CN112529845A - Image quality value determination method, image quality value determination device, storage medium, and electronic device - Google Patents
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Abstract
The embodiment of the invention provides a method and a device for determining an image quality value, a storage medium and an electronic device, wherein the method comprises the following steps: carrying out graying processing on the acquired target image to obtain a grayed image; determining a degree of blur of the target image based on the grayed image; determining the degree of occlusion of the target image based on the grayed image; and determining the target quality value of the target image based on the fuzziness and the occlusion degree. The method and the device solve the problems of low accuracy, poor universality and low robustness of determining the image quality value in the related technology, and achieve the effects of efficiently and accurately determining the image quality value, strong universality and high robustness.
Description
Technical Field
The embodiment of the invention relates to the field of communication, in particular to an image quality value determination method, an image quality value determination device, a storage medium and an electronic device.
Background
In determining the quality value of the image, this may be determined by an image quality evaluation. Image Quality Assessment (IQA) is one of the basic techniques in Image processing, and mainly evaluates the Quality (degree of Image distortion) of an Image by performing characteristic analysis research on the Image. In the image quality evaluation, subjective quality evaluation and objective quality evaluation are classified according to whether or not an experimenter participates in the evaluation. The subjective evaluation means that a subject scores an image according to a rule and a standard established in advance, and manually scores subjective opinions of the image to be measured, and an average Score of multiple persons is used as an image quality Score to obtain an average subjective perception Score (MOS) or an average subjective perception Score Difference (DMOS). The objective evaluation method is to establish an image quality perception model and automatically predict image quality perception. Generally, the accuracy of subjective image quality evaluation is very high when judged by the human eye, but this method requires a lot of manpower and material resources. In the process, the testee is required to have related background knowledge, influence factors such as emotion and energy of the testee in the test state are considered, and meanwhile, the test process is extremely easy to be interfered by the external environment. Therefore, such methods need to be used with caution in case of a large increase in image data. The objective image quality evaluation method can be free from the limitation of human vision, automatically and quickly obtains the prediction score of the image to be measured, and has the advantages of simplicity, high efficiency, time saving, economy and the like. The objective image quality method detected through experiments can be embedded into various hardware systems, and the method is more suitable for actual demand application, so that a mathematical model capable of automatically and accurately predicting subjective quality needs to be designed.
The quality of the image is evaluated, and the image with high imaging quality is screened out for subsequent processing, so that the requirement of computing resources can be effectively reduced, and the efficiency and accuracy of image processing are greatly improved. The following description will be given by taking a license plate as an example:
license plate information is one of very important information in the field of security video monitoring, and with more and more monitoring scenes in recent years and exponential increase of video data volume, huge challenges are provided for analysis of monitoring data. The common license plate recognition, reconstruction and other methods usually need to carry out a large amount of calculation on batch license plate images, and consume a huge amount of time cost. In addition, the quality of the license plate image greatly affects the final recognition accuracy. Therefore, the image quality of the license plate is evaluated, the target with high imaging quality is screened out, the license plate is identified, the requirement of computing resources can be effectively reduced, and the identification efficiency and accuracy are greatly improved. In the related art, the quality of an image is generally determined using a sharpness parameter and a tilt angle parameter of the image, or the quality of the image is determined using a convolutional neural network. However, the real imaging quality of the license plate cannot be effectively quantified by a single parameter, and the evaluation method is not comprehensive in the case of multiple scenes; when the convolutional neural network is used for determining the image quality, the process of training the model excessively depends on data collection, and the method is low in robustness aiming at license plates in different scenes.
Therefore, the problems of low accuracy, poor universality and low robustness in determining the image quality value exist in the related technology.
In view of the above problems in the related art, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a method and a device for determining an image quality value, a storage medium and an electronic device, which are used for at least solving the problems of low accuracy, poor universality and low robustness of the determination of the image quality value in the related technology.
According to an embodiment of the present invention, there is provided an image quality value determination method including: carrying out graying processing on the acquired target image to obtain a grayed image; determining a degree of blur of the target image based on the grayed image; determining the degree of occlusion of the target image based on the grayed image; determining a target quality value for the target image based on the blurriness and the occlusion.
According to another embodiment of the present invention, there is provided an image quality value determination apparatus including: the processing module is used for carrying out graying processing on the acquired target image to obtain a grayed image; a first determining module for determining a degree of blur of the target image based on the grayed image; a second determination module for determining the degree of occlusion of the target image based on the grayed image; a third determination module to determine a target quality value of the target image based on the blurriness and the occlusion.
According to a further embodiment of the present invention, there is also provided a computer-readable storage medium having a computer program stored thereon, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
According to the invention, after the target image is acquired, the target image is subjected to graying processing to obtain a grayed image, the fuzziness and the shielding degree of the target image are determined according to the grayed image, and the quality value of the target image is determined according to the fuzziness and the shielding degree. The quality value of the target image can be efficiently and accurately determined through the fuzziness and the shielding degree of the target image, and the quality value can be embedded into various systems, so that the problems of low accuracy, poor universality and low robustness of the determination of the quality value of the image in the related technology can be solved, and the effects of efficiently and accurately determining the quality value of the image, strong universality and high robustness are achieved.