Lens image quality testing method and device, equipment and storage medium

Abstract

The invention relates to a lens image quality testing method and device, equipment and a storage medium. The lens image quality testing method comprises the steps that n images used for image quality testing are obtained, the n images are obtained through shooting of w lenses on the electronic equipment, focal segments corresponding to at least two lenses in the w lenses are different, and n and w are positive integers larger than 1; image quality consistency parameter extraction is carried out on the n images, a plurality of image quality consistency parameters among different images in the n images are obtained, and the image quality consistency parameters are used for describing the image quality consistency degree among the images shot by different lenses; and one or more test results are generated based on the plurality of image quality consistency parameters, and the test results are used for representing the consistency of the image quality among the images shot by the different lenses. Therefore, one or more image quality consistency tests can be carried out on a plurality of lenses with different focal lengths.

Description

Technical Field

[0001] The present disclosure relates to the field of image processing, and in particular to a lens image quality testing method, device, equipment and storage medium. Background Art

[0002] With the continuous development of mobile phones, having multiple lenses with different focal lengths on a mobile phone has become a common strategy for various mobile phone manufacturers. For mobile phones with multiple lenses with different focal lengths, image quality consistency is particularly important when taking photos with multiple lenses at the same time. Among them, photos taken with multiple lenses with poor image quality consistency will have serious image quality deviations. Summary of the invention

[0003] In order to overcome the problems existing in the related art, the present disclosure provides a lens image quality testing method, device, equipment and storage medium.

[0004] According to a first aspect of an embodiment of the present disclosure, a lens image quality testing method is provided, comprising: acquiring n images for image quality testing, wherein the n images are captured by w lenses on an electronic device, at least two of the w lenses correspond to different focal lengths, and n and w are positive integers greater than 1; extracting image quality consistency parameters from the n images to obtain a plurality of image quality consistency parameters between different images in the n images, wherein the image quality consistency parameters are used to describe the degree of consistency in image quality between images captured by different lenses; and generating one or more test results based on the plurality of image quality consistency parameters, wherein the test results are used to indicate the consistency in image quality between images captured by different lenses.

[0005] In some embodiments, an image includes a first test image having color blocks of multiple different colors; performing image quality consistency parameter extraction on n images to obtain multiple image quality consistency parameters between different images in the n images, including: for each first test image in a first number of first test images, performing data collection on a target color block on the first test image to obtain a first sampling parameter corresponding to each color block in the target color block on the first test image, wherein the target color block is a color block among multiple color blocks on the first test image for performing an image quality consistency test, the first sampling parameter is a parameter for describing color in a first color space, and the first number is less than or equal to n; performing color space conversion on the first sampling parameter to obtain a second sampling parameter, wherein the second sampling parameter is a parameter for describing color and brightness in a second color space, and the first color space and the second color space are different color spaces; performing image quality consistency parameter extraction on the second sampling parameter corresponding to the first number of first test images to obtain multiple image quality consistency parameters between different first test images in the first number of first test images.

[0006] In some embodiments, the target color block is a color block for exposure consistency testing, and the second sampling parameter includes a brightness parameter; image quality consistency parameter extraction is performed on the second sampling parameter corresponding to the first number of first test images to obtain multiple image quality consistency parameters between different first test images in the first number of first test images, including: using the brightness parameters corresponding to any two first test images in the first number of first test images to perform difference processing to obtain a brightness difference; generating one or more test results based on multiple image quality consistency parameters, including: in response to the brightness difference values ​​corresponding to any two first test images being less than a brightness difference threshold, generating a first test result, the first test result being used to indicate that the exposure consistency between images taken with different lenses meets the requirements.

[0007] In some embodiments, the target color block is a color block used for contrast consistency testing, and the second sampling parameter includes a brightness parameter; image quality consistency parameter extraction is performed on the second sampling parameter corresponding to the first number of first test images to obtain multiple image quality consistency parameters between different first test images in the first number of first test images, including: for each first test image, based on the brightness parameter corresponding to the target color block in the first test image, a contrast test is performed to obtain a contrast parameter corresponding to the first test image; difference processing is performed using the contrast parameters corresponding to any two first test images in the first number of first test images to obtain a contrast difference; one or more test results are generated based on multiple image quality consistency parameters, including: in response to the contrast difference corresponding to any two first test images being less than a contrast difference threshold, a second test result is generated, and the second test result is used to indicate that the contrast consistency between images captured by different lenses meets the requirements.

[0008] In some embodiments, the target color block is a color block for a white balance consistency test, or the target color block is a color block for a color restoration consistency test, the second sampling parameter includes a brightness parameter, a first color coordinate parameter, and a second color coordinate parameter, the first test image includes a first specific test image and a second specific test image, the first specific test image is a first test image captured by a first lens in a first number of first test images, the second specific test image is any first test image other than the first specific test image in the first number of first test images, and the first lens is one of the w lenses; image quality consistency parameters are extracted from the second sampling parameters corresponding to the first number of first test images to obtain multiple image quality consistency parameters between different first test images in the first number of first test images, including: for each color block in the target color block, using the brightness parameter, the first color coordinate parameter, and the first color coordinate parameter corresponding to the first specific test image and the second color coordinate parameter, and perform color difference processing on the brightness parameter, the first color coordinate parameter, and the second color coordinate parameter corresponding to the second specific test image to obtain the color difference parameter between the first specific test image and the second specific test image; and generate one or more test results based on multiple image quality consistency parameters, including: in response to the target color block being a color block for white balance consistency test, and the color difference parameters between the first specific test image and any second specific test image are all less than the first color difference threshold, generating a third test result, the third test result being used to indicate that the white balance consistency between images taken with different lenses meets the requirements; in response to the target color block being a color block for color reproduction consistency test, and the color difference parameters between the first specific test image and any second specific test image are all less than the second color difference threshold, generating a fourth test result, the fourth test result being used to indicate that the color reproduction consistency between images taken with different lenses meets the requirements.

[0009] In some embodiments, the image includes a second test image having a checkerboard pattern; performing image quality consistency parameter extraction on n images to obtain multiple image quality consistency parameters between different images in the n images, including: for each second test image in a second number of second test images, performing data acquisition on at least one acquisition area on the second test image to obtain a third sampling parameter corresponding to the acquisition area on the second test image, wherein the acquisition area is an area pre-set on the second test image for data acquisition, the acquisition area includes a boundary line with color changes, and the second number is less than or equal to n; performing image quality consistency parameter extraction on the third sampling parameters corresponding to the second number of second test images to obtain multiple image quality consistency parameters between different second test images in the second number of second test images.

[0010] In some embodiments, image quality consistency parameters are extracted from the third sampling parameters corresponding to the second number of second test images to obtain multiple image quality consistency parameters between different second test images in the second number of second test images, including: for each acquisition area in each second test image, according to different values ​​of the third sampling parameters corresponding to the acquisition area, and the edge extension function ESF, constructing an ESF data set corresponding to the acquisition area; performing sharpness processing based on the ESF data set to obtain sharpness parameters corresponding to the acquisition area; for each second test image, determining the sharpness parameters corresponding to the second test image based on the sharpness parameters corresponding to each acquisition area in the second test image; performing difference processing using the sharpness parameters corresponding to any two second test images in the second number of second test images to obtain a sharpness difference; generating one or more test results based on multiple image quality consistency parameters, including: in response to the sharpness difference corresponding to any two second test images being less than a sharpness difference threshold, generating a fifth test result, the fifth test result being used to indicate that the sharpness consistency between images captured by different lenses meets the requirements.

[0011] In some embodiments, based on the sharpness parameters corresponding to each acquisition area in the second test image, the sharpness parameters corresponding to the second test image are determined, including: in response to the number of acquisition areas being 1, determining the sharpness parameters corresponding to the acquisition areas as the sharpness parameters corresponding to the second test image; in response to the number of acquisition areas being greater than 1, performing a mean calculation based on the sharpness parameters corresponding to each acquisition area to obtain the sharpness parameters corresponding to the second test image.

[0012] In some embodiments, the image is obtained by at least one of the following methods: based on the same field of view, the image is obtained by using lenses with different focal lengths; based on the same shooting position, the image is obtained by using lenses with different focal lengths.

[0013] In some embodiments, in response to images being captured using lenses of different focal lengths based on the same field of view, and being captured using lenses of different focal lengths based on the same shooting position; the multiple images used for extracting image quality consistency parameters have the same field of view or the same shooting position.

[0014] According to a second aspect of an embodiment of the present disclosure, a lens image quality testing device is provided, comprising: an acquisition module, used to acquire n images for image quality testing, wherein the n images are captured by w lenses on an electronic device, at least two of the w lenses correspond to different focal lengths, and n and w are positive integers greater than 1; a processing module, used to extract image quality consistency parameters from the n images, and obtain multiple image quality consistency parameters between different images in the n images, wherein the image quality consistency parameters are used to describe the degree of consistency in image quality between images captured by different lenses; the processing module is also used to generate one or more test results based on the multiple image quality consistency parameters, wherein the test results are used to indicate the consistency in image quality between images captured by different lenses.

[0015] In some embodiments, the image includes a first test image having color blocks of multiple different colors; the processing module is also used to: for each first test image in a first number of first test images, collect data on a target color block on the first test image to obtain a first sampling parameter corresponding to each color block in the target color block on the first test image, wherein the target color block is a color block among multiple color blocks on the first test image for performing an image quality consistency test, the first sampling parameter is a parameter for describing color in a first color space, and the first number is less than or equal to n; perform a color space conversion on the first sampling parameter to obtain a second sampling parameter, wherein the second sampling parameter is a parameter for describing color and brightness in a second color space, and the first color space and the second color space are different color spaces; perform image quality consistency parameter extraction on the second sampling parameter corresponding to the first number of first test images to obtain multiple image quality consistency parameters between different first test images in the first number of first test images.

[0016] In some embodiments, the target color block is a color block used for exposure consistency testing, and the second sampling parameter includes a brightness parameter; the processing module is also used to: perform difference processing using the brightness parameters corresponding to any two first test images in the first number of first test images to obtain a brightness difference; in response to the brightness difference corresponding to any two first test images being less than a brightness difference threshold, generate a first test result, and the first test result is used to indicate that the exposure consistency between images taken with different lenses meets the requirements.

[0017] In some embodiments, the target color block is a color block used for contrast consistency testing, and the second sampling parameter includes a brightness parameter; the processing module is also used to: for each first test image, based on the brightness parameter corresponding to the target color block in the first test image, perform contrast test processing to obtain the contrast parameter corresponding to the first test image; use the contrast parameters corresponding to any two first test images in the first number of first test images to perform difference processing to obtain the contrast difference; in response to the contrast difference corresponding to any two first test images being less than the contrast difference threshold, generate a second test result, and the second test result is used to indicate that the contrast consistency between images taken with different lenses meets the requirements.

[0018] In some embodiments, the target color block is a color block for a white balance consistency test, or the target color block is a color block for a color reproduction consistency test, the second sampling parameter includes a brightness parameter, a first color coordinate parameter, and a second color coordinate parameter, the first test image includes a first specific test image and a second specific test image, the first specific test image is a first test image captured by a first lens in a first number of first test images, the second specific test image is any first test image other than the first specific test image in the first number of first test images, and the first lens is one of the w lenses; the processing module is further used to: for each color block in the target color block, use the brightness parameter, the first color coordinate parameter, and the second color coordinate parameter corresponding to the first specific test image and the second specific test image to obtain a color block; The corresponding brightness parameters, first color coordinate parameters, and second color coordinate parameters are subjected to color difference processing to obtain color difference parameters between the first specific test image and the second specific test image; in response to the target color block being a color block for white balance consistency test, and the color difference parameters between the first specific test image and any second specific test image are all less than the first color difference threshold, a third test result is generated, and the third test result is used to indicate that the white balance consistency between images taken with different lenses meets the requirements; in response to the target color block being a color block for color reproduction consistency test, and the color difference parameters between the first specific test image and any second specific test image are all less than the second color difference threshold, a fourth test result is generated, and the fourth test result is used to indicate that the color reproduction consistency between images taken with different lenses meets the requirements.

[0019] In some embodiments, the image includes a second test image having a checkerboard pattern; the processing module is also used to: for each second test image in a second number of second test images, perform data acquisition on at least one acquisition area on the second test image to obtain a third sampling parameter corresponding to the acquisition area on the second test image, wherein the acquisition area is an area pre-set for data acquisition on the second test image, the acquisition area includes a boundary line of color change, and the second number is less than or equal to n; perform image quality consistency parameter extraction on the third sampling parameters corresponding to the second number of second test images to obtain multiple image quality consistency parameters between different second test images in the second number of second test images.

[0020] In some embodiments, the processing module is also used to: for each acquisition area in each second test image, construct an ESF data set corresponding to the acquisition area according to different values ​​of the third sampling parameter corresponding to the acquisition area and the edge extension function ESF; perform sharpness processing based on the ESF data set to obtain sharpness parameters corresponding to the acquisition area; for each second test image, determine the sharpness parameters corresponding to the second test image based on the sharpness parameters corresponding to each acquisition area in the second test image; perform difference processing using the sharpness parameters corresponding to any two second test images in the second number of second test images to obtain a sharpness difference; in response to the sharpness difference corresponding to any two second test images being less than a sharpness difference threshold, generate a fifth test result, and the fifth test result is used to indicate that the sharpness consistency between images taken with different lenses meets the requirements.

