CN111932521A - Image quality testing method and device, server and computer readable storage medium - Google Patents
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Abstract
The application relates to an image quality testing method and device, a server and a computer readable storage medium, which are used for acquiring images to be tested generated by photographing different testing scenes by a camera and calculating image quality parameters of the images to be tested. And comparing the image quality parameters of the image to be tested with the preset image quality standard of the test scene corresponding to the image to be tested to generate a quality test result. The traditional mode of judging through human eyes is realized, the image quality parameters of the image to be tested are calculated, and then the image quality parameters of the image to be tested are compared with the preset image quality standard of the test scene corresponding to the image to be tested to carry out quantification, so that the unification of the judgment standard is realized. Furthermore, the accuracy of the image quality test result is improved.
Description
Technical Field
The present application relates to the field of intelligent terminal technologies, and in particular, to a method and an apparatus for testing image quality, a server, and a computer-readable storage medium.
Background
With the development of mobile technology, camera photographing technology has also been rapidly developed. In the traditional technology, in the test of the imaging quality of a camera, after a tester shoots a test scene, the tester subjectively judges whether the shot image meets the requirements or not through human eyes. Obviously, the subjectivity is too strong by means of human eye judgment, and the unified standard is difficult to realize. Therefore, the test results of the imaging quality of the camera are uneven, and the accuracy of the test results is reduced.
Disclosure of Invention
The embodiment of the application provides an image quality testing method and device, a server and a computer readable storage medium, which can improve the accuracy of a testing result.
A method of image quality testing, the method comprising:
acquiring images to be tested, which are generated by photographing different test scenes by a camera;
calculating the image quality parameter of the image to be tested;
and comparing the image quality parameters of the image to be tested with the preset image quality standard of the test scene corresponding to the image to be tested to generate a quality test result.
An image quality testing apparatus, the apparatus comprising:
the system comprises a to-be-tested image acquisition module, a to-be-tested image acquisition module and a to-be-tested image acquisition module, wherein the to-be-tested image acquisition module is used for acquiring to-be-tested images generated by photographing different test scenes by a camera;
the image quality parameter calculation module is used for calculating the image quality parameters of the image to be tested;
and the quality test result generation module is used for comparing the image quality parameters of the image to be tested with the preset image quality standard of the test scene corresponding to the image to be tested to generate a quality test result.
A server comprising a memory and a processor, the memory having stored therein a computer program which, when executed by the processor, causes the processor to perform the steps of the image quality testing method as described above.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the image quality testing method as described above.
The image quality testing method and device, the server and the computer readable storage medium acquire images to be tested generated by photographing different testing scenes by the camera and calculate the image quality parameters of the images to be tested. And comparing the image quality parameters of the image to be tested with the preset image quality standard of the test scene corresponding to the image to be tested to generate a quality test result. The traditional mode of judging through human eyes is realized, the image quality parameters of the image to be tested are calculated, and then the image quality parameters of the image to be tested are compared with the preset image quality standard of the test scene corresponding to the image to be tested to carry out quantification, so that the unification of the judgment standard is realized. Furthermore, the accuracy of the image quality test result is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a diagram of an exemplary embodiment of an application environment of a method for image quality testing;
FIG. 2 is a flow diagram of a method for image quality testing in one embodiment;
FIG. 3 is a flowchart of a method for calculating image quality parameters of an image to be tested when performing a quality test on the exposure level of the image to be tested in FIG. 2;
FIG. 4 is a flow chart of a method for image quality testing in another embodiment;
FIG. 5 is a flowchart of a method for image quality testing in yet another embodiment;
FIG. 6 is a flowchart of an image quality testing method in yet another embodiment;
FIG. 7 is a block diagram showing the construction of an image quality testing apparatus according to an embodiment;
FIG. 8 is a block diagram showing the construction of an image quality testing apparatus according to another embodiment;
fig. 9 is a schematic diagram of an internal configuration of a server in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Fig. 1 is a diagram illustrating an application scenario of the image quality testing method in one embodiment. As shown in fig. 1, the application environment includes an electronic device 120 and a server 140. By the image quality testing method in the present application, the server 140 obtains the to-be-tested image generated by photographing different testing scenes by the camera on the electronic device 120, and calculates the image quality parameter of the to-be-tested image. And comparing the image quality parameters of the image to be tested with the preset image quality standard of the test scene corresponding to the image to be tested to generate a quality test result. Here, the electronic device 120 has a camera, and the electronic device 120 may be any terminal device such as a mobile phone, a tablet computer, a PDA (Personal Digital Assistant), a wearable device, and the like.
