center points in the reference image K, and the third line L3 and the fourth line L4 are defined based on the four positioning center points in the first contrast image M.

[0050] FIG. 7 is a schematic diagram illustrating how the processing device 4 generates calibration information for the rotation angle. As shown in FIG. 7, the fifth line L5 and the sixth line L6 of the reference image K are parallel to each other, and the seventh line L7 and the eighth line L8 of the first contrast image M are also parallel to each other. The fifth line L5 and the seventh line L7 intersect to form a first angle G1, and the sixth line L6 and the eighth line L8 intersect to form a second angle G2. The processing device 4 calculates the average value of the first angle G1 and the second angle G2 to define the degree to which the calibration pattern B of the first contrast image M is rotated relative to the calibration pattern B of the reference image K. Ideally, the first angle G1 is equal to the second angle G2. However, in practice, there is a slight difference between the first angle G1 and the second angle G2. Therefore, the average value of the first angle G1 and the second angle G2 is defined as the rotation angle. Finally, the processing device 4 provides rotation angle calibration information, which includes a rotation angle indication and a rotation direction indication D2 of the image capture device 3 to be evaluated.

[0051] Specifically, the fifth line L5 and the sixth line L6 are defined based on four positioning center points in the reference image K, and the seventh line L7 and the eighth line L8 are defined based on four positioning center points in the first contrast image M.

[0052] FIG. 8 is a schematic diagram illustrating how the processing device 4 generates scale calibration information. As shown in FIG. 8, the processing device 4 acquires the areas of the first sub-positioning block 131, the second sub-positioning block 132, the third sub-positioning block 133, and the fourth sub-positioning block 134 in the reference image K and the first contrast image M, and then adds up the areas of the first sub-positioning block 131, the second sub-positioning block 132, the third sub-positioning block 133, and the fourth sub-positioning block 134 in the reference image K to calculate an average value, thereby generating a first area average value. The processing device 4 then adds up the areas of the first sub-positioning block 131, the second sub-positioning block 132, the third sub-positioning block 133, and the fourth sub-positioning block 134 in the first contrast image M to calculate an average value, thereby generating a second area average value. The processing device 4 calculates the ratio between the second area average value and the first area average value to calculate the scale, thereby providing scale calibration information. This scale calibration information includes a movement direction indication D3 of the image capture device 3 to be evaluated.

[0053] 9 is a schematic diagram of how the processing device 4 generates the calibration information for the camera tilt angle. As shown in FIG. 9, the processing device 4 calculates the area ratios of the first sub-positioning block 131, the second sub-positioning block 132, the third sub-positioning block 133, and the fourth sub-positioning block 134 in the reference image K and the first contrast image M to calculate the camera tilt angle and provide the calibration information for the camera tilt angle. The calibration information for the camera tilt angle includes a movement direction indication D4 of the image capture device 3 to be evaluated.

[0054] 10 is a flowchart of an example method for the processing device 4 to perform a quantitative evaluation of sharpness. Referring to FIGS. 4 and 10, in step S1001, the processing device 4 analyzes the modulation transfer functions (MTFs) of the first sub-analysis blocks in the reference image and the second contrast image to obtain a reference modulation transfer function and a contrast modulation transfer function, respectively. In step S1002, the processing device 4 calculates the deviation percentage of the contrast modulation transfer function from the reference modulation transfer function. In step S1003, the processing device 4 generates a quantitative evaluation result of the sharpness of the second contrast image based on the deviation percentage.

[0055] For example, if the deviation percentage of the contrast image transfer function from the reference modulation transfer function is less than 5%, the quantitative evaluation score of the clarity of the second contrast image is 7 to 10 points. If the deviation percentage of the contrast modulation transfer function from the reference modulation transfer function is within the range of 5% to 10%, the quantitative evaluation score of the clarity of the second contrast image is 4 to 6 points. If the deviation percentage of the contrast modulation transfer function from the reference modulation transfer function is within the range of 10% to 15%, the quantitative evaluation score of the clarity of the second contrast image is 1 to 3 points. If the deviation percentage of the contrast modulation transfer function from the reference modulation transfer function is more than 15%, the quantitative evaluation score of the clarity of the second contrast image is 0 points.

[0056] FIG. 11 is a flowchart of a specific embodiment of a method for quantitatively evaluating white balance by the processing device 4. Referring to FIGS. 4 and 11, in step S1101, the processing device 4 analyzes color space values ​​in the background blocks of the reference image and the second contrast image to obtain contrast color space values ​​and reference color space values, respectively. Specifically, the color space values ​​are CIELAB color space values ​​or CIELCH color space values. In step S1102, the processing device 4 calculates the deviation percentage of the contrast color space values ​​relative to the reference color space values. In step S1103, the processing device 4 generates a quantitative evaluation result of the white balance of the second contrast image based on the deviation percentage.