[0021] In some embodiments, the processing module is also used to: in response to the number of acquisition areas being 1, determine the sharpness parameter corresponding to the acquisition area as the sharpness parameter corresponding to the second test image; in response to the number of acquisition areas being greater than 1, perform mean calculation based on the sharpness parameters corresponding to each acquisition area to obtain the sharpness parameter corresponding to the second test image.

[0022] In some embodiments, the image is obtained by at least one of the following methods: based on the same field of view, the image is obtained by using lenses with different focal lengths; based on the same shooting position, the image is obtained by using lenses with different focal lengths.

[0023] In some embodiments, in response to images being captured using lenses of different focal lengths based on the same field of view, and being captured using lenses of different focal lengths based on the same shooting position; the multiple images used for extracting image quality consistency parameters have the same field of view or the same shooting position.

[0024] According to a third aspect of an embodiment of the present disclosure, a lens image quality testing device is provided, comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to execute the first aspect and any one of the methods in the first aspect.

[0025] According to a fourth aspect of an embodiment of the present disclosure, a non-temporary computer-readable storage medium is provided. When instructions in the storage medium are executed by a processor of a mobile terminal, the mobile terminal is enabled to execute the first aspect and any one of the methods in the first aspect.

[0026] The technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: by acquiring multiple images for image quality testing taken by lenses of different focal lengths, and performing sampling and image quality consistency parameter extraction, multiple image quality consistency parameters are obtained. And the multiple image quality consistency parameters are judged to obtain corresponding image quality consistency results. Thus, one or more image quality consistency tests are performed on multiple lenses of different focal lengths.

[0027] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0029] Figure 1 The figure is a schematic diagram of a terminal photographing scene according to an exemplary embodiment.

[0030] Figure 2 The figure is a flow chart of a lens image quality testing method according to an exemplary embodiment.

[0031] Figure 3 The figure is a schematic diagram of a 24-color picture card according to an exemplary embodiment.

[0032] Figure 4 The figure is a schematic diagram of a chessboard chart according to an exemplary embodiment.

[0033] Figure 5 is a flow chart of another lens image quality testing method according to an exemplary embodiment.

[0034] Figure 6 The figure is a schematic diagram of target color block sampling according to an exemplary embodiment.

[0035] Figure 7 is a flow chart of another lens image quality testing method according to an exemplary embodiment.

[0036] Figure 8 is a flow chart of yet another lens image quality testing method according to an exemplary embodiment.

[0037] Fig. 9 is a flow chart of another lens image quality testing method according to an exemplary embodiment.

[0038] Fig.10 is a flow chart of another lens image quality testing method according to an exemplary embodiment.

[0039] Fig.11 The figure is a schematic diagram of a sampling area of ​​a chessboard chart according to an exemplary embodiment.

[0040] Fig.12 is a flow chart of yet another lens image quality testing method according to an exemplary embodiment.

[0041] Fig.13 is a schematic diagram of an ESF curve according to an exemplary embodiment.

[0042] Fig.14 The figure is a schematic diagram of an image used for image quality testing according to an exemplary embodiment.

[0043] Fig.15 is another schematic diagram of an image for image quality testing according to an exemplary embodiment.

[0044] Fig.16 is another schematic diagram of an image for image quality testing according to an exemplary embodiment.

[0045] Fig.17 is a schematic diagram of another image for image quality testing according to an exemplary embodiment.

[0046] Fig.18 The figure is a schematic diagram of a lens image quality testing device according to an exemplary embodiment.

[0047] Fig.19 The figure is a schematic diagram of a lens image quality testing device according to an exemplary embodiment.

[0048] Fig. 20 The diagram is a schematic diagram of a network device according to an exemplary embodiment. DETAILED DESCRIPTION

[0049] Here, exemplary embodiments will be described in detail, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present disclosure.

[0050] The present disclosure is applicable to scenarios where image quality consistency testing is performed on multiple lenses deployed on a terminal. For example, Figure 1 In the illustrated scene 100, the terminal 101 may have one or more lenses 1011. Of course, in the disclosed embodiments, it can be generally considered that the terminal has multiple lenses 1011. The lenses may also be referred to as cameras, and different lenses 1011 may correspond to different focal lengths, that is, different lenses 1011 may be lenses corresponding to different focal lengths. The lenses 1011 of different focal lengths may be used to shoot different scenes.

[0051] In some examples, the multiple lenses 1011 may include an ultra-wide-angle lens, a wide-angle lens, a 2x telephoto lens, a 5x telephoto lens, etc. Of course, the specific lens type is not limited in the present disclosure, and for example, a macro lens may also be included.

[0052] As Figure 1 As shown, it is assumed that the user shoots the subject 102 in the environment through multiple lenses 1011 configured on the terminal 101. Different lenses 1011 can respectively capture the images corresponding to the lenses 1011. However, if there is a problem of image quality consistency in the images captured by different lenses 1011, the terminal cannot obtain the image that the user wants to see when using multiple images for image synthesis. For example, due to poor sharpness consistency, the synthesized image may have better sharpness in some areas and poor sharpness in some areas. Similarly, for example, due to poor color restoration consistency, the color of a certain area in the synthesized image should be consistent under normal circumstances, but because the color restoration consistency of the images captured by different lenses 1011 is poor, color inconsistency may occur. Of course, similar situations may occur for other image quality problems, and the present disclosure does not list them one by one.

[0053] Obviously, for a terminal 101 with multiple lenses 1011, the importance of image quality consistency is self-evident. In some related technologies, staff need to test and adjust each lens in a natural scene. If a quality consistency test is performed, each lens needs to be tested and adjusted one by one in each scene. At the same time, staff are required to make judgments, such as whether the color reproduction is consistent, whether the contrast is consistent, whether the sharpness is consistent, etc. However, it is difficult for different staff members to have consistent judgment standards, and even if the same staff member makes manual judgments every time, it is difficult to ensure consistent judgments.

[0054] Therefore, the present disclosure obtains multiple images for image quality testing taken by lenses of different focal lengths, performs sampling and extracts image quality consistency parameters, and obtains multiple image quality consistency parameters. The multiple image quality consistency parameters are judged to obtain corresponding image quality consistency results. Thus, one or more image quality consistency tests are performed on multiple lenses of different focal lengths.

[0055] Figure 2 is a flow chart of a lens image quality testing method according to an exemplary embodiment. Figure 2 As shown, the method can be executed by a terminal.

[0056] In some embodiments, the terminal may also be referred to as a terminal device, a mobile station (MS), a mobile terminal (MT), etc., which is a device that provides voice and / or data connectivity to a user. For example, the terminal may be a handheld device with a wireless connection function, a vehicle-mounted device, etc. At present, some examples of terminals are: a smart phone (mobile phone), a pocket personal computer (PPC), a handheld computer, a personal digital assistant (PDA), a laptop computer, a tablet computer, a wearable device, or a vehicle-mounted device, etc. In addition, when it is a vehicle to everything (V2X) communication system, the terminal device may also be a vehicle-mounted device. It should be understood that the embodiments of the present disclosure do not limit the specific technology and specific device form adopted by the terminal.

[0057] The method may include the following steps:

[0058] In step S11 , n images for image quality testing are acquired.

[0059] In some embodiments, the terminal may obtain n images for image quality testing. The n images may be captured by w lenses on the electronic device. At least two of the w lenses have different focal lengths. n and w are positive integers greater than 1.

[0060] For example, in some cases, n is the same as w. Assuming that n and w are both 4, it means that 4 images are obtained by taking pictures through 4 lenses on the electronic device. That is, each lens takes an image for image quality testing.

[0061] For another example, in some cases n is an integer multiple of w. Assuming w is 4, n can be 4, 8, 12, 16, etc. That is, through each of the 4 lenses on the electronic device, one or more images can be taken. Of course, the number of images taken by each lens is usually the same. Therefore, when each lens takes 2 images, n is 2 times w; when each lens takes 4 images, n is 4 times w, and so on.

[0062] For example, the electronic device may be the terminal mentioned above.

[0063] For example, an image used for image quality testing may be referred to as a test image. The test image may be pre-photographed and stored in the terminal. For example, the test image may be stored in a memory of the terminal. The terminal may access the pre-stored test image by accessing a relevant area of ​​the storage area, and the terminal may read and use the stored test image.

[0064] For another example, the test image may be pre-photographed and stored in a server, and the terminal may establish a communication connection with the server in a wired or wireless manner. Then, the server is accessed through the communication connection, and the test image stored in the server is obtained. The terminal may send an acquisition instruction for obtaining the test image to the server; the server may also actively send the test image to the terminal. The specific implementation process is not limited in this disclosure.

[0065] In some examples, the server may be, for example, a server or a server cluster. Of course, it may also be a server or a server cluster built on a virtual machine, which is not limited in the present disclosure.

[0066] Wireless methods may include, for example, code division multiple access (CDMA), wideband code division multiple access (WCDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal frequency-division multiple access (OFDMA), single carrier-frequency division multiple access (SC-FDMA), and carrier sense multiple access with collision avoidance. According to the capacity, rate, latency and other factors of different networks, the network can be divided into 2G (generation, G) network, 3G network, 4G network or future evolution network, such as 5G network, which can also be called new radio (NR). Of course, it may also include wireless communication methods such as near field communication (NFC), Bluetooth, Zigbee, and WiFi.

[0067] In some embodiments, the test images may be obtained by shooting in different scenes. For example, in a scene with a consistent field of view (FOV), different lenses are used to shoot respectively, and images shot by different lenses with a consistent FOV are obtained. For another example, in a scene with a consistent shooting position, different lenses are used to shoot respectively, and images shot by different lenses with a consistent shooting position are obtained.

[0068] It is clear that when keeping the shooting position consistent, you can first use the lens corresponding to the minimum magnification focal length to shoot. The purpose is to ensure that when shooting with the lens of the minimum magnification focal length, only the subject and the fixed background are shot to avoid introducing other objects and interfering with the test image. Among them, the lens corresponding to the minimum magnification focal length can be an ultra-wide-angle lens. For example, if you first use a 2x telephoto lens to shoot, then when you use an ultra-wide-angle lens or a wide-angle lens to shoot while keeping the shooting position unchanged, interfering objects may be captured. Such as nearby tables, chairs, etc. If this type of test image with interfering objects is used for image quality consistency testing, the test results will be inaccurate.

[0069] In some embodiments, the test image may include a test image obtained by photographing a 24-color chart, and the test image may also include a test image obtained by photographing a checkerboard chart.

[0070] For example, Figure 3 A schematic diagram of a 24-color chart is shown. It can be seen that the chart includes 24 squares, and different squares correspond to different colors. Of course, the figure only uses black, white and gray to illustrate different squares, and the situation where the color difference may be large. It should be understood that Figure 2 Although there are multiple white squares and squares with the same grayscale, the figure is only for illustration and does not mean that the colors of these squares are the same. In other words, for a 24-color card, each square has a unique color, and each color is different.

[0071] For example, Figure 4 A schematic diagram of a checkerboard chart is shown. It can be seen that the checkerboard chart has a black and white checkerboard pattern. Considering that when the test image obtained by shooting the checkerboard chart is used to perform image quality consistency test, including sharpness test, the checkerboard in the test image containing the checkerboard chart will have a certain tilt angle, so that the black and white boundary line has a certain slope, which is conducive to sharpness consistency test.

[0072] In step S12, image quality consistency parameters are extracted using n images to obtain a plurality of image quality consistency parameters between different images.

[0073] In some embodiments, the terminal may extract image quality consistency parameters from multiple images in S12 to obtain image quality consistency parameters between different images. The image quality consistency parameters may be used to describe the degree of consistency of image quality between images captured by different lenses.

[0074] In some embodiments, after acquiring images for image quality testing, the terminal may collect data on such images to obtain sampling data corresponding to the images.

[0075] For example, a portion of the test image may be sampled. Assuming that the test image is a test image including a 24-color card, the terminal may sample a target color block in the 24-color card. Assuming that the test image is a test image including a checkerboard card, the terminal may sample a portion of the region of interest (ROI) in the checkerboard card.

[0076] In some embodiments, the image quality consistency parameter may be an image quality consistency parameter between any two images, and the image quality consistency parameter is used to describe the image quality consistency between the two corresponding images. For example, the contrast consistency parameter between image 1 and image 2 may be used to describe the contrast consistency between image 1 and image 2.

[0077] For example, multiple image quality consistency parameters can be determined between any two images. For example, between image 1 and image 2, contrast consistency parameters, exposure consistency parameters, white balance consistency parameters, color reproduction consistency parameters, sharpness consistency parameters, etc. can be determined.

[0078] For another example, any two images can be regarded as an image group, and different image groups can also correspond to multiple image quality consistency parameters. Image 1 and image 2 can constitute image group 1, and the contrast consistency parameters, exposure consistency parameters, white balance consistency parameters, color restoration consistency parameters, sharpness consistency parameters, etc. corresponding to the image group 1 can be determined. Image 3 and image 4 can constitute image group 2, and the contrast consistency parameters, exposure consistency parameters, white balance consistency parameters, color restoration consistency parameters, sharpness consistency parameters, etc. corresponding to the image group 2 can also be determined. Image 2 and image 5 can constitute image group 3, and the contrast consistency parameters, exposure consistency parameters, white balance consistency parameters, color restoration consistency parameters, sharpness consistency parameters, etc. corresponding to the image group 3 can also be determined. Of course, the present disclosure does not limit the number of image groups and the specific composition of each image group.