FIG. 2 is a flow diagram of a method for image quality testing in one embodiment. The image quality testing method in this embodiment is described by taking the server 140 in fig. 1 as an example. As shown in fig. 2, the image quality testing method includes steps 220 to 260. Wherein,
In the test of the imaging quality of the camera, different test scenes are photographed by the camera to generate an image to be tested. The different test scenes include a backlight scene, a high dynamic scene, a point light source scene, a face scene, a night scene, a landscape scene, and the like, which are not limited in the present application. Corresponding shooting parameters are configured in advance for each scene in the camera. And for each test scene, photographing by adopting the photographing parameters corresponding to the scene to generate an image to be tested. Then, the server acquires images to be tested, which are generated by photographing different test scenes by the camera, from the camera.
And 240, calculating the image quality parameter of the image to be tested.
Then, the server calculates an image quality parameter of the image to be tested. The image quality parameters may include exposure, color, definition, and the like of an image, and certainly, may also include other parameters capable of reflecting image quality, which is not limited in this application.
And 260, comparing the image quality parameters of the image to be tested with the preset image quality standard of the test scene corresponding to the image to be tested to generate a quality test result.
The server stores preset image quality standards of different test scenes in advance. The image to be tested generated by photographing different test scenes by the camera is obtained through the steps, and the image quality parameter of the image to be tested is calculated. And then, acquiring a preset image quality standard of a test scene corresponding to the image to be tested, and comparing the image quality parameter of the image to be tested with the preset image quality standard of the test scene corresponding to the image to be tested to generate a quality test result.
In the embodiment of the application, the to-be-tested images generated by photographing different test scenes by the camera are obtained, and the image quality parameters of the to-be-tested images are calculated. And comparing the image quality parameters of the image to be tested with the preset image quality standard of the test scene corresponding to the image to be tested to generate a quality test result. The traditional mode of judging through human eyes is realized, the image quality parameters of the image to be tested are calculated, and then the image quality parameters of the image to be tested are compared with the preset image quality standard of the test scene corresponding to the image to be tested to carry out quantification, so that the unification of the judgment standard is realized. Furthermore, the accuracy of the image quality test result is improved.
In one embodiment, there is provided an image quality testing method, further comprising:
different films and backlight panels are controlled to move by the film moving controller to realize the switching of test scenes.
Specifically, in the test of the imaging quality of the camera, the camera needs to photograph different test scenes, and the quality of images photographed in different test scenes is tested. In the traditional method, a tester generally goes outdoors to restore a test scene for shooting. In this case, the test scenario is often influenced by various other factors such as weather conditions during the process of restoring the test scenario, so that the test scenario cannot be restored well.
Therefore, when the test scene is restored, the test scene can be restored by combining different films and backlight panels. Among them, a film used for printing plate making is called a film, and corresponds to a negative film of a photograph. Different films are obtained by respectively shooting different test scenes. The backlight panel is a light source device for emitting light, and a uniform backlight panel can be used to emit uniform light. The test scene can be restored uniformly through the film and the backlight panel. And different films and backlight panels can be controlled by the film moving controller to move so as to automatically switch test scenes. Therefore, the camera is used for photographing the test scene to generate an image to be tested.
In the embodiment of the application, the traditional method is to restore the test scene outdoors for shooting by testers, and the test scene cannot be restored well due to the influence of various other factors such as weather conditions and the like in the process of restoring the test scene. In the embodiment of the application, different films and backlight panels are controlled to move by the film moving controller, so that the test scenes can be automatically switched. The test scene can be uniformly restored based on the film and the backlight panel, and the automatic switching of the test scene is realized through the film moving controller, so that the accuracy of the image quality test result is improved through the standard of the uniform test scene.
In one embodiment, as shown in fig. 3, when performing a quality test on the exposure level of the image to be tested, step 240, calculating an image quality parameter of the image to be tested includes:
Specifically, images are generally captured by a camera, and the captured images are RGB images. The RGB color scheme is a color standard in the industry, and various colors are obtained by changing three color channels of red (R), green (G) and blue (B) and superimposing the three color channels on each other, wherein RGB represents the colors of the three color channels of red, green and blue.