[0057] FIG. 12 is a flowchart of an example method for the processing device 4 to perform a quantitative evaluation of contrast. Referring to FIGS. 4 and 12, in step S1201, the processing device 4 analyzes the grayscales of each color block of the second sub-analysis block of the reference image and the second contrast image to obtain a plurality of contrast grayscales and a plurality of reference grayscales, respectively. In step S1202, the processing device 4 calculates a plurality of deviation percentages between the plurality of contrast grayscales and the plurality of reference grayscales. In step S1203, the processing device 4 calculates an average value of the plurality of deviation percentages. In step S1204, the processing device 4 generates a quantitative evaluation result of the contrast of the second contrast image based on the average value of the plurality of deviation percentages.

[0058] FIG. 13 is a flowchart of an example method by which the processing device 4 performs a quantitative evaluation of color saturation. Referring to FIGS. 4 and 13, in step S1301, the processing device 4 analyzes the modulation transfer functions of the third sub-analysis blocks of the reference image and the second contrast image to obtain a reference modulation transfer function and a contrast modulation transfer function, respectively. In step S1302, the processing device 4 calculates the deviation percentage of the contrast image modulation transfer function from the reference image modulation transfer function. In step S1303, the processing device 4 generates a quantitative evaluation result of the color saturation of the second contrast image based on the deviation percentage.

[0059] 14 is a flowchart of an example method for the processing device 4 to perform a quantitative evaluation of image brightness. Referring to FIGS. 4 and 14, in step S1401, the processing device 4 analyzes the average grayscale of all color blocks of the reference image and the second contrast image. In step S1402, the processing device 4 calculates the deviation percentage of the average grayscale of the second contrast image relative to the average grayscale of the reference image. In step S1403, the processing device 4 generates a quantitative evaluation result of the image brightness of the second contrast image based on the deviation percentage.

[0060] 15 is a flowchart of an embodiment of a method for quantitatively evaluating the noise level by the processing device 4. Referring to FIGS. 4 and 15, in step S1501, the processing device 4 analyzes the peak signal-to-noise ratios of the second sub-analysis blocks of the reference image and the second contrast image to obtain the peak signal-to-noise ratio of each contrast and the reference peak noise ratio. In step S1502, the processing device 4 calculates the deviation percentage of the peak signal-to-noise ratio of the contrast of the second contrast image from the reference peak noise ratio. In step S1503, the processing device 4 generates a quantitative evaluation result of the noise level of the second contrast image based on the deviation percentage.

[0061] Using the quantitative analysis methods for the six image quality items shown in Figures 10 to 15, the processing device 4 can obtain quantitative scores for the six quality items of the image captured by the image capture device 3 being evaluated. Ultimately, the processing device 4 creates a radar chart based on these quantitative scores and displays the evaluation results for clarity, white balance, contrast, saturation, image brightness, and noise level. Because each image quality item is evaluated quantitatively, users can easily determine whether images captured with cameras of different specifications are similar in quality, making image quality evaluation more objective and solving the problem of long camera development cycles.

[0062] Figure 16 shows a radar chart of the quantitative evaluation of image quality for two different cameras. As shown in Figure 16, the items in the quantitative evaluation of image quality include sharpness, white balance, contrast, saturation, brightness, and noise level. The sharpness score of the first camera T1 is higher than the sharpness score of the second camera T2. The white balance score of the first camera T1 is higher than the white balance score of the second camera T2. The contrast score of the first camera T1 is lower than the contrast score of the second camera T2. The saturation score of the first camera T1 is higher than the saturation score of the second camera T2. The brightness score of the first camera T1 is lower than the brightness score of the second camera T2. The noise level score of the first camera T1 is lower than the noise level score of the second camera T2.

[0063] [Beneficial effects of the invention] One beneficial effect of the present invention is that the image quality evaluation system, image quality evaluation method, and image calibration device provided by the present invention can precisely position and calibrate images captured by an image capture device in complex scenes and assign quantitative evaluation scores to the image quality, making image quality evaluation more objective and solving the problem of long camera development cycles.

[0064] The contents disclosed above are merely preferred and possible embodiments of the present invention, and do not limit the scope of the claims of the present invention. Therefore, all equivalent technical modifications made based on the contents of the specification and accompanying drawings of the present invention are intended to be included in the scope of the claims of the present invention. [Explanation of symbols]

[0065] 1. Image correction device 2. Reference image capture device 3. Image capture device to be evaluated 4 Processing equipment 5 Moving mechanism Scene A B Calibration Pattern 11 Background Blocks 12 Analysis Block 121 First Sub-Analysis Block 1211 First Color Block 1212 Second Color Block 122 Second sub-analysis block 1221A, 1221B, 1221C, 1221D Third Color Block 1222 Fourth Color Block 1223 5th Color Block 1231A, 1231B, 1231C, 1231D, 1231E, 1231F 6th Color Block 123 Third Sub-Analysis Block 13 Positioning block 131 First sub-positioning block 132 Second sub-positioning block 133 Third Sub-Positioning Block 134 4th sub-positioning block K Reference Image M First contrast image L1~L8 1st line to 8th line G1 First Angle G2 Second Angle P1 First center point P2 Second center point D1 Direction of movement D2 Turning direction indication D3 Direction of movement D4 Direction of movement T1 First Camera T2 Second Camera S501 to S511, S1001 to S1003, S1101 to S1103, S1201 to S1204, S1301 to S1303, S1401 to S1403, S1501 to S1503 steps