[0079] In step S13, one or more test results are generated based on a plurality of image quality consistency parameters.

[0080] In some embodiments, the terminal may generate one or more test results based on the multiple image quality consistency parameters determined in S13, wherein the test results may be used to indicate the consistency of image quality between images captured by different lenses.

[0081] In some examples, the consistency of image quality may include: the consistency of image quality between images captured by different lenses meets requirements.

[0082] In other examples, the consistency of image quality may also include: the image quality consistency between images captured by different lenses does not meet the requirements.

[0083] For example, the contrast consistency between the images captured by each lens can be determined based on the contrast consistency parameters corresponding to different image groups. For example, if the contrast consistency parameters corresponding to any image grouping meet the corresponding preset conditions, it can be considered that the contrast consistency between the images captured by each lens is very good and meets the requirements corresponding to the contrast consistency. For another example, if there are any one or more image groupings whose contrast consistency parameters do not meet the corresponding preset conditions, or meet other corresponding preset conditions, it can be considered that the contrast consistency between the images captured by each lens is not very good and does not meet the requirements corresponding to the contrast consistency.

[0084] Among them, the preset conditions may be used to describe the conditions corresponding to meeting the consistency requirements, and other preset conditions may be considered to be used to describe the conditions corresponding to not meeting the consistency requirements.

[0085] In some embodiments, one test result may correspond to a certain image quality consistency, and different test results may be used to correspond to different image quality consistencies. For example, test result 1 may be used to indicate the consistency of contrast between images captured by different lenses. Test result 2 may be used to indicate the consistency of white balance between images captured by different lenses. Of course, the above is only an exemplary description, and more test results may be included. The present disclosure does not limit the number of test results, or which image quality consistency each test result corresponds to.

[0086] In some embodiments, after the terminal obtains one or more test results, it can be displayed or sent to a designated device. This is used to inform the staff responsible for optimizing the consistency of image quality of different lenses to make corresponding optimization adjustments. Of course, the terminal can also directly trigger the optimization of the consistency of image quality of different lenses based on one or more test results. For example, based on the test results used to describe the consistency of sharpness, the sharpness consistency optimization of different lenses is triggered; for example, based on the test results used to describe the consistency of white balance, the white balance consistency optimization of different lenses is triggered.

[0087] The present disclosure obtains multiple images for image quality testing taken by lenses of different focal lengths, performs sampling and extracts image quality consistency parameters, and obtains multiple image quality consistency parameters. The multiple image quality consistency parameters are judged to obtain corresponding image quality consistency results. Thus, one or more image quality consistency tests are performed on multiple lenses of different focal lengths.

[0088] In the lens image quality testing method provided by the embodiment of the present disclosure, the image includes a first test image, and the first test image has color blocks of multiple different colors. Figure 5 is a flow chart of another lens image quality testing method according to an exemplary embodiment. Figure 5As shown, in S12, image quality consistency parameters of n images are extracted to obtain a plurality of image quality consistency parameters between different images in the n images, and the following steps may also be included:

[0089] In step S21, for each first test image in the first number of first test images, a target color block on the first test image is sampled to obtain first sampling parameters corresponding to each color block in the target color block on the first test image.

[0090] In some embodiments, the image used for the image quality test may include a first test image having color blocks of multiple different colors.

[0091] For example, the first test image may include a 24-color chart, so the first test image may have 24 color blocks of different colors.

[0092] It can be understood that each color block included in the 24-color chart can correspond to a different color, and the color in the area corresponding to each color block is consistent. The colors of different color blocks can be described using different standards. For example, if a color is described using the red, green, and blue (RGB) standard, it can be described using three dimensions: red (R), green (G), and blue (B). For another example, L * a * b * The standard describes a certain color, which can be described by L * 、a * , b * It is described in three dimensions. Among them, L * Indicates brightness, a * Represents a color coordinate in the color space. * Represents another color coordinate in this color space. * It can represent any color in the color transition from green to red. * It can represent any color in the color transition from blue to yellow. Of course, other possible standards can also be used to describe the color, which is not limited in the present disclosure.

[0093] Of course, the first test image may also include other types of color charts. For example, compared with the 24-color chart, other types of color charts may include more or fewer color blocks, and more or fewer colors, which is not limited in the present disclosure. At the same time, the order of different color blocks in other types of color charts may also be the same or different, which is not limited in the present disclosure.

[0094] In some embodiments, the terminal may collect data on the target color block on the first test image for each first test image in the first number of first test images to obtain the first sampling parameters corresponding to each color block in the target color block on the first test image. The target color block may be one or more color blocks among the multiple color blocks on the first test image for image quality consistency testing. For example, the target color block may be one of the multiple color blocks on the first test image, and the one color block is used for exposure consistency testing; for another example, the target color block may be multiple color blocks among the multiple color blocks on the first test image, and the multiple color blocks may be two color blocks for contrast consistency testing, or four color blocks for white balance consistency testing, or 18 color blocks for color reproduction consistency testing. Of course, the number of target color blocks should be less than or equal to the total number of color blocks in the first test image. It is understood that the number of color blocks used for different image quality consistency tests, and which color block in the first test image corresponds to, can be adaptively selected and adjusted according to actual conditions. For example, for a 24-color chart with different color arrangements, or a color chart with more colors, etc., a suitable number of color blocks with suitable colors can be selected based on actual conditions to perform corresponding image quality consistency tests, which is not limited in this disclosure. The first sampling parameter can be a parameter used to describe color in the first color space.

[0095] Among them, the color blocks used for image quality consistency testing can be roughly divided into color blocks for exposure consistency testing, color blocks for white balance consistency testing, color blocks for contrast consistency testing and color blocks for color reproduction consistency testing according to different image quality types.

[0096] For the color blocks used for exposure consistency testing, neutral color blocks can be selected. Among them, when measuring colors with the RGB standard, colors with completely consistent values ​​for the three channels of R, G, and B can be called neutral colors. Neutral colors usually include black, white, and various grays that transition between black and white. In some examples, a color block corresponding to neutral gray can be selected for exposure consistency testing. Among them, neutral gray can be a color with completely consistent values ​​for the three channels of R, G, and B, and all of them are 122. Of course, colors with R, G, and B channel values ​​that fluctuate within a certain range of 122 can also be considered neutral gray, such as fluctuating 2-3 values ​​up and down, etc. The specific floating range can be arbitrarily set according to actual conditions, and the present disclosure is not limited thereto.

[0097] For the color blocks used for contrast consistency testing, black blocks and white blocks can be selected. Normally, when measuring colors with the RGB standard, the R, G, and B channel values ​​of the black block are all 0, and the R, G, and B channel values ​​of the white block are all 255. However, in some cases, the black block can be selected from the color block with the same R, G, and B channel values ​​in the first test image and the lowest value. The white block can be selected from the color block with the same R, G, and B channel values ​​in the first test image and the highest value. In some examples, the appropriate color block can also be selected according to the actual situation, which is not limited by the present disclosure.

[0098] For the color blocks used for white balance consistency test, neutral color blocks can be selected. Similar to the color blocks used for exposure consistency test, for the color blocks used for white balance consistency test, it is only necessary to ensure that the values ​​of the three channels R, G, and B are completely consistent when measuring the color using the RGB standard.

[0099] It is clear that for the color blocks of the exposure consistency test, the contrast consistency test, and the white balance consistency test, neutral color blocks need to be selected, that is, the values ​​of the three channels R, G, and B are completely consistent.

[0100] For the color blocks used for color reproduction consistency testing, you can select color blocks corresponding to any color. When measuring colors with the RGB standard, the values ​​of the three channels R, G, and B can be completely inconsistent, partially consistent, or completely consistent, and the present disclosure is not limited thereto. In some examples, usually, it is preferred to select color blocks whose values ​​of the three channels R, G, and B are completely inconsistent or partially consistent. The reason is that neutral color blocks are more suitable for exposure consistency testing, contrast consistency testing, and white balance consistency testing.

[0101] For example, the first number of first test images should be less than or equal to n images, that is, the first number should be less than or equal to n.

[0102] For example, the first color space may be an RGB color space, and the first adopted parameters may include parameters corresponding to the R channel, parameters corresponding to the G channel, and parameters corresponding to the B channel.

[0103] In some embodiments, sampling the target color block may refer to Figure 6 As shown in FIG. 1 , for example, for a certain target color block, the target color block is divided into 3×3 regions to obtain 9 regions corresponding to the target color block. Then, the central region of the target color block can be sampled. Figure 6 The area marked with slashes in the middle. After sampling the central area, the terminal can obtain the R channel parameters, G channel parameters and B channel parameters corresponding to the central area.

[0104] In some cases, although the color card is divided into regions, it is clear that each region includes multiple pixels. Therefore, sampling the central region of the target color block can be to sample each pixel in the central region to determine the R channel parameter, G channel parameter, and B channel parameter corresponding to each pixel in the central region. The terminal can calculate the mean of each channel parameter based on the R channel parameter, G channel parameter, and B channel parameter corresponding to each pixel in the central region. The mean of the R channel parameter can be recorded as R srgb , the mean value of the G channel parameter can be recorded as G srgb , and the mean of the B channel parameters can be recorded as B srgb .

[0105] In step S22, the first sampling parameters are converted into a color space to obtain second sampling parameters.

[0106] In some embodiments, the terminal may perform color space conversion on the first sampling parameter obtained in S21 to obtain a second sampling parameter, wherein the second sampling parameter is a parameter used to describe color and brightness in a second color space, and the first color space and the second color space are different color spaces.

[0107] For example, the second color space can be L * a * b * Color space.

[0108] In some embodiments, the terminal may perform linearization correction before performing color space conversion on the first sampling parameter.

[0109] For example, the terminal can srgb , G srgb and B srgb Perform linear correction to obtain the corrected R linear , G linear and B linear For example, refer to Formula 1 for linear correction.

[0110]

[0111] It is understandable that R srgb Substituting into formula 1, we can get R linear , G srgb Substituting into formula 1, we can get G linear , B srgb Substituting into formula 1, we can get B linear .

[0112] In some embodiments, the terminal may convert the first sampling parameter into the third color space first, and then convert it into the second color space to obtain the second sampling parameter.

[0113] For example, the third color space may be an XYZ color space, where X, Y, and Z represent the chromaticity of different theoretical primary colors, respectively, and Y may also represent brightness. Of course, the specific definitions in the relevant art may be referred to, and the present disclosure will not elaborate on them.

[0114] For example, the terminal may convert the first sampling parameter into the third color space according to Formula 2.

[0115]

[0116] Among them, M rgb-xyz It can be considered as the transformation matrix used for third space transformation. It can be considered as the third adopted parameter corresponding to the third color space. rgb-xyz It can be shown as formula 3.

[0117]

[0118] Of course, Formula 3 is only one possible implementation method, and the values ​​of the parameters in the specific matrix can be arbitrarily replaced or modified according to actual conditions, and the present disclosure does not limit this.

[0119] After that, the terminal can obtain the value obtained in formula 2 Perform color space conversion again, that is, convert to the second color space, and then obtain the second sampling parameters. * 、a * and b * For example, you can Substitute into Formula 4 to determine L * 、a * and b * .

[0120]

[0121] Among them, X0=95.05, Y0=100, Z0=108.90. It can be understood that the above is only an exemplary description. Of course, the specific values ​​of X0, Y0 and Z0 can be arbitrarily replaced or modified according to actual conditions, and the present disclosure does not limit them. f(x) can be obtained by referring to Formula 5.

[0122]

[0123] In some examples, the second sampling parameter may include L * 、a * and b* .

[0124] In step S23, image quality consistency parameters are extracted from the second sampling parameters corresponding to the first number of first test images to obtain a plurality of image quality consistency parameters between different first test images in the first number of first test images.

[0125] In some embodiments, the terminal may extract image quality consistency parameters based on the second sampling parameters corresponding to each first test image obtained in S22, thereby obtaining multiple image quality consistency parameters between different first test images in the first number of first test images.

[0126] For example, the terminal can perform difference processing based on the second sampling parameters corresponding to any two first test images in the first number of first test images. The image quality consistency parameter corresponding to the first test image can be obtained. For two different first test images, a corresponding image quality consistency parameter can be obtained respectively, and for the same two first test images, multiple different image quality consistency parameters can also be obtained respectively. For example, the first test image 1 and the first test image 2 can obtain corresponding exposure consistency parameters; the first test image 3 and the first test image 5 can obtain corresponding exposure consistency parameters; the first test image 2 and the first test image 5 can obtain corresponding exposure consistency parameters. For another example, the first test image 1 and the first test image 2 can obtain corresponding white balance consistency parameters; the first test image 1 and the first test image 2 can also obtain corresponding contrast consistency parameters, etc.

[0127] The present disclosure is applicable to the case of testing an image including multiple color blocks, and provides a process for extracting image quality consistency parameters for an image including multiple color blocks. The terminal can extract image quality consistency parameters from the image used for testing, and obtain multiple image quality consistency parameters. The multiple image quality consistency parameters can be used to make judgments and obtain corresponding image quality consistency results. Thus, one or more image quality consistency tests can be performed on multiple lenses with different focal lengths.