When the exposure of the image to be tested is tested, because the exposure of the image to be tested is mainly analyzed, the RGB image mainly reflects the three-primary-color information of the image, and the YUV image mainly reflects the luminance and chrominance information, the RGB image needs to be converted into the YUV image in view of the strong correlation between the exposure and the luminance. Firstly, acquiring color components of an image to be tested in a first color space (RGB); secondly, converting the color components of the first color space (RGB) into color components of a second color space (YUV); finally, an image to be tested represented by YUV color components is obtained.
Then, the average value and the histogram of the color components of the image to be tested in the second color space are calculated. Specifically, the average value and the histogram of the YUV color components of the image to be tested may be calculated, respectively. For example, the average value and histogram of the Y color component of the image to be tested are calculated, the average value and histogram of the U color component of the image to be tested are calculated, and the average value and histogram of the V color component of the image to be tested are calculated. The histogram of the color components can represent the distribution of the color components.
In the embodiment of the application, when the exposure of the image to be tested is tested, the color component of the image to be tested in the first color space is obtained, the color component of the image to be tested in the first color space (RGB) is converted into the color component of the image to be tested in the second color space, and then the average value and the histogram of the color component of the image to be tested in the second color space (YUV) are calculated. The YUV image mainly reflects brightness and chrominance information, so that the average value and the histogram of the YUV color component of the image to be tested are calculated. And then, the exposure of the image to be tested is subjected to quality test through the average value and the histogram of the YUV color components of the image to be tested. Finally, the accuracy of the image quality test is improved.
In one embodiment, comparing the image quality parameter of the image to be tested with a preset image quality standard of a test scene corresponding to the image to be tested to generate a quality test result, includes:
acquiring a preset image quality standard of a test scene corresponding to an image to be tested, wherein the preset image quality standard comprises an average value of color components of a second color space and a standard of a histogram;
and comparing the calculated average value and the histogram of the color components of the image to be tested in the second color space with a preset image quality standard to generate a quality test result.
Specifically, the server stores preset image quality standards of a plurality of test scenes. Therefore, when the server compares the image quality parameter of the image to be tested with the preset image quality standard of the test scene corresponding to the image to be tested, the server firstly obtains the preset image quality standard of the test scene corresponding to the image to be tested. The preset image quality standard comprises an average value of color components of the second color space and a standard of a histogram. Specifically, the average value of the color components of the second color space of the test scene corresponding to the image to be tested and the standard of the histogram are obtained from the server.
And comparing the calculated average value and histogram of the color components of the image to be tested in the second color space with the average value and histogram standard of the color components of the second color space of the test scene corresponding to the image to be tested to generate a quality test result. For example, the average value standard of the color components may be a threshold interval, and if the calculated average value of the color components of the image to be tested in the second color space does not fall within the threshold interval, it may be determined that the average value of the color components of the image to be tested in the second color space does not meet the standard. At this time, the generated quality test result is failed.
If the calculated average value of the color components of the image to be tested in the second color space falls within the threshold interval, it can be obtained that the average value of the color components of the image to be tested in the second color space meets the standard. At this time, the calculated histogram of the color component of the image to be tested in the second color space is further compared with the preset image quality standard.
The histogram standard of the color component may be a standard histogram of an image captured in a scene to be tested. Firstly, normalizing the calculated histogram of the color component of the image to be tested in the second color space, and then comparing the processed histogram with the standard histogram in the same test scene, specifically, calculating the similarity of the two histograms, and when the similarity is greater than a preset threshold, obtaining that the histogram of the color component of the image to be tested in the second color space meets the standard. At this time, the generated quality test result is qualified under the condition that the average value and the histogram of the color components of the image to be tested in the second color space both meet the standard.
And when the similarity is smaller than or equal to a preset threshold value, obtaining that the histogram of the color component of the image to be tested in the second color space does not meet the standard. At this time, the generated quality test result is failed. Wherein, the similarity can be calculated by calculating Euclidean distance.
In the embodiment of the application, first, a preset image quality standard of a test scene corresponding to an image to be tested is obtained. And comparing the calculated average value and the histogram of the color components of the image to be tested in the second color space with the preset image quality standard to generate a quality test result. And testing the image quality from the average value and the histogram of the color components of the image to be tested in the second color space. The average value of the color components can represent the concentration trend of the color components, and the histogram of the color components can represent the distribution condition of the color components. Thus, the accuracy of the quality test result is improved from two dimensions.
In one embodiment, the first color space is an RGB color space and the second color space is a YUV color space; calculating an average and histogram of color components of the second color space, comprising:
and calculating the average value and the histogram of the Y color component of the image to be tested in the YUV color space.