Claims

1. an image calibration device located within the scene and used to display a calibration pattern; an image capture device that photographs the scene and the calibration pattern to obtain a first contrast image; a processing unit arranged to acquire the first contrast image and a reference image including the scene and the calibration pattern, compare the first contrast image and the calibration pattern in the reference image to generate a comparison result, and generate calibration information based on the comparison result; An image quality assessment system comprising: a positioning calibration operation of the image capture device is performed based on the calibration information; the image capture device is configured to capture an image of the scene and the calibration pattern to obtain a second contrast image after completing a positioning calibration operation of the image capture device, and the processing device is configured to compare the second contrast image with the calibration pattern in the reference image to generate an image quality assessment result; the calibration pattern includes an analysis block and a background block; the processor analyzes the second contrast image, an analysis block of the second contrast image, or a background block of the second contrast image to obtain a contrast modulation transfer function, a contrast color space value, a contrast grayscale, a contrast average grayscale, a contrast peak signal to noise ratio, or any combination thereof; the processing device analyzes the reference image, an analysis block of the reference image, or a background block of the reference image to obtain a reference modulation transfer function, a reference color space value, a reference grayscale, a reference average grayscale, a reference peak signal to noise ratio, or any combination thereof; the processing device generates a sharpness evaluation result, a white balance evaluation result, a contrast evaluation result, a saturation evaluation result, an image brightness evaluation result, a noise level evaluation result, or any combination thereof, of the image quality evaluation results based on a deviation percentage of the contrast modulation transfer function relative to the reference modulation transfer function, a deviation percentage of the contrast color space value relative to the reference color space value, a deviation percentage of the contrast grayscale relative to the reference grayscale, a deviation percentage of the contrast mean grayscale relative to the reference mean grayscale, a deviation percentage of the contrast peak signal to noise ratio relative to the reference peak signal to noise ratio, or any combination thereof. An image quality evaluation system comprising:

2. the calibration pattern includes a positioning block; The processing device includes: analyzing the positioning block in the first contrast image to obtain first position information; analyzing the positioning block in the reference image to obtain reference position information; comparing the first position information and the reference position information to obtain the calibration information for performing the positioning calibration operation; 2. The image quality assessment system of claim 1, arranged to:

3. the positioning calibration operation includes configuring the processing device to control a movement mechanism coupled to the image capture device based on the calibration information to adjust an orientation and position of the image capture device; The image quality evaluation system according to claim 2 , wherein the calibration information includes a center point movement amount, a rotation angle, a scale ratio, and a camera tilt angle.

4. 2. The image quality assessment system of claim 1, further comprising five image proofers, said five image proofers being identical to one another and positioned at four corners and a center of said scene, respectively.

5. an image capture device photographing a scene and a calibration pattern displayed by an image calibration device positioned within the scene to obtain a first contrast image; a processing unit acquiring the first contrast image and a reference image including the scene and the calibration pattern, comparing the first contrast image with the calibration pattern in the reference image to generate a comparison result, and generating calibration information based on the comparison result; a positioning calibration operation performed on the image capture device is performed based on the calibration information; the image capture device captures an image of the scene and the calibration pattern after completing the positioning calibration operation to obtain a second contrast image; the processing unit compares the second contrast image with the calibration pattern in the reference image to generate an image quality assessment result; the calibration pattern includes an analysis block and a background block; the processor analyzes the second contrast image, an analysis block of the second contrast image, or a background block of the second contrast image to obtain a contrast modulation transfer function, a contrast color space value, a contrast grayscale, a contrast average grayscale, a contrast peak signal to noise ratio, or any combination thereof; the processing device analyzes the reference image, an analysis block of the reference image, or a background block of the reference image to obtain a reference modulation transfer function, a reference color space value, a reference grayscale, a reference average grayscale, a reference peak signal to noise ratio, or any combination thereof; the processing device generates a sharpness evaluation result, a white balance evaluation result, a contrast evaluation result, a saturation evaluation result, an image brightness evaluation result, a noise level evaluation result, or any combination thereof, of the image quality evaluation results based on a deviation percentage of the contrast modulation transfer function relative to the reference modulation transfer function, a deviation percentage of the contrast color space value relative to the reference color space value, a deviation percentage of the contrast grayscale relative to the reference grayscale, a deviation percentage of the contrast mean grayscale relative to the reference mean grayscale, a deviation percentage of the contrast peak signal to noise ratio relative to the reference peak signal to noise ratio, or any combination thereof.

10. An image quality evaluation method comprising:

Images