[0128] In the lens image quality testing method provided by the embodiment of the present disclosure, the target color block is a color block used for exposure consistency testing, and the second sampling parameter includes a brightness parameter. Figure 7 FIG. 1 is a flowchart of another lens image quality testing method according to an exemplary embodiment. Figure 7 As shown, in S23, extracting image quality consistency parameters from the second sampling parameters corresponding to the first number of first test images to obtain multiple image quality consistency parameters between different first test images in the first number of first test images may include the following steps:

[0129] In step S31, difference processing is performed using brightness parameters corresponding to any two first test images among the first number of first test images to obtain a brightness difference.

[0130] In some embodiments, the target color block may be a color block used for exposure consistency testing, for example, the target color block is one color block in a 24-color chart.

[0131] In some embodiments, the color block used for the exposure consistency test may be a neutral color block. The neutral color may be a color whose R, G, and B channel values ​​are completely consistent when the color is measured using the RGB standard. For example, a color block corresponding to neutral gray may be selected for the exposure consistency test.

[0132] For example, the target color block may be the 22nd color block in a 24-color chart. Figure 3 The 4th color block in the 4th row from left to right of the 24 color blocks shown. It is understandable that the order of the color blocks in the 24-color chart from 1 to 24 can be determined from top to bottom and from left to right. For example, the leftmost color block in the first row is the 1st color block, and the rightmost color block is the 6th color block. The leftmost color block in the second row is the 7th color block, and the rightmost color block is the 12th color block, and so on.

[0133] It is clear that when performing exposure consistency test in each embodiment of the present disclosure, only the 22nd color block in the 24-color chart is used as an example for description. Color blocks corresponding to other neutral colors can also be selected according to actual conditions, or color blocks in other positions can be selected due to different color block orders, which is not limited in the present disclosure.

[0134] In some embodiments, the terminal may sample the 22nd color block in each first test image to obtain a second sampling parameter corresponding to the 22nd color block of each first test image.

[0135] In some embodiments, the second sampling parameter includes a brightness parameter. The brightness parameter may be, for example, the L * .

[0136] In some embodiments, the terminal may perform difference processing according to brightness parameters corresponding to any two first test images among the plurality of first test images to obtain the brightness difference corresponding to the two first test images.

[0137] For example, the L corresponding to the 22nd color block in the first test image 1 can be * and L corresponding to the 22nd color block in the first test image 2 * The difference is calculated to obtain the brightness difference between the first test image 1 and the first test image 2. In some examples, the brightness difference can be recorded as ΔL * .

[0138] For example, for any two first test images among the plurality of first test images, their corresponding ΔL may be calculated. * .

[0139] In some embodiments, generating one or more test results based on multiple image quality consistency parameters in S13 may include the following steps:

[0140] In step S32, in response to the brightness difference values ​​corresponding to any two first test images being smaller than the brightness difference threshold, a first test result is generated.

[0141] In some embodiments, when the brightness difference values ​​corresponding to any two first test images are less than the brightness difference threshold, the terminal can generate a first test result, wherein the first test result can be used to indicate that the exposure consistency between images captured by different lenses meets the requirements.

[0142] For example, for any two first test images among the plurality of first test images, ΔL corresponding to * , are all less than a preset brightness difference threshold, it can be considered that the exposure consistency between any two of the multiple first test images is very good. This further indicates that the exposure consistency between the lenses shooting the corresponding first test images is very good. The terminal can then generate a test result indicating that the exposure consistency meets the requirements, that is, the first test result.

[0143] For another example, for ΔL corresponding to any two first test images among the plurality of first test images, * , the maximum ΔL can be determined * , denoted as ΔL * max The terminal can determine the ΔL * max Is it less than the brightness difference threshold? * max When the maximum ΔL is less than the brightness difference threshold, the first test result can be generated. * are all smaller than the brightness difference threshold, then the remaining ΔL can be determined * They must also be smaller than the brightness difference threshold.

[0144] In some embodiments, when there are two first test images whose corresponding brightness difference is greater than or equal to the brightness difference threshold, the terminal may generate a sixth test result, wherein the sixth test result may be used to indicate that the exposure consistency between images captured by different lenses does not meet the requirements.

[0145] For example, for a plurality of first test images, there are two first test images corresponding to ΔL *When the brightness difference is less than or equal to a preset brightness difference threshold, it can be considered that the exposure consistency between the two first test images is not very good. This further indicates that the exposure consistency between the lenses used to shoot the two first test images is not very good. The terminal can then generate a test result indicating that the exposure consistency does not meet the requirements, i.e., the sixth test result.

[0146] In some embodiments, the preset brightness difference threshold may be 15. Of course, the brightness difference threshold may also be any other possible value, such as 20, 10, etc. The specific value of the brightness difference threshold may be set according to actual conditions, and the present disclosure does not limit it.

[0147] In some embodiments, exposure consistency may also be referred to as exposure multi-shot consistency.

[0148] The present disclosure provides a method for testing exposure consistency, which enables exposure consistency testing of multiple lenses with different focal lengths.

[0149] In the lens image quality testing method provided by the embodiment of the present disclosure, the target color block is a color block used for contrast consistency testing, and the second sampling parameter includes a brightness parameter. Figure 8 FIG. 1 is a flow chart of another lens image quality testing method according to an exemplary embodiment. Figure 8 As shown, in S23, extracting image quality consistency parameters from the second sampling parameters corresponding to the first number of first test images to obtain multiple image quality consistency parameters between different first test images in the first number of first test images may include the following steps:

[0150] In step S41, for each first test image, a contrast test process is performed based on the brightness parameter corresponding to the target color block in the first test image to obtain the contrast parameter corresponding to the first test image.

[0151] In some embodiments, the target color block may be a color block used for contrast consistency testing, for example, the target color block is 2 color blocks in a 24-color chart.

[0152] In some embodiments, the color blocks used for contrast consistency testing can be selected from neutral color blocks. For example, black blocks and white blocks. Normally, the R, G, and B channel values ​​of the black block are all 0, and the R, G, and B channel values ​​of the white block are all 255. However, in some cases, the black block can also be selected from the color block with the same R, G, and B channel values ​​in the first test image and the lowest value. The white block can also be selected from the color block with the same R, G, and B channel values ​​in the first test image and the highest value.

[0153] In some embodiments, the terminal may sample the 19th color block and the 24th color block in each first test image to obtain the second sampling parameters corresponding to the 19th color block of each first test image and the second sampling parameters corresponding to the 24th color block of each first test image.

[0154] It is clear that when performing contrast consistency test in various embodiments of the present disclosure, only the 19th color block and the 24th color block in the 24-color chart are used as examples for description. Color blocks corresponding to other neutral colors can also be selected according to actual conditions, or color blocks in other positions can be selected due to different color block orders, which is not limited in the present disclosure.

[0155] In some embodiments, the second sampling parameter includes a brightness parameter. For example, the brightness parameter may be the L mentioned above. * .

[0156] In some embodiments, for each first test image, the terminal may perform a contrast test process based on brightness parameters corresponding to a second number of target color blocks in the first test image to obtain contrast parameters corresponding to the first preset image.

[0157] For example, the L corresponding to the 19th color block in the first measurement test image * Denoted as L * 19 , the L corresponding to the 24th color block in the first measurement test image * Denoted as L * 24 Assuming that the contrast processing is performed on the first measurement test image 1, the L * 19 and the first measurement test image 1 L * 24 Perform contrast test processing to obtain a contrast parameter corresponding to the first measured test image 1, which is recorded as Contrast.

[0158] The contrast test process can be implemented by referring to Formula 6:

[0159]

[0160] In some examples, a contrast parameter corresponding to each first test image may be determined.

[0161] In step S42, difference processing is performed using contrast parameters corresponding to any two first test images among the first number of first test images to obtain a contrast difference.

[0162] In some embodiments, the terminal may perform difference processing using contrast parameters corresponding to any two first test images among the first number of first test images to obtain a contrast difference corresponding to the two first test images.

[0163] For example, the contrast parameter corresponding to the first test image 1 and the contrast parameter corresponding to the first test image 2 may be calculated to obtain the contrast difference between the first test image 1 and the first test image 2. In some examples, the contrast difference may be recorded as ΔContrast.

[0164] For example, the corresponding ΔContrast may be calculated for any two first test images among the first number of first test images.

[0165] In some embodiments, generating one or more test results based on multiple image quality consistency parameters in S13 may include the following steps:

[0166] In step S43, in response to the contrast difference values ​​corresponding to any two first test images being smaller than the contrast difference threshold, a second test result is generated.

[0167] In some embodiments, when the contrast difference values ​​corresponding to any two first test images are less than the contrast difference threshold, the terminal may generate a second test result, wherein the second test result is used to indicate that the contrast consistency between images captured by different lenses meets the requirements.

[0168] For example, if the ΔContrast corresponding to any two first test images among the multiple first test images is less than a preset contrast difference threshold, it can be considered that the contrast consistency between any two first test images among the multiple first test images is very good. This further indicates that the contrast consistency between the lenses used to shoot the corresponding first test images is very good. The terminal can then generate a test result indicating that the contrast consistency meets the requirements, i.e., the second test result.

[0169] For another example, for the ΔContrasts corresponding to any two first test images among the plurality of first test images, the maximum ΔContrast can be determined, which is recorded as ΔContrast max The terminal can determine the ΔContrast max Is it less than the contrast difference threshold? max When the contrast difference is less than the threshold, a second test result can be generated. max are all smaller than the contrast difference threshold, then it can be determined that the remaining ΔContrast must also be smaller than the contrast difference threshold.

[0170] In some embodiments, when there are two first test images whose contrast difference is greater than or equal to the contrast difference threshold, the terminal may generate a seventh test result, wherein the seventh test result may be used to indicate that the contrast consistency between images captured by different lenses does not meet the requirements.

[0171] For example, if the ΔContrast corresponding to two first test images among the multiple first test images is less than or equal to a preset contrast difference threshold, it can be considered that the contrast consistency between the two first test images is not very good. This further indicates that the contrast consistency between the lenses used to shoot the two first test images is not very good. The terminal can then generate a test result indicating that the contrast consistency does not meet the requirements, i.e., the seventh test result.

[0172] In some embodiments, the preset contrast difference threshold may be 20%. Of course, the contrast difference threshold may also be any other possible value, such as 10%, 30%, etc. The specific value of the contrast difference threshold may be set according to actual conditions, and the present disclosure does not limit it.

[0173] In some embodiments, contrast consistency may also be referred to as contrast multi-shot consistency.

[0174] The present disclosure provides a method for testing contrast consistency, which enables contrast consistency testing of multiple lenses with different focal lengths.

[0175] In the lens image quality test method provided by the embodiment of the present disclosure, the target color block is a color block used for white balance consistency test, or the target color block is a color block used for color reproduction consistency test, the second sampling parameter includes a brightness parameter, a first color coordinate parameter and a second color coordinate parameter, the first test image includes a first specific test image and a second specific test image, the first specific test image is a first test image captured by a first lens in a first number of first test images, and the second specific test image is any first test image other than the first test image in the first number of first test images. The first lens is one of the w lenses. Fig. 9 FIG. 1 is a flowchart of another lens image quality testing method according to an exemplary embodiment. Fig. 9 As shown, in S23, extracting image quality consistency parameters from the second sampling parameters corresponding to the first number of first test images to obtain multiple image quality consistency parameters between different first test images in the first number of first test images may include the following steps:

[0176] In step S51, for each color block in the target color block, color difference processing is performed using the brightness parameters, first color coordinate parameters, and second color coordinate parameters corresponding to the first specific test image and the brightness parameters, first color coordinate parameters, and second color coordinate parameters corresponding to the second specific test image to obtain the color difference parameters between the first specific test image and the second specific test image.

[0177] In some embodiments, the first specific test image may be a first test image captured by a first lens among a first number of first test images.

[0178] For example, the first specific test image may be a first test image captured by a wide-angle lens among the first number of first test images. The first lens is one of the w lenses. For example, the w lenses include an ultra-wide-angle lens, a wide-angle lens, a 2x telephoto lens, and a 5x telephoto lens. The first lens may be a wide-angle lens.

[0179] In some embodiments, the target color block may be a color block used for white balance consistency testing, for example, the target color block is 4 color blocks in a 24-color chart.

[0180] In some embodiments, the color blocks used for white balance consistency testing may be neutral color blocks, and any number of color blocks corresponding to colors with completely consistent values ​​of the three channels of R, G, and B may be selected.

[0181] For example, the terminal may sample the 20th to 23rd color blocks in each first test image to obtain the second sampling parameters corresponding to each color block in the 20th to 23rd color blocks of each first test image.

[0182] It is clear that when performing contrast consistency test in various embodiments of the present disclosure, only the 20th color block and the 23rd color block in the 24-color chart are used as examples for description. Color blocks corresponding to other neutral colors can also be selected according to actual conditions, or color blocks in other positions can be selected due to different color block orders, which is not limited in the present disclosure.

[0183] In some embodiments, the target color block may be a color block used for color reproduction consistency testing. For example, the target color block is 18 color blocks in a 24-color chart.

[0184] In some embodiments, the color blocks used for color reproduction consistency testing can be selected from color blocks corresponding to any color. When measuring colors with the RGB standard, the values ​​of the three channels R, G, and B can be completely inconsistent, partially consistent, or completely consistent. In other words, any number of color blocks of any color can be selected for color reproduction consistency testing. Of course, in some examples, color blocks whose values ​​of the three channels R, G, and B are completely inconsistent or partially consistent are usually selected.