Specifically, YUV is a color coding method, which is often used in various video processing components. Where "Y" represents brightness (Luma) or gray scale value, and "U" and "V" represent Chroma (Chroma or Chroma) and are used to describe the color and saturation of the image for specifying the color of the pixel.
In the quality test of the exposure level of the image to be tested, in view of the strong correlation between the exposure level and the brightness, after the RGB image is converted into the YUV image, the average value and the histogram of the Y color component of the image to be tested in the YUV color space can be selected to be calculated. Without the need to compute the mean and histogram of each color component of the YUV color space. Therefore, the accuracy of the image quality test is ensured, and meanwhile, the system resources are saved.
As shown in fig. 4, there is provided an image quality testing method, including the steps of:
402, in the test of the imaging quality of the camera, shooting different test scenes by the camera to generate an image to be tested;
in step 414, if the preset exposure level standard is not satisfied, a specific comparison result is displayed, for example, a non-standard item is displayed.
In the embodiment of the application, when the exposure level of the image to be tested is subjected to quality test, in view of the strong correlation between the exposure level and the brightness, after the RGB image is converted into the YUV image, the average value and the histogram of the Y color component of the image to be tested in the YUV color space can be selected and calculated. Without the need to compute the mean and histogram of each color component of the YUV color space. Therefore, the accuracy of the image quality test is ensured, and meanwhile, the system resources are saved.
In one embodiment, when performing a quality test on the color of an image to be tested, calculating an image quality parameter of the image to be tested, comprises:
acquiring a color component of an image to be tested in a first color space;
and converting the color component of the image to be tested in the first color space into the color component of the image to be tested in the third color space.
Specifically, the first color space is an RGB color space, and the third color space is an HSV color space. The way of HSV expressing color images consists of three parts: in this specification, "H" represents Hue, S "represents Saturation, and V" represents Value. The HSV color space is used more in image processing, and is closer to the perception experience of people on color than the RGB color space. The HSV color space can visually express the hue, the brightness and the brightness of the color, and the color contrast is convenient to carry out.
The image is typically captured by a camera, and the captured image is an RGB image. When the quality of the color of the image to be tested is tested, because the color of the image to be tested is mainly analyzed, the hue, the vividness and the brightness of the color can be very intuitively expressed in consideration of the HSV color space, and the color can be conveniently compared. Therefore, it is necessary to convert the RGB image into the HSV image. Firstly, acquiring color components of an image to be tested in a first color space (RGB); secondly, converting the color components of the first color space (RGB) into color components of a third color space (HSV); finally, the image to be tested, represented by HSV color components, is obtained.
In the embodiment of the application, when the quality of the color of the image to be tested is tested, the color component of the image to be tested in the first color space is obtained, and the color component of the image to be tested in the first color space is converted into the color component of the image to be tested in the third color space. Because when the quality test is carried out on the color of the image to be tested, the color of the image to be tested is mainly analyzed, and the hue, the vividness and the brightness of the color can be very intuitively expressed in consideration of the HSV color space, so that the color comparison is convenient to carry out. Therefore, the RGB image is converted into the HSV image, and the accuracy of the subsequent color quality test is improved.
In one embodiment, comparing the image quality parameter of the image to be tested with a preset image quality standard of a test scene corresponding to the image to be tested to generate a quality test result, includes:
carrying out image segmentation on an image to be tested to obtain different image areas;
acquiring a preset color standard of an image area under a test scene corresponding to an image to be tested;
and comparing the color components of the different image areas in the third color space with the preset color standard of the image area to generate a quality test result.
Specifically, an image segmentation algorithm is adopted to perform image segmentation on the image to be tested to obtain different image areas. For example, for an image to be tested, such as a blue sky and a white cloud, the image is segmented to obtain an area corresponding to the blue sky and an area corresponding to the white cloud.
The preset color standard of the image area under the test scene corresponding to the image to be tested is obtained from the server, for example, the preset color standard (range of HSV color component) corresponding to the blue sky area under the test scene of the blue sky white cloud is obtained from the server, and the preset color standard (range of HSV color component) corresponding to the white cloud area under the test scene of the blue sky white cloud is obtained from the server.