[0185] For example, the terminal may sample the 1st to 18th color blocks in each first test image to obtain the second sampling parameters corresponding to each color block in the 1st to 18th color blocks of each first test image.

[0186] It is clear that when performing contrast consistency test in each embodiment of the present disclosure, only the first color block and the 18th color block in the 24-color chart are used as examples for description. Color blocks corresponding to other neutral colors can also be selected according to actual conditions, or color blocks in other positions can be selected due to different color block orders, which is not limited in the present disclosure.

[0187] In some embodiments, the second sampling parameter includes a brightness parameter, a first color coordinate parameter, and a second color coordinate parameter. The brightness parameter may be the L * , the first color coordinate parameter can be the a mentioned above * The second color coordinate parameter can be the b mentioned above. * .

[0188] In some embodiments, the first test image may include a first specific test image and a second specific test image, wherein the first specific test image is a first test image among multiple first test images, and the second specific test image is any first test image among the multiple first test images except the first specific test image.

[0189] For example, the first specific test image may be a first test image taken with a wide-angle lens. It can be understood that in various embodiments of the present disclosure, the wide-angle lens may be referred to as a main camera lens.

[0190] In some embodiments, for each color block in the target color block, the terminal may perform color difference processing using the brightness parameter, the first color coordinate parameter, and the second color coordinate parameter corresponding to the color block in the first specific test image and the brightness parameter, the first color coordinate parameter, and the second color coordinate parameter corresponding to the color block in the second specific test image to obtain the color difference parameter between the first specific test image and the second specific test image, which is recorded as ΔE 00 It can be understood that the color difference parameter is a color difference parameter for the color block between the first specific test image and the second specific test image.

[0191] For example, taking the first color block as an example, assuming that the multiple lenses include 4 lenses, namely, an ultra-wide-angle lens, a wide-angle lens, a 2x telephoto lens, and a 5x telephoto lens. The corresponding brightness parameters, first color coordinate parameters, and second color coordinate parameters in the first specific test image taken by different lenses can be recorded as L * wide-1 、a * wide-1 、b *wide-1 , L * uw-1 、a * uw-1 、b * uw-1 , L * 2倍-1 、a * 2倍-1 、b * 2倍-1 , L * 5倍-1 、a * 5倍-1 、b * 5倍-1 . Among them, wide represents a wide-angle lens, and uw represents an ultra-wide-angle lens. "-1" represents the first color block. For example, "wide-1" represents the first color block in the first test image taken by the ultra-wide-angle lens.

[0192] For example, ΔE can be calculated by referring to Formula 7 00

[0193]

[0194] Among them, ΔL represents the brightness difference, ΔC ab Expressed as chroma difference, ΔH ab Expressed as hue difference. ΔL, ΔC ab and ΔH ab It can be obtained according to formula 8.

[0195]

[0196] Among them, x' represents the color block corresponding to the wide-angle lens. For example, x' is wide-1 or wide-21. y' represents the color block corresponding to the non-wide-angle lens. For example, y' is uw-1, uw-21, 2x-5, 5x-13, etc.

[0197] It can be understood that for the calculation of ΔL and ΔC in formula 8 ab and ΔH ab When calculating ΔL, the parameters of the same color block collected by different lenses should be used. For example, when calculating ΔL, if L' x' For L' wide-1 , then L' y' Should be L' uw-1 , L' 2倍-1 or L' 5倍-1 .

[0198] For a', b', L', C' ab It can be determined by formula 9.

[0199]

[0200] It can be understood that the corresponding color blocks in the first test image taken by different lenses have the corresponding a', b', L', C' ab , L * 、a * and b * Different subscripts may be carried. For example, the lower right subscript may add "wide-1" or "-wide-1" to indicate that the parameter is the parameter corresponding to the first color block in the first test image taken by the wide-angle lens. Other lenses and other color blocks are similar and will not be described in detail in this disclosure.

[0201] Among them, G can be obtained by formula 10.

[0202]

[0203] in, Indicates the corresponding color blocks in the first test image taken by different lenses The average value of It can be calculated by formula 11.

[0204]

[0205] For S involved in Formula 7 L According to formula 12,

[0206]

[0207] in, It can represent the average value of L' corresponding to the corresponding color block in the first test image taken by different lenses.

[0208] For S involved in Formula 7 C According to formula 13,

[0209]

[0210] in, It can be said that the corresponding color blocks in the first test image taken by different lenses correspond to C a ’ b The average value of C a ' b It can be obtained by formula 14,

[0211]

[0212] For S involved in Formula 7 H According to formula 15,

[0213]

[0214] Where T can be obtained by formula 16

[0215]

[0216] in, It can represent the corresponding h' of the corresponding color block in the first test image taken by different lenses ab The average value of .

[0217] For R involved in Formula 7 T According to formula 17,

[0218] R T =-sin(Δθ)*R C

[0219] ...Formula 17

[0220] Among them, Δθ can be obtained by formula 18,

[0221]

[0222] Among them, exp is used to express exponential functions, such as exp(2), which expresses e 2 .

[0223] For R involved in formula 17 C According to formula 19,

[0224]

[0225] In some embodiments, generating one or more test results based on multiple image quality consistency parameters in S14 may include the following steps:

[0226] In step S52 , in response to the target color block being a color block for white balance consistency test and the color difference parameters between the first specific test image and any second specific test image being less than the first color difference threshold, a third test result is generated.

[0227] In some embodiments, when the target color block is a color block for white balance consistency test, and the color difference parameters between the first specific test image and any second specific test image are less than the first color difference threshold, the terminal can generate a third test result. The third test result is used to indicate that the white balance consistency between images captured by different lenses meets the requirements.

[0228] For example, the third number is 4, and the target color blocks are the 20th to 23rd color blocks. The first specific test image can be the first test image taken by a wide-angle lens, and the second specific test image can be the first test image taken by a non-wide-angle lens. The non-wide-angle lens can be, for example, an ultra-wide-angle lens, a 2x telephoto lens, a 5x telephoto lens, etc. For a certain color block, the ΔE between the first specific test image and any second specific test image is 00 , are all less than the preset first color difference threshold, it can be considered that the white balance consistency between the first specific test image and any second specific test image under this color block is very good. This further indicates that the white balance consistency between the wide-angle lens and other non-wide-angle lenses is very good under this color block. When the ΔE between the first specific test image and any second specific test image under each color block is 00 , are all smaller than a preset first color difference threshold, the terminal can generate a test result indicating that the white balance consistency meets the requirements, that is, a third test result.

[0229] For another example, for different color blocks, the ΔE between the first specific test image and any second specific test image is 00 , we can determine the maximum ΔE 00 , denoted as ΔE 00max The terminal can determine the ΔE 00max Is it less than the first color difference threshold? 00max When the maximum ΔE is less than the first color difference threshold, a third test result can be generated. 00 are all smaller than the contrast difference threshold, then the remaining ΔE can be determined 00 They must also be smaller than the first color difference threshold.

[0230] In some embodiments, when there is a ΔE between a first specific test image and a second specific test image 00 When the color difference is greater than or equal to the first color difference threshold, the terminal can generate an eighth test result. The eighth test result can be used to indicate that the white balance consistency between images taken by different lenses does not meet the requirements.

[0231] For example, for a ΔE between a first specific test image and a second specific test image 00 When the color difference is greater than or equal to the first color difference threshold, it can be considered that the white balance consistency between the first specific test image and the second specific test image is not very good. This further indicates that the white balance consistency between the wide-angle lens and the lens used to shoot the second specific test image is not very good. The terminal can then generate a test result indicating that the white balance consistency does not meet the requirements, that is, the eighth test result.

[0232] In some examples, the terminal may also determine the color difference according to a ΔE greater than or equal to the first color difference threshold.00 Corresponding to which color block, and then determining that under this color block, the white balance consistency does not meet the requirements.

[0233] In some embodiments, the preset first color difference threshold may be 5. Of course, the first color difference threshold may also be any other possible value, such as 2, 6, etc. The specific value of the first color difference threshold may be set according to actual conditions, and the present disclosure does not limit it.

[0234] In some embodiments, white balance consistency may also be referred to as white balance multi-shot consistency.

[0235] In step S53, in response to the target color block being a color block for color reproduction consistency test, and the color difference parameters between the first specific test image and any second specific test image are less than the second color difference threshold, a fourth test result is generated. The fourth test result is used to indicate that the color reproduction consistency between images captured by different lenses meets the requirements.

[0236] In some embodiments, when the target color block is a color block for color reproduction consistency testing, and the color difference parameters between the first specific test image and any second specific test image are less than the second color difference threshold, the terminal can generate a fourth test result. The fourth test result is used to indicate that the color reproduction consistency between images captured by different lenses meets the requirements.

[0237] For example, the third number is 18, and the target color blocks are the 1st to 18th color blocks. The first specific test image can be the first test image taken by a wide-angle lens, and the second specific test image can be the first test image taken by a non-wide-angle lens. The non-wide-angle lens can be, for example, an ultra-wide-angle lens, a 2x telephoto lens, a 5x telephoto lens, etc. For a certain color block, the ΔE between the first specific test image and any second specific test image is 00 , are all less than the preset second color difference threshold, it can be considered that the color reproduction consistency between the first specific test image and any second specific test image under this color block is very good. This further indicates that the color reproduction consistency between the wide-angle lens and other non-wide-angle lenses is very good under this color block. When the ΔE between the first specific test image and any second specific test image under each color block is 00 , are all smaller than a preset second color difference threshold, the terminal can generate a test result indicating that the color restoration consistency meets the requirement, that is, a fourth test result.

[0238] For another example, for different color blocks, the ΔE between the first specific test image and any second specific test image is 00 , we can determine the maximum ΔE 00 , denoted as ΔE 00max The terminal can determine the ΔE00max Is it less than the second color difference threshold? 00max When the maximum ΔE is less than the second color difference threshold, a fourth test result can be generated. 00 are all smaller than the contrast difference threshold, then the remaining ΔE can be determined 00 They must also be smaller than the second color difference threshold.

[0239] In some embodiments, when there is a ΔE between a first specific test image and a second specific test image 00 When the color difference is greater than or equal to the second color difference threshold, the terminal can generate a ninth test result. The ninth test result can be used to indicate that the color restoration consistency between images taken by different lenses does not meet the requirements.

[0240] For example, for a ΔE between a first specific test image and a second specific test image 00 When the color difference is greater than or equal to the second color difference threshold, it can be considered that the color reproduction consistency between the first specific test image and the second specific test image is not very good. This further indicates that the color reproduction consistency between the wide-angle lens and the lens used to shoot the second specific test image is not very good. The terminal can then generate a test result indicating that the color reproduction consistency does not meet the requirements, that is, the ninth test result.

[0241] In some examples, the terminal may also determine the color difference according to a ΔE greater than or equal to the second color difference threshold. 00 Corresponding to which color block, and then determining that under this color block, the color restoration consistency does not meet the requirements.

[0242] In some embodiments, the preset second color difference threshold may be 7. Of course, the second color difference threshold may also be any other possible value, such as 6, 8, etc. The specific value of the second color difference threshold may be set according to actual conditions, and the present disclosure does not limit it.

[0243] In some examples, the first color difference threshold and the second color difference threshold are not too correlated in value, that is, the first color difference threshold can be greater than the second color difference threshold, or less than the second color difference threshold, and the present disclosure does not limit this. Of course, in some cases, the first color difference threshold and the second color difference threshold can also be correlated. For example, the first color difference threshold can be configured to be less than the second color difference threshold. It can be understood that when performing image quality consistency testing, the tolerance for white balance consistency testing is often higher than the tolerance for color reproduction consistency testing. In other words, for the same ΔE 00, when performing a color reproduction consistency test, the test result that may be obtained is that the color reproduction consistency meets the corresponding requirements. However, when performing a white balance consistency test, the test result that may be obtained is that the white balance consistency does not meet the corresponding requirements. Therefore, by configuring the first color difference threshold to be smaller than the second color difference threshold, the accuracy of the white balance consistency test can be greatly improved, and the strict requirements for the white balance consistency test can also be met.

[0244] In some embodiments, color reproduction consistency may also be referred to as color reproduction multi-shot consistency.

[0245] The present disclosure provides a testing method for white balance consistency and color reproduction consistency, which enables contrast consistency testing and color reproduction consistency testing for multiple lenses with different focal lengths.

[0246] It can be understood that in the above embodiments, by sampling different color blocks in the first test image and calculating the image quality consistency parameters, the test of different image quality consistency is realized. For example, for the 24-color picture card, the 1st to 18th color blocks can be used for color restoration consistency test, and the neutral color characteristics of the 19th to 24th color blocks can be used to test the exposure consistency, white balance consistency, and contrast consistency. Among them, the 22nd color block is used for exposure consistency test, the 19th and 24th color blocks are used for contrast consistency test, and the 20th to 23rd color blocks are used for white balance consistency test. Thereby, only shooting the 24-color picture card is realized, that is, the consistency test of the four image quality dimensions of exposure, contrast, white balance, and color restoration is completed, which greatly improves the efficiency of testing and evaluation.

[0247] In the lens image quality testing method provided by the embodiment of the present disclosure, the image includes a second test image having a checkerboard pattern. Fig.10 FIG. 1 is a flowchart of another lens image quality testing method according to an exemplary embodiment. Fig.10 As shown, in S12, the image quality consistency parameters of n images are extracted to obtain a plurality of image quality consistency parameters between different images in the n images, which may include the following steps:

[0248] In step S61, for each second test image in the second number of second test images, data is collected on at least one collection area on the second test image to obtain a third sampling parameter corresponding to the collection area on the second test image.

[0249] In some embodiments, the image used for the image quality test may include a second test image having a black and white checkerboard pattern.

[0250] For example, the second test image may include a checkerboard pattern.

[0251] In some embodiments, the terminal may collect data for at least one sampling area on each second test image in the second number of second test images to obtain a third sampling parameter corresponding to the sampling area on the second test image. The sampling area is an area pre-set on the second test image for data collection, and the collection area includes a boundary line with color changes. For example, the collection area includes a boundary line adjacent to black and white.

[0252] For example, the second number of second test images should be less than or equal to n images, that is, the second number should be less than or equal to n. It should be understood that the first number and the second number are not related, and the first number and the second number can be configured independently.

[0253] For example, the sampling area can be called the ROI area, and the number of sampling areas can be 5. Fig.11 The five different ROI regions shown include ROI 1 in the center of the second test image and ROI 2, ROI 3, ROI 4 and ROI 5 distributed at four edges of the second test image. Each ROI region includes adjacent black and white boundary lines.

[0254] For example, the terminal may collect data from each ROI region to obtain sampling information of a plurality of pixels in the region, that is, the third sampling parameter.

[0255] In some embodiments, after data acquisition is performed on the ROI region, data expansion may be performed on the data acquired, such as by using oversampling to expand the data acquired, such as by performing 4 times oversampling on each ROI region.

[0256] Of course, the specific implementation process of supersampling can refer to related technologies, such as data supplementation by pixel mean value, etc., which is not limited in this disclosure. At the same time, the number of ROI regions and specific positions are only exemplary descriptions, and this disclosure does not limit the number of ROI regions and the specific positions of each ROI region. It can be understood that the number of ROI regions and the specific positions can be adjusted arbitrarily according to actual conditions.

[0257] In the embodiments of the present disclosure, “data collection” and “sampling” have the same meaning and can be used interchangeably.

[0258] In step S62, image quality consistency parameters are extracted from the third sampling parameters corresponding to the second number of second test images to obtain a plurality of image quality consistency parameters between different second test images in the second number of second test images.

[0259] In some embodiments, the terminal may extract image quality consistency parameters based on the third sampling parameters corresponding to each second test image obtained in S61, thereby obtaining multiple image quality consistency parameters between different second test images in the second number of second test images.

[0260] For example, the terminal can perform difference processing according to the third sampling parameters corresponding to any two second test images of the second number of second test images. The image quality consistency parameter corresponding to the second test image can be obtained. For two different second test images, a corresponding image quality consistency parameter can be obtained respectively. For example, the second test image 1 and the second test image 2 can obtain corresponding sharpness consistency parameters; the second test image 3 and the second test image 5 can obtain corresponding sharpness consistency parameters; the second test image 2 and the second test image 5 can obtain corresponding sharpness consistency parameters.

[0261] The present disclosure is applicable to the case of testing an image including a checkerboard pattern, and provides a process for extracting image quality consistency parameters for an image including a checkerboard pattern. The terminal can extract image quality consistency parameters from the image used for testing, and obtain multiple image quality consistency parameters. The multiple image quality consistency parameters can be used to make judgments and obtain corresponding image quality consistency results. Thus, one or more image quality consistency tests can be performed on multiple lenses with different focal lengths.

[0262] In the lens image quality testing method provided by the embodiment of the present disclosure, Fig.12 FIG. 1 is a flow chart of another lens image quality testing method according to an exemplary embodiment. Fig.12 As shown, in S62, performing image quality consistency parameter extraction on the third sampling parameters corresponding to the second number of second test images to obtain multiple image quality consistency parameters between different second test images in the second number of second test images may include the following steps:

[0263] In step S71, for each acquisition area in each second test image, an edge spread function (ESF) data set corresponding to the acquisition area is constructed according to different values ​​of the third sampling parameter corresponding to the acquisition area and the ESF.

[0264] In some embodiments, for each ROI region in each second test image, the terminal may obtain an ESF data set corresponding to the ROI region through ESF calculation according to different values ​​of the third sampling parameter obtained by sampling or supersampling the ROI region.

[0265] For example, the data in the ESF data set can be obtained by formula 20. It can be understood that formula 20 shows an ESF function.

[0266]

[0267] Among them, j represents pixels, and j is related to the multiple of supersampling. For example, when the supersampling is 4 times supersampling, the value of j is 0, 0.25, 0.5, 0.75, 1, 1.25, 1.5... until the upper limit of the pixel value in the ROI is reached. P is the horizontal coordinate of the pixel in the POI area, and r is the vertical coordinate of the pixel in the POI area. Therefore, any pixel in the ROI area can be located by p and r. R is the maximum value of r, which can also be considered as the height of the ROI area. P is the maximum value of p, which can also be considered as the width of the ROI area. φ(p, r) represents the pixel value corresponding to the pixel with coordinates p and r. α(p, r, j) is whether to include the archiving factor. Within the interval range, when α(p, r, j) is 1, it means that it is included in the interval weighted average calculation, and when α(p, r, j) is 0, it means that it is not included in the interval weighted average calculation. α(p, r, j) can be determined by formula 21.

[0268]

[0269] Where s(r) is the fitted edge centroid equation, and s(r) can be obtained by formula 22:

[0270]

[0271] Where K is a constant and m is the slope of the black and white edges in the ROI area.

[0272] In some embodiments, based on the above-mentioned Formula 20, Formula 21 and Formula 22, different third sampling parameters are introduced to obtain corresponding ESF data, thereby forming an ESF data set.

[0273] For example, the terminal can plot the ESF data set to obtain an ESF curve. Fig.13 As shown. In the ESF curve, the horizontal axis represents pixels, and the vertical axis represents the pixel value intensity. It can be seen from the ESF curve that there is a short transition phase at the black-white change boundary when taking pictures. In this transition phase, there are overshoots and undershoots. Therefore, the terminal can use overshoots and undershoots to perform sharpness consistency tests.

[0274] In step S72, sharpness processing is performed based on the ESF data set to obtain sharpness parameters corresponding to the acquisition area.

[0275] In some embodiments, the terminal may perform sharpness processing according to the ESF data set, and thereby obtain sharpness parameters corresponding to the second test image.

[0276] For example, refer to Fig.13 The overshoot and undershoot in the ESF curve are used to calculate the sharpness, thereby obtaining the sharpness parameter corresponding to the second test image. The sharpness calculation can refer to formula 23 to obtain the sharpness parameter (ringing).

[0277]

[0278] Among them, Ringing is the sharpness parameter, and the black value and white value are Fig.12 The values ​​at both ends of the ESF curve gradually converge. It can be understood that the values ​​closer to 255 after convergence are white values, which can also be considered as the values ​​collected from the white squares in the checkerboard in the second test image. The values ​​closer to 0 after convergence are black values, which can also be considered as the values ​​collected from the black squares in the checkerboard in the second test image. Of course, the white value should be 255 and the black value should be 0 ideally, but in reality, due to hardware equipment and objective factors, it is usually impossible to achieve the ideal state. abs means taking the absolute value.

[0279] It can be understood that formula 23 obtains the size corresponding to the white overshoot and the black undershoot by comparing the corresponding values ​​in the ESF curve. By using the maximum value as the sharpness parameter, the sharpness of the image can be more accurately reflected.

[0280] In step S73, for each second test image, based on the sharpness parameters corresponding to each acquisition area in the second test image, the sharpness parameter corresponding to the second test image is determined.

[0281] In some embodiments, the terminal may determine, for each second test image, a sharpness parameter corresponding to the second test image based on sharpness parameters corresponding to each acquisition area in the second test image.

[0282] For example, when the second test image includes only one acquisition area, the terminal may use the sharpness parameter corresponding to the acquisition area as the sharpness parameter corresponding to the second test image.

[0283] For another example, when the second test image includes multiple acquisition areas, the terminal can calculate the sharpness parameters corresponding to the multiple acquisition areas, such as taking the average value, variance, standard deviation, etc., to determine the sharpness parameters corresponding to the second test image.

[0284] In step S74, difference processing is performed using the sharpness parameters corresponding to any two second test images in the second number of second test images to obtain a sharpness difference.

[0285] In some embodiments, the terminal may perform difference processing using sharpness parameters corresponding to any two second test images among the second number of second test images to obtain a sharpness difference corresponding to the two second test images.

[0286] For example, the difference between the Ringing corresponding to the second test image 1 and the Ringing corresponding to the second test image 2 may be calculated to obtain the sharpness difference between the second test image 1 and the second test image 2. In some examples, the sharpness difference may be recorded as ΔRinging.

[0287] For example, the corresponding ΔRinging may be calculated for any two second test images among the plurality of second test images.

[0288] In some embodiments, generating one or more test results based on multiple image quality consistency parameters in S13 may include the following steps:

[0289] In step S75, in response to the sharpness difference values ​​corresponding to any two second test images being smaller than the sharpness difference threshold, a fifth test result is generated.

[0290] In some embodiments, when the sharpness difference values ​​corresponding to any two second test images are less than the sharpness difference threshold, the terminal may generate a fifth test result, wherein the fifth test result is used to indicate that the sharpness consistency between images captured by different lenses meets the requirement.

[0291] For example, if the ΔRinging corresponding to any two second test images among the multiple second test images is less than a preset sharpness difference threshold, it can be considered that the sharpness consistency between any two second test images among the multiple second test images is very good. This further indicates that the sharpness consistency between the lenses used to shoot the corresponding second test images is very good. The terminal can then generate a test result indicating that the sharpness consistency meets the requirements, i.e., the fifth test result.

[0292] For another example, for the ΔRingings corresponding to any two second test images among the plurality of second test images, the maximum ΔRinging can be determined, which is recorded as ΔRinging max The terminal can determine the ΔRinging max Is it less than the sharpness difference threshold? max If the maximum ΔRinging is smaller than the sharpness difference threshold, a fifth test result can be generated. It can be understood that if the largest ΔRinging is smaller than the sharpness difference threshold, it can be determined that the remaining ΔRingings must also be smaller than the sharpness difference threshold.

[0293] In some embodiments, when the sharpness difference between the two second test images is greater than or equal to the sharpness difference threshold, the terminal may generate a tenth test result, wherein the tenth test result may be used to indicate that the sharpness consistency between the images captured by different lenses does not meet the requirements.

[0294] For example, if the ΔRinging corresponding to two second test images among the multiple second test images is less than or equal to the preset sharpness difference threshold, it can be considered that the sharpness consistency between the two second test images is not very good. This further indicates that the sharpness consistency between the lenses used to shoot the two second test images is not very good. The terminal can then generate a test result indicating that the sharpness consistency does not meet the requirements, that is, the tenth test result.

[0295] In some embodiments, the preset sharpness difference threshold may be 20%. Of course, the sharpness difference threshold may also be any other possible value, such as 10%, 30%, etc. The specific value of the sharpness difference threshold may be set according to actual conditions, and the present disclosure does not limit it.

[0296] In some embodiments, sharpness consistency may also be referred to as sharpness multi-shot consistency.

[0297] The present disclosure provides a method for testing sharpness consistency, which enables sharpness consistency testing of multiple lenses with different focal lengths.

[0298] In the lens image quality testing method provided by the embodiment of the present disclosure, determining the sharpness parameters corresponding to the second test image based on the sharpness parameters corresponding to each acquisition area in the second test image in S73 may include: in response to the number of acquisition areas being 1, determining the sharpness parameters corresponding to the acquisition areas as the sharpness parameters corresponding to the second test image; in response to the number of acquisition areas being greater than 1, performing mean calculation based on the sharpness parameters corresponding to each acquisition area to obtain the sharpness parameters corresponding to the second test image.

[0299] In some embodiments, in response to the number of acquisition areas being 1, the terminal may determine the sharpness parameter corresponding to the acquisition area as the sharpness parameter corresponding to the second test image.

[0300] In some embodiments, in response to the number of acquisition areas being greater than one, the terminal may perform mean calculation based on sharpness parameters corresponding to each acquisition area to obtain sharpness parameters corresponding to the second test image.

[0301] For example, if there are multiple ROI regions, the sharpness parameters respectively calculated for the multiple ROI regions may be averaged, and the sharpness parameter average is used as the sharpness parameter corresponding to the second test image.

[0302] For example, suppose there are five ROI regions, namely ROI 1, ROI 2, ROI 3, ROI 4 and ROI 5. The sharpness parameters corresponding to different ROI regions can be Ringing roi-1 、Ringing roi-2 、Ringing roi-3 、Ringing roi-4 and Ringing roi-5 . Then the mean value of the sharpness parameter can be obtained by formula 24.

[0303]

[0304] Among them, Ringing final It is clear that the denominator in Formula 24 can be adaptively adjusted according to the actual number of ROI regions, that is, the number of ROI regions, which is the denominator in Formula 24. The numerator in Formula 24 is obtained by adding the sharpness parameters corresponding to each ROI region.