And acquiring color components of different image areas in the third color space from the color components of the image to be tested in the third color space. And comparing the color components of the different image areas in the third color space with the preset color standard of the image area to generate a quality test result. For example, the color component of the blue-sky region in the HSV image of the image to be tested is (H1, S1, V1), and the preset color standard of the blue-sky region stored on the server is (H1, S1-S2, V1-V2). And comparing the two color components to obtain a quality test result. And if the color component of the blue-sky area in the HSV image of the image to be tested falls within the range of the preset color standard of the blue-sky area stored on the server, the generated quality test result is qualified. And if the color component of the blue-sky area in the HSV image of the image to be tested does not fall within the range of the preset color standard of the blue-sky area stored on the server, the generated quality test result is unqualified.
In the embodiment of the application, when the quality of the color of the image to be tested is tested, the image to be tested is segmented to obtain different image areas. Therefore, the quality test result is generated based on the comparison between the color components of the different image areas in the third color space and the preset color standard of the image area. The image segmentation realizes independent tests on different image areas, and color components in the same image area obtained by image segmentation are closer to each other, while the color components in different image areas are farther from each other. Therefore, the color components of the image area in the third color space based on different image areas are compared with the preset color standard of the image area, so that the color quality test of the image area is realized.
In one embodiment, the third color space is an HSV color space.
As shown in fig. 5, there is provided an image quality testing method including:
502, in the test of the imaging quality of a camera, photographing different test scenes by the camera to generate an image to be tested;
in step 514, if the preset color standard is not satisfied, a specific comparison result is displayed, for example, an substandard item is displayed.
In the embodiment of the application, when the quality of the color of the image to be tested is tested, the image to be tested is segmented to obtain different image areas. Therefore, the quality test result is generated based on the comparison between the color components of the different image areas in the third color space and the preset color standard of the image area. Because the color components in the same image region obtained by image segmentation are closer, and the color components in different image regions are farther. Therefore, the color components of the image area in the third color space based on different image areas are compared with the preset color standard of the image area, so that the color quality test of the image area is realized.
In one embodiment, as shown in fig. 6, when performing quality test on the definition of the image to be tested, calculating an image quality parameter of the image to be tested includes:
comparing the image quality parameters of the image to be tested with the preset image quality standard of the test scene corresponding to the image to be tested to generate a quality test result, comprising the following steps:
and 640, comparing the definition of the image to be tested with a preset image quality standard of a test scene corresponding to the image to be tested, and generating a quality test result.
Specifically, as in the quality test of the exposure and color of the image to be tested, the definition of the image to be tested is calculated when the definition of the image to be tested is subjected to the quality test. Then, comparing the definition of the image to be tested with a preset image quality standard of a test scene corresponding to the image to be tested, judging whether the preset image quality standard is met or not, and if the preset image quality standard is met, displaying the image to pass; if the preset color standard is not met, a specific comparison result is displayed, for example, an item which does not meet the standard is displayed.
In the embodiment of the application, the traditional mode of judging through human eyes is realized, the image quality parameters of the image to be tested are calculated, and then the image quality parameters of the image to be tested are compared with the preset image quality standard of the test scene corresponding to the image to be tested to carry out quantification, so that the unification of the judgment standard is realized. Furthermore, the accuracy of the image quality test result is improved.
In one embodiment, there is provided an image quality testing method, further comprising:
and adjusting the photographing parameters in the test scene according to the quality test result.
In the embodiment of the application, firstly, images to be tested, which are generated by photographing different test scenes by a camera, are obtained; secondly, calculating the image quality parameters of the image to be tested, comparing the image quality parameters of the image to be tested with the preset image quality standard of the test scene corresponding to the image to be tested, and generating a quality test result. And finally, if the quality test result is unqualified, displaying the specific quality test result. And adjusting the photographing parameters in the test scene based on the quality test result. Therefore, the photographing parameters under the test scene are adjusted according to the quality test result, and the photographing parameters under the test scene are optimized.
It should be understood that, although the steps in the flowcharts in the above-described figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in the above figures may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 7, there is provided an image quality testing apparatus 700, including:
the to-be-tested image acquisition module 720 is used for acquiring to-be-tested images generated by photographing different test scenes by the camera;
an image quality parameter calculation module 740, configured to calculate an image quality parameter of an image to be tested;
the quality test result generating module 760 is configured to compare the image quality parameter of the image to be tested with a preset image quality standard of a test scene corresponding to the image to be tested, and generate a quality test result.
In one embodiment, as shown in fig. 8, there is also provided an image quality testing apparatus 700, further comprising:
and the test scene switching module 780 is configured to control different films and backlight panels to move through the film moving controller to implement switching of test scenes.