[0305] This work card takes into account that there may be multiple acquisition areas for data acquisition in the second test image, so by performing algebraic operations on the sharpness parameters corresponding to the multiple acquisition areas, such as taking the average, the sharpness parameters corresponding to the second test image can be obtained more accurately, so as to better perform sharpness consistency testing.

[0306] The present disclosure provides a plurality of methods for determining the sharpness parameter corresponding to the second test image, which satisfies the sharpness consistency test method in scenarios with different acquisition areas, and implements sharpness consistency test for multiple lenses with different focal lengths.

[0307] In the lens image quality testing method provided by the embodiment of the present disclosure, the image is obtained by at least one of the following methods: based on the same field of view, the image is obtained by using lenses with different focal lengths; based on the same shooting position, the image is obtained by using lenses with different focal lengths.

[0308] In some embodiments, the images used for image quality testing may be captured based on the same field of view using lenses with different focal lengths.

[0309] For example, assuming that the lenses with different focal lengths include an ultra-wide-angle lens, a wide-angle lens, a 2x telephoto lens, and a 5x telephoto lens, then the images captured based on the same field of view can be as follows: Fig.14 As shown, Fig.14 The first test images taken by four lenses are shown in FIG. Since the field of view angles remain the same, the positions of the color blocks in the first test images taken by different lenses are basically the same. Fig.15 As shown, the second test image is taken by four lenses. Fig.14Similarly, since the field of view angle remains consistent, the positions of the checkerboards in the second test images captured by different lenses are substantially the same.

[0310] It is understandable that there may be some differences in color because they are taken with different lenses. Fig.14 , between the first test images in the middle part, Fig.15 There are some color differences between the second test image in the middle part, namely Fig.14 The grayscale difference on the background behind the 24-color chart in Fig.15 The grayscale differences of the white cells in the checkerboard chart.

[0311] In some embodiments, the images used for image quality testing may be taken based on the same shooting position using lenses with different focal lengths.

[0312] For example, assuming that the lenses with different focal lengths include an ultra-wide-angle lens, a wide-angle lens, a 2x telephoto lens, and a 5x telephoto lens, then the images captured based on the same shooting position can be as follows: Fig.16 As shown, Fig.16 The first test images taken by four lenses are shown in FIG. Since the shooting positions are consistent, the sizes of the 24-color charts in the first test images taken by different lenses are different. Fig.17 As shown, the second test image is taken by four lenses. Fig.16 Similarly, since the shooting position remains consistent, the sizes of the checkerboards in the second test images shot by different lenses are different.

[0313] It can be seen that the shorter the focal length of the lens, the smaller the picture taken. Among the four lenses mentioned above, the ultra-wide-angle lens has the shortest focal length, and the 5x telephoto lens has the longest focal length.

[0314] The present disclosure provides a variety of ways to obtain images for image quality testing, so as to obtain a plurality of images for image quality testing taken by lenses of different focal lengths in a variety of scenarios, and to perform sampling and image quality consistency parameter extraction to obtain a plurality of image quality consistency parameters. The plurality of image quality consistency parameters are judged to obtain corresponding image quality consistency results. Thus, one or more image quality consistency tests are performed on a plurality of lenses of different focal lengths.

[0315] In the lens image quality testing method provided by the embodiment of the present disclosure, in response to images being shot with lenses of different focal lengths based on the same field of view, and being shot with lenses of different focal lengths based on the same shooting position; the multiple images used for extracting image quality consistency parameters have the same field of view angle or the same shooting position.

[0316] In some embodiments, when the images are taken based on the same field of view angle and lenses with different focal lengths, and based on the same shooting position and lenses with different focal lengths, when the terminal uses multiple images to extract image quality consistency parameters, the image quality consistency parameters may be extracted for multiple images with the same field of view angle, or the image quality consistency parameters may be extracted for multiple images with the same shooting position.

[0317] It can be understood that when performing image quality consistency tests on multiple images taken in the same scene, the test results of image quality consistency can be accurately obtained. If image quality consistency tests are performed based on multiple images taken in different scenes, since the test images themselves are shot in different scenes and are shot with different lenses, it is more likely that the image quality consistency does not meet the requirements. Therefore, even if the image quality consistency actually meets the requirements, due to the large differences in the images used for testing themselves, misjudgment is likely to occur, resulting in unnecessary adjustments.

[0318] The present invention utilizes a plurality of images for image quality testing captured by lenses of different focal lengths in the same scene to perform sampling and extract image quality consistency parameters, thereby obtaining a plurality of image quality consistency parameters, thereby ensuring the accuracy of the image quality consistency test.

[0319] Based on the same concept, an embodiment of the present disclosure also provides a lens image quality testing device.

[0320] It is understandable that the lens image quality testing device provided in the embodiment of the present disclosure includes hardware structures and / or software modules corresponding to the execution of each function in order to realize the above functions. In combination with the units and algorithm steps of each example disclosed in the embodiment of the present disclosure, the embodiment of the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the technical solution of the embodiment of the present disclosure.

[0321] Fig.18 is a schematic diagram of a lens image quality testing device according to an exemplary embodiment. Fig.18The device 200 includes: an acquisition module 201, used to acquire n images for image quality testing, wherein the n images are captured by w lenses on an electronic device, at least two of the w lenses correspond to different focal lengths, and n and w are positive integers greater than 1; a processing module 202, used to extract image quality consistency parameters from the n images to obtain multiple image quality consistency parameters between different images, wherein the image quality consistency parameters are used to describe the degree of consistency of image quality between images captured by different lenses; the processing module 202 is also used to generate one or more test results based on the multiple image quality consistency parameters, wherein the test results are used to indicate the image quality consistency between images captured by different lenses.

[0322] The present disclosure obtains multiple images for image quality testing taken by lenses of different focal lengths, performs sampling and extracts image quality consistency parameters, and obtains multiple image quality consistency parameters. The multiple image quality consistency parameters are judged to obtain corresponding image quality consistency results. Thus, one or more image quality consistency tests are performed on multiple lenses of different focal lengths.

[0323] In some embodiments, the image includes a first test image having color blocks of multiple different colors; the processing module 202 is also used to: for each first test image in the first number of first test images, collect data on the target color block on the first test image to obtain a first sampling parameter corresponding to each color block in the target color block on the first test image, wherein the target color block is a color block among multiple color blocks on the first test image for performing an image quality consistency test, the first sampling parameter is a parameter for describing color in a first color space, and the first number is less than or equal to n; perform color space conversion on the first sampling parameter to obtain a second sampling parameter, wherein the second sampling parameter is a parameter for describing color and brightness in a second color space, and the first color space and the second color space are different color spaces; perform image quality consistency parameter extraction on the second sampling parameter corresponding to the first number of first test images to obtain multiple image quality consistency parameters between different first test images in the first number of first test images.

[0324] The present disclosure is applicable to the case of testing an image including multiple color blocks, and provides a process for extracting image quality consistency parameters for an image including multiple color blocks. The terminal can extract image quality consistency parameters from the image used for testing, and obtain multiple image quality consistency parameters. The multiple image quality consistency parameters can be used to make judgments and obtain corresponding image quality consistency results. Thus, one or more image quality consistency tests can be performed on multiple lenses with different focal lengths.

[0325] In some embodiments, the target color block is a color block used for exposure consistency testing, and the second sampling parameter includes a brightness parameter; the processing module 202 is also used to: perform difference processing using the brightness parameters corresponding to any two first test images in the first number of first test images to obtain a brightness difference; in response to the brightness difference corresponding to any two first test images being less than a brightness difference threshold, generate a first test result, and the first test result is used to indicate that the exposure consistency between images taken with different lenses meets the requirements.

[0326] The present disclosure provides a method for testing exposure consistency, which enables exposure consistency testing of multiple lenses with different focal lengths.

[0327] In some embodiments, the target color block is a color block used for contrast consistency testing, and the second sampling parameter includes a brightness parameter; the processing module 202 is also used to: for each first test image, based on the brightness parameter corresponding to the target color block in the first test image, perform contrast test processing to obtain the contrast parameter corresponding to the first test image; perform difference processing using the contrast parameters corresponding to any two first test images in the first number of first test images to obtain a contrast difference; in response to the contrast difference corresponding to any two first test images being less than a contrast difference threshold, generate a second test result, the second test result being used to indicate that the contrast consistency between images captured by different lenses meets the requirements.

[0328] The present disclosure provides a method for testing contrast consistency, which enables contrast consistency testing of multiple lenses with different focal lengths.

[0329] In some embodiments, the target color block is a color block for a white balance consistency test, or the target color block is a color block for a color reproduction consistency test, the second sampling parameter includes a brightness parameter, a first color coordinate parameter, and a second color coordinate parameter, the first test image includes a first specific test image and a second specific test image, the first specific test image is a first test image captured by a first lens in a first number of first test images, the second specific test image is any first test image other than the first specific test image in the first number of first test images, and the first lens is one of the w lenses; the processing module 202 is further used to: for each color block in the target color block, use the brightness parameter, the first color coordinate parameter, and the second color coordinate parameter corresponding to the first specific test image to compare with the second specific test image; The brightness parameters, first color coordinate parameters, and second color coordinate parameters corresponding to the image are subjected to color difference processing to obtain color difference parameters between the first specific test image and the second specific test image; in response to the target color block being a color block for white balance consistency test, and the color difference parameters between the first specific test image and any second specific test image are all less than the first color difference threshold, a third test result is generated, and the third test result is used to indicate that the white balance consistency between images taken with different lenses meets the requirements; in response to the target color block being a color block for color reproduction consistency test, and the color difference parameters between the first specific test image and any second specific test image are all less than the second color difference threshold, a fourth test result is generated, and the fourth test result is used to indicate that the color reproduction consistency between images taken with different lenses meets the requirements.

[0330] The present disclosure provides a testing method for white balance consistency and color reproduction consistency, which enables contrast consistency testing and color reproduction consistency testing for multiple lenses with different focal lengths.

[0331] In some embodiments, the image includes a second test image having a checkerboard pattern; the processing module 202 is also used to: for each second test image in a second number of second test images, perform data acquisition on at least one acquisition area on the second test image to obtain a third sampling parameter corresponding to the acquisition area on the second test image, wherein the acquisition area is an area pre-set for data acquisition on the second test image, the acquisition area includes a boundary line of color change, and the second number is less than or equal to n; perform image quality consistency parameter extraction on the third sampling parameters corresponding to the second number of second test images to obtain multiple image quality consistency parameters between different second test images in the second number of second test images.

[0332] The present disclosure is applicable to the case of testing an image including a checkerboard pattern, and provides a process for extracting image quality consistency parameters for an image including a checkerboard pattern. The terminal can extract image quality consistency parameters from the image used for testing, and obtain multiple image quality consistency parameters. The multiple image quality consistency parameters can be used to make judgments and obtain corresponding image quality consistency results. Thus, one or more image quality consistency tests can be performed on multiple lenses with different focal lengths.

[0333] In some embodiments, the processing module 202 is also used to: for each acquisition area in each second test image, construct an ESF data set corresponding to the acquisition area according to different values ​​of the third sampling parameter corresponding to the acquisition area and the edge extension function ESF; perform sharpness processing based on the ESF data set to obtain sharpness parameters corresponding to the acquisition area; for each second test image, determine the sharpness parameters corresponding to the second test image based on the sharpness parameters corresponding to each acquisition area in the second test image; perform difference processing using the sharpness parameters corresponding to any two second test images in the second number of second test images to obtain a sharpness difference; in response to the sharpness difference corresponding to any two second test images being less than a sharpness difference threshold, generate a fifth test result, and the fifth test result is used to indicate that the sharpness consistency between images taken with different lenses meets the requirements.

[0334] The present disclosure provides a method for testing sharpness consistency, which enables sharpness consistency testing of multiple lenses with different focal lengths.

[0335] In some embodiments, the processing module 202 is also used to: in response to the number of acquisition areas being 1, determine the sharpness parameter corresponding to the acquisition area as the sharpness parameter corresponding to the second test image; in response to the number of acquisition areas being greater than 1, perform mean calculation based on the sharpness parameters corresponding to each acquisition area to obtain the sharpness parameter corresponding to the second test image.

[0336] The present disclosure provides a plurality of methods for determining the sharpness parameter corresponding to the second test image, which satisfies the sharpness consistency test method in scenarios with different acquisition areas, and implements sharpness consistency test for multiple lenses with different focal lengths.

[0337] In some embodiments, the image is obtained by at least one of the following methods: based on the same field of view, the image is obtained by using lenses with different focal lengths; based on the same shooting position, the image is obtained by using lenses with different focal lengths.

[0338] The present disclosure provides a variety of ways to obtain images for image quality testing, so as to obtain a plurality of images for image quality testing taken by lenses of different focal lengths in a variety of scenarios, and to perform sampling and image quality consistency parameter extraction to obtain a plurality of image quality consistency parameters. The plurality of image quality consistency parameters are judged to obtain corresponding image quality consistency results. Thus, one or more image quality consistency tests are performed on a plurality of lenses of different focal lengths.

[0339] In some embodiments, in response to images being captured using lenses of different focal lengths based on the same field of view, and being captured using lenses of different focal lengths based on the same shooting position; the multiple images used for extracting image quality consistency parameters have the same field of view or the same shooting position.