In one embodiment, when performing the quality test on the exposure level of the image to be tested, the image quality parameter calculation module 740 is further configured to obtain a color component of the image to be tested in the first color space; converting the color component of the image to be tested in the first color space into the color component of the image to be tested in the second color space; and calculating the average value and the histogram of the color components of the image to be tested in the second color space.
In an embodiment, the quality test result generating module 760 is further configured to obtain a preset image quality standard of a test scene corresponding to the image to be tested, where the preset image quality standard includes an average value of color components of the second color space and a standard of a histogram; and comparing the calculated average value and the histogram of the color components of the image to be tested in the second color space with a preset image quality standard to generate a quality test result.
In one embodiment, the first color space is an RGB color space and the second color space is a YUV color space; the image quality parameter calculating module 740 is further configured to calculate an average value and a histogram of a Y color component of the image to be tested in the YUV color space.
In one embodiment, when performing quality test on the color of the image to be tested, the image quality parameter calculation module 740 is further configured to obtain the color component of the image to be tested in the first color space; and converting the color component of the image to be tested in the first color space into the color component of the image to be tested in the third color space.
In one embodiment, the quality test result generating module 760 is further configured to perform image segmentation on the image to be tested to obtain different image regions; acquiring a preset color standard of an image area under a test scene corresponding to an image to be tested; and comparing the color components of the different image areas in the third color space with the preset color standard of the image area to generate a quality test result.
In one embodiment, the third color space is an HSV color space.
In one embodiment, the image quality parameter calculating module 740 is further configured to calculate the sharpness of the image to be tested when performing the quality test on the sharpness of the image to be tested;
the quality test result generating module 760 is further configured to compare the definition of the image to be tested with a preset image quality standard of a test scene corresponding to the image to be tested, and generate a quality test result.
In one embodiment, there is also provided an image quality testing apparatus 700, further comprising:
and the photographing parameter adjusting module is used for adjusting the photographing parameters in the test scene according to the quality test result.
The division of each module in the image quality testing apparatus is only for illustration, and in other embodiments, the image quality testing apparatus may be divided into different modules as required to complete all or part of the functions of the image quality testing apparatus.
For the specific definition of the image quality testing apparatus, reference may be made to the above definition of the image quality testing method, which is not described herein again. All or part of each module in the image quality testing device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a watch is further provided, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the image quality testing method provided in the above embodiments.
Fig. 9 is a schematic diagram of an internal configuration of a server in one embodiment. As shown in fig. 9, the server includes a processor and a memory connected by a system bus. Wherein, the processor is used for providing calculation and control capability and supporting the operation of the whole server. The memory may include a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The computer program can be executed by a processor for implementing an image quality testing method provided by the above embodiments. The internal memory provides a cached execution environment for the operating system computer programs in the non-volatile storage medium. The server may be any terminal device such as a mobile phone, a tablet computer, a PDA (Personal Digital Assistant), a POS (Point of Sales), a vehicle-mounted computer, and a wearable device.
The implementation of each module in the image quality testing apparatus provided in the embodiment of the present application may be in the form of a computer program. The computer program may be run on an electronic device or a server. The program modules constituting the computer program may be stored on a memory of the electronic device or the server. Which when executed by a processor, performs the steps of the method described in the embodiments of the present application.
The embodiment of the application also provides a computer readable storage medium. One or more non-transitory computer-readable storage media containing computer-executable instructions that, when executed by one or more processors, cause the processors to perform the steps of the image quality testing method.
A computer program product containing instructions which, when run on a computer, cause the computer to perform an image quality testing method.
Any reference to memory, storage, database, or other medium used by embodiments of the present application may include non-volatile and/or volatile memory. Suitable non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), synchronous Link (Synchlink) DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and bus dynamic RAM (RDRAM).
The above examples of image quality tests only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (13)
1. An image quality testing method, characterized in that the method comprises:
acquiring images to be tested, which are generated by photographing different test scenes by a camera;
calculating the image quality parameter of the image to be tested;
and comparing the image quality parameters of the image to be tested with the preset image quality standard of the test scene corresponding to the image to be tested to generate a quality test result.
2. The method of claim 1, further comprising:
different films and backlight panels are controlled to move by the film moving controller to realize the switching of test scenes.
3. The method of claim 1 or 2, wherein when performing a quality test on the exposure of the image to be tested, the calculating of the image quality parameter of the image to be tested comprises:
acquiring a color component of the image to be tested in a first color space;
converting the color component of the image to be tested in the first color space into the color component of the image to be tested in the second color space;
and calculating the average value and the histogram of the color components of the image to be tested in the second color space.
4. The method of claim 3, wherein comparing the image quality parameter of the image to be tested with a preset image quality standard of a test scene corresponding to the image to be tested to generate a quality test result comprises:
acquiring a preset image quality standard of a test scene corresponding to the image to be tested, wherein the preset image quality standard comprises an average value of color components of a second color space and a standard of a histogram;
and comparing the calculated average value and the histogram of the color components of the image to be tested in the second color space with the preset image quality standard to generate a quality test result.
5. The method of claim 4, wherein the first color space is an RGB color space and the second color space is a YUV color space; the calculating the average and histogram of the color components of the second color space includes:
and calculating the average value and the histogram of the Y color component of the image to be tested in the YUV color space.
6. The method according to claim 1 or 2, wherein when performing a quality test on the color of the image to be tested, the calculating the image quality parameter of the image to be tested comprises:
acquiring a color component of the image to be tested in a first color space;
and converting the color component of the image to be tested in the first color space into the color component of the image to be tested in the third color space.
7. The method of claim 6, wherein comparing the image quality parameter of the image to be tested with a preset image quality standard of the test scene corresponding to the image to be tested to generate a quality test result comprises:
carrying out image segmentation on the image to be tested to obtain different image areas;
acquiring a preset color standard of the image area under a test scene corresponding to the image to be tested;
and comparing the color components of the different image areas in a third color space with the preset color standard of the image area to generate a quality test result.
8. The method of claim 7, wherein the third color space is an HSV color space.
9. The method according to claim 1 or 2, wherein when performing quality testing on the sharpness of the image to be tested, the calculating of the image quality parameter of the image to be tested comprises:
calculating the definition of the image to be tested;
the comparing the image quality parameter of the image to be tested with the preset image quality standard of the test scene corresponding to the image to be tested to generate a quality test result, comprising:
and comparing the definition of the image to be tested with a preset image quality standard of a test scene corresponding to the image to be tested to generate a quality test result.
10. The method of claim 1, further comprising:
and adjusting the photographing parameters in the test scene according to the quality test result.
11. An image quality testing apparatus, characterized in that the apparatus comprises:
the system comprises a to-be-tested image acquisition module, a to-be-tested image acquisition module and a to-be-tested image acquisition module, wherein the to-be-tested image acquisition module is used for acquiring to-be-tested images generated by photographing different test scenes by a camera;
the image quality parameter calculation module is used for calculating the image quality parameters of the image to be tested;
and the quality test result generation module is used for comparing the image quality parameters of the image to be tested with the preset image quality standard of the test scene corresponding to the image to be tested to generate a quality test result.
12. A server, comprising a memory and a processor, the memory having a computer program stored thereon, wherein the computer program, when executed by the processor, causes the processor to perform the steps of the image quality testing method according to any one of claims 1 to 10.
13. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the image quality testing method according to any one of claims 1 to 10.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113340569A (en) * | 2021-05-17 | 2021-09-03 | 浪潮金融信息技术有限公司 | Method, system and medium for detecting quality of high-speed photographing instrument |
| CN113438469A (en) * | 2021-05-31 | 2021-09-24 | 深圳市大工创新技术有限公司 | Automatic testing method and system for security camera |
| CN116055712A (en) * | 2022-08-16 | 2023-05-02 | 荣耀终端有限公司 | Method, device, chip, electronic equipment and medium for determining film forming rate |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH03160309A (en) * | 1989-11-20 | 1991-07-10 | Fujitsu Ltd | Image quality testing apparatus |