[0340] The present invention utilizes a plurality of images for image quality testing captured by lenses of different focal lengths in the same scene to perform sampling and extract image quality consistency parameters, thereby obtaining a plurality of image quality consistency parameters, thereby ensuring the accuracy of the image quality consistency test.

[0341] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0342] Fig.19 3 is a schematic diagram of a lens image quality test device according to an exemplary embodiment. For example, the device 300 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0343] Reference Fig.19 , the device 300 may include one or more of the following components: a processing component 302 , a memory 304 , a power component 306 , a multimedia component 308 , an audio component 310 , an input / output (I / O) interface 312 , a sensor component 314 , and a communication component 316 .

[0344] The processing component 302 generally controls the overall operation of the device 300, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 302 may include one or more processors 320 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 302 may include one or more modules to facilitate the interaction between the processing component 302 and other components. For example, the processing component 302 may include a multimedia module to facilitate the interaction between the multimedia component 308 and the processing component 302.

[0345] The memory 304 is configured to store various types of data to support operations on the device 300. Examples of such data include instructions for any application or method operating on the device 300, contact data, phone book data, messages, pictures, videos, etc. The memory 304 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0346] The power component 306 provides power to the various components of the device 300. The power component 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 300.

[0347] The multimedia component 308 includes a screen that provides an output interface between the device 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 308 includes a front camera and / or a rear camera. When the device 300 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0348] The audio component 310 is configured to output and / or input audio signals. For example, the audio component 310 includes a microphone (MIC), and when the device 300 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 304 or sent via the communication component 316. In some embodiments, the audio component 310 also includes a speaker for outputting audio signals.

[0349] I / O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0350] The sensor assembly 314 includes one or more sensors for providing various aspects of the status assessment of the device 300. For example, the sensor assembly 314 can detect the open / closed state of the device 300, the relative positioning of components, such as the display and keypad of the device 300, the sensor assembly 314 can also detect the position change of the device 300 or a component of the device 300, the presence or absence of user contact with the device 300, the orientation or acceleration / deceleration of the device 300, and the temperature change of the device 300. The sensor assembly 314 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 314 can also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 314 can also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0351] The communication component 316 is configured to facilitate wired or wireless communication between the device 300 and other devices. The device 300 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 316 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 316 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0352] In an exemplary embodiment, the apparatus 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0353] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 304 including instructions, and the instructions can be executed by the processor 320 of the device 300 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0354] Fig. 20 4 is a schematic diagram of a network device according to an exemplary embodiment. For example, the apparatus 400 may be provided as a server, such as the server mentioned above that can provide images for image quality testing. Fig. 20 , the apparatus 400 includes a processing component 422, which further includes one or more processors, and a memory resource represented by a memory 432 for storing instructions executable by the processing component 422, such as an application. The application stored in the memory 432 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 422 is configured to execute instructions to perform any of the methods involved in the above method.

[0355] The device 400 may also include a power supply component 426 configured to perform power management of the device 400, a wired or wireless network interface 450 configured to connect the device 400 to a network, and an input / output (I / O) interface 458. The device 400 may operate based on an operating system stored in the memory 432, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or the like.

[0356] The present invention can perform image quality consistency test during the debugging process of multiple cameras with the same or different focal lengths, conduct closed-loop test on the consistent image quality effects of multiple cameras, and optimize the debugging parameters through the test results.

[0357] The present invention obtains 24 color cards and checkerboard images of each focal length when the FOV is consistent, and 24 color cards and checkerboard images of each focal length when the shooting position is consistent. The multi-camera consistency of the image quality dimensions related to exposure and color is evaluated by calculating the 24 color cards, and the multi-camera consistency of sharpness is evaluated by calculating the checkerboard card.

[0358] The present disclosure solves the following technical problems: testing and evaluation of white balance consistency between different modules and sensors (including different focal lengths); testing and evaluation of color reproduction consistency between different modules and sensors (including different focal lengths); testing and evaluation of exposure consistency between different modules and sensors (including different focal lengths); testing and evaluation of contrast consistency between different modules and sensors (including different focal lengths); testing and evaluation of sharpness consistency between different modules and sensors (including different focal lengths). At the same time, the present disclosure uses color cards alone to complete multi-camera consistency testing and evaluation of four image quality dimensions: exposure, contrast, white balance, and color reproduction, greatly improving the efficiency of testing and analysis.

[0359] It is to be understood that in the present disclosure, "plurality" refers to two or more than two, and other quantifiers are similar. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. The singular forms "a", "the" and "the" are also intended to include plural forms, unless the context clearly indicates other meanings.

[0360] It is further understood that the terms "first", "second", etc. are used to describe various information, but such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other, and do not indicate a specific order or degree of importance. In fact, the expressions "first", "second", etc. can be used interchangeably. For example, without departing from the scope of the present disclosure, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information.

[0361] It is further understood that the meanings of the words "in response to" and "if" involved in the present disclosure depend on the context and the actual usage scenario. For example, the word "in response to" used herein can be interpreted as "at..." or "when..." or "if" or "if".

[0362] It can be further understood that, unless otherwise specified, “connection” includes a direct connection without other components between the two, and also includes an indirect connection with other components between the two.

[0363] It is further understood that, although the operations are described in a specific order in the drawings in the embodiments of the present disclosure, it should not be understood as requiring the operations to be performed in the specific order shown or in a serial order, or requiring the execution of all the operations shown to obtain the desired results. In certain environments, multitasking and parallel processing may be advantageous.

[0364] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any modifications, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure.

[0365] It should be understood that the present disclosure is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the scope of the appended claims.

Claims

1. A lens image quality testing method, characterized in that: include: Obtaining n images for image quality testing, wherein the n images are captured by w lenses on an electronic device, at least two of the w lenses have different focal lengths, and n and w are positive integers greater than 1; Extracting image quality consistency parameters from the n images to obtain a plurality of image quality consistency parameters between different images in the n images, wherein the image quality consistency parameters are used to describe the degree of consistency of image quality between images captured by different lenses; One or more test results are generated based on the plurality of image quality consistency parameters, wherein the test results are used to indicate the consistency of image quality between images captured by different lenses.

2. The method according to claim 1, characterized in that The image includes a first test image, wherein the first test image has color blocks of multiple different colors; The extracting of image quality consistency parameters from the n images to obtain a plurality of image quality consistency parameters between different images in the n images includes: For each of the first test images in a first number of the first test images, data is collected for a target color block on the first test image to obtain a first sampling parameter corresponding to each color block in the target color block on the first test image, wherein the target color block is a color block used for image quality consistency test among multiple color blocks on the first test image, the first sampling parameter is a parameter used to describe color in a first color space, and the first number is less than or equal to n; Performing color space conversion on the first sampling parameter to obtain a second sampling parameter, wherein the second sampling parameter is a parameter used to describe color and brightness in a second color space, and the first color space and the second color space are different color spaces; Image quality consistency parameters are extracted from the second sampling parameters corresponding to a first number of the first test images to obtain a plurality of image quality consistency parameters between different first test images in the first number of the first test images.

3. The method according to claim 2, characterized in that The target color block is a color block used for exposure consistency testing, and the second sampling parameter includes a brightness parameter; The extracting image quality consistency parameters from the second sampling parameters corresponding to the first number of the first test images to obtain a plurality of image quality consistency parameters between different first test images in the first number of the first test images includes: performing difference processing using brightness parameters corresponding to any two of the first test images in a first number of the first test images to obtain a brightness difference; The generating one or more test results based on the plurality of image quality consistency parameters comprises: In response to the brightness difference values ​​corresponding to any two of the first test images being less than a brightness difference threshold, a first test result is generated, where the first test result is used to indicate that exposure consistency between images captured by different lenses meets requirements.

4. The method according to claim 2, characterized in that: The target color block is a color block used for contrast consistency testing, and the second sampling parameter includes a brightness parameter; The extracting image quality consistency parameters from the second sampling parameters corresponding to the first number of the first test images to obtain a plurality of image quality consistency parameters between different first test images in the first number of the first test images includes: For each of the first test images, performing contrast test processing based on the brightness parameter corresponding to the target color block in the first test image to obtain a contrast parameter corresponding to the first test image; performing difference processing using contrast parameters corresponding to any two of the first test images in a first number of the first test images to obtain a contrast difference; The generating one or more test results based on the plurality of image quality consistency parameters comprises: In response to the contrast difference values ​​corresponding to any two of the first test images being less than a contrast difference threshold, a second test result is generated, where the second test result is used to indicate that the contrast consistency between images captured by different lenses meets the requirement.

5. The method according to claim 2, characterized in that: The target color block is a color block for a white balance consistency test, or the target color block is a color block for a color reproduction consistency test, the second sampling parameter includes a brightness parameter, a first color coordinate parameter, and a second color coordinate parameter, the first test image includes a first specific test image and a second specific test image, the first specific test image is a first test image captured by a first lens in a first number of the first test images, the second specific test image is any first test image in a first number of the first test images except the first specific test image, and the first lens is one of the w lenses; The extracting image quality consistency parameters from the second sampling parameters corresponding to the first number of the first test images to obtain a plurality of image quality consistency parameters between different first test images in the first number of the first test images includes: For each color block in the target color block, color difference processing is performed using the brightness parameter, the first color coordinate parameter, and the second color coordinate parameter corresponding to the first specific test image and the brightness parameter, the first color coordinate parameter, and the second color coordinate parameter corresponding to the second specific test image to obtain a color difference parameter between the first specific test image and the second specific test image; The generating one or more test results based on the plurality of image quality consistency parameters comprises: In response to the target color block being a color block for white balance consistency test, and the color difference parameters between the first specific test image and any second specific test image are less than a first color difference threshold, generating a third test result, wherein the third test result is used to indicate that the white balance consistency between images captured by different lenses meets the requirement; In response to the target color block being a color block for color reproduction consistency testing, and the color difference parameters between the first specific test image and any of the second specific test images are less than a second color difference threshold, a fourth test result is generated, and the fourth test result is used to indicate that the color reproduction consistency between images taken with different lenses meets the requirements.

6. The method according to claim 1, characterized in that The image includes a second test image having a checkerboard pattern thereon; The extracting of image quality consistency parameters from the n images to obtain a plurality of image quality consistency parameters between different images in the n images includes: For each of the second test images in the second number of the second test images, data is collected on at least one collection area on the second test image to obtain a third sampling parameter corresponding to the collection area on the second test image, wherein the collection area is an area preset on the second test image for data collection, the collection area includes a boundary line of color change, and the second number is less than or equal to n; Image quality consistency parameters are extracted from the third sampling parameters corresponding to the second number of the second test images to obtain a plurality of image quality consistency parameters between different second test images in the second number of the second test images.

7. The method according to claim 6, characterized in that The extracting image quality consistency parameters from the third sampling parameters corresponding to the second number of the second test images to obtain a plurality of image quality consistency parameters between different second test images in the second number of the second test images includes: For each of the acquisition areas in each of the second test images, constructing an ESF data set corresponding to the acquisition area according to different values ​​of the third sampling parameter corresponding to the acquisition area and an edge spread function ESF; Performing sharpness processing based on the ESF data set to obtain a sharpness parameter corresponding to the acquisition area; For each of the second test images, determining a sharpness parameter corresponding to the second test image based on the sharpness parameters corresponding to each acquisition area in the second test image; performing difference processing using sharpness parameters corresponding to any two of the second test images in the second number of the second test images to obtain a sharpness difference; The generating one or more test results based on the plurality of image quality consistency parameters comprises: In response to the sharpness difference values ​​corresponding to any two of the second test images being less than a sharpness difference threshold, a fifth test result is generated, where the fifth test result is used to indicate that the sharpness consistency between images captured by different lenses meets the requirement.

8. The method according to claim 7, characterized in that The determining, based on the sharpness parameters corresponding to each acquisition area in the second test image, the sharpness parameter corresponding to the second test image includes: In response to the number of the acquisition area being 1, determining a sharpness parameter corresponding to the acquisition area as a sharpness parameter corresponding to the second test image; In response to the number of the acquisition areas being greater than one, a mean value calculation is performed based on the sharpness parameters corresponding to the respective acquisition areas to obtain the sharpness parameters corresponding to the second test image.

9. The method according to any one of claims 1 to 8, characterized in that: The image is obtained by at least one of the following methods: Based on the same field of view, the images were taken with lenses of different focal lengths. Based on the same shooting position, the photos were taken using lenses of different focal lengths.

10. The method according to claim 9, characterized in that In response to the images being shot with lenses of different focal lengths based on the same field of view, and being shot with lenses of different focal lengths based on the same shooting position; the multiple images used for extracting image quality consistency parameters have the same field of view angle or the same shooting position.

11. A lens image quality testing device, characterized in that: include: an acquisition module, used to acquire n images for image quality testing, wherein the n images are captured by w lenses on the electronic device, at least two of the w lenses have different focal lengths, and n and w are positive integers greater than 1; A processing module, used to extract image quality consistency parameters from the n images to obtain a plurality of image quality consistency parameters between different images in the n images, wherein the image quality consistency parameters are used to describe the degree of consistency of image quality between images captured by different lenses; The processing module is further used to generate one or more test results based on the multiple image quality consistency parameters, wherein the test results are used to indicate the consistency of image quality between images captured by different lenses.

12. A lens image quality testing device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to: execute the method described in any one of claims 1-10.

13. A non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a processor of a mobile terminal, the mobile terminal is enabled to execute the method according to any one of claims 1 to 10.

Images