| JP2002163637A (en) * | 2000-11-27 | 2002-06-07 | Omron Corp | Device and method for examining image |
| JP2006140873A (en) * | 2004-11-15 | 2006-06-01 | Konica Minolta Holdings Inc | Device and method for evaluating image quality |
| US20080019611A1 (en) * | 2004-03-31 | 2008-01-24 | Canon Kabushiki Kaisha | Imaging System Performance Measurement |
| JP2011058925A (en) * | 2009-09-09 | 2011-03-24 | Ricoh Elemex Corp | Method and device for inspecting image |
| US20110286665A1 (en) * | 2010-05-24 | 2011-11-24 | Canon Kabushiki Kaisha | Image processing apparatus, control method, and computer-readable medium |
| US20120194847A1 (en) * | 2011-01-27 | 2012-08-02 | Ricoh Company, Ltd. | Image testing apparatus, image forming apparatus and computer readable information recording medium |
| CN104282019A (en) * | 2014-09-16 | 2015-01-14 | 电子科技大学 | Blind image quality evaluation method based on natural scene statistics and perceived quality propagation |
| US20150125090A1 (en) * | 2012-05-15 | 2015-05-07 | Sharp Kabushiki Kaisha | Image processing device, image processing method, television receiver, program, and recording medium |
| EP2889833A1 (en) * | 2013-12-26 | 2015-07-01 | Thomson Licensing | Method and apparatus for image quality assessment |
| CN105447878A (en) * | 2015-12-14 | 2016-03-30 | 深圳市高斯贝尔家居智能电子有限公司 | Image quality test analysis method and system |
| US20160094824A1 (en) * | 2014-09-25 | 2016-03-31 | Sony Corporation | Image processing method, image processing apparatus and electronic device |
| CN108335293A (en) * | 2018-01-31 | 2018-07-27 | 泰康保险集团股份有限公司 | The determination method and device of picture quality |
| CN109618149A (en) * | 2018-12-10 | 2019-04-12 | 北京旷视科技有限公司 | Test method, device and the smart machine of equipment image quality |
-
2020
- 2020-08-13 CN CN202010814353.0A patent/CN111932521B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH03160309A (en) * | 1989-11-20 | 1991-07-10 | Fujitsu Ltd | Image quality testing apparatus |
| JP2002163637A (en) * | 2000-11-27 | 2002-06-07 | Omron Corp | Device and method for examining image |
| US20080019611A1 (en) * | 2004-03-31 | 2008-01-24 | Canon Kabushiki Kaisha | Imaging System Performance Measurement |
| JP2006140873A (en) * | 2004-11-15 | 2006-06-01 | Konica Minolta Holdings Inc | Device and method for evaluating image quality |
| JP2011058925A (en) * | 2009-09-09 | 2011-03-24 | Ricoh Elemex Corp | Method and device for inspecting image |
| US20110286665A1 (en) * | 2010-05-24 | 2011-11-24 | Canon Kabushiki Kaisha | Image processing apparatus, control method, and computer-readable medium |
| US20120194847A1 (en) * | 2011-01-27 | 2012-08-02 | Ricoh Company, Ltd. | Image testing apparatus, image forming apparatus and computer readable information recording medium |
| US20150125090A1 (en) * | 2012-05-15 | 2015-05-07 | Sharp Kabushiki Kaisha | Image processing device, image processing method, television receiver, program, and recording medium |
| EP2889833A1 (en) * | 2013-12-26 | 2015-07-01 | Thomson Licensing | Method and apparatus for image quality assessment |
| CN104282019A (en) * | 2014-09-16 | 2015-01-14 | 电子科技大学 | Blind image quality evaluation method based on natural scene statistics and perceived quality propagation |
| US20160094824A1 (en) * | 2014-09-25 | 2016-03-31 | Sony Corporation | Image processing method, image processing apparatus and electronic device |
| CN105447878A (en) * | 2015-12-14 | 2016-03-30 | 深圳市高斯贝尔家居智能电子有限公司 | Image quality test analysis method and system |
| CN108335293A (en) * | 2018-01-31 | 2018-07-27 | 泰康保险集团股份有限公司 | The determination method and device of picture quality |
| CN109618149A (en) * | 2018-12-10 | 2019-04-12 | 北京旷视科技有限公司 | Test method, device and the smart machine of equipment image quality |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113340569A (en) * | 2021-05-17 | 2021-09-03 | 浪潮金融信息技术有限公司 | Method, system and medium for detecting quality of high-speed photographing instrument |
| CN113438469A (en) * | 2021-05-31 | 2021-09-24 | 深圳市大工创新技术有限公司 | Automatic testing method and system for security camera |
| CN113438469B (en) * | 2021-05-31 | 2022-03-15 | 深圳市大工创新技术有限公司 | Automatic testing method and system for security camera |
| CN116055712A (en) * | 2022-08-16 | 2023-05-02 | 荣耀终端有限公司 | Method, device, chip, electronic equipment and medium for determining film forming rate |
| CN116055712B (en) * | 2022-08-16 | 2024-04-05 | 荣耀终端有限公司 | Method, device, chip, electronic equipment and medium for determining film forming rate |
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|---|---|
| CN111932521B (en) | 2023-01-03 |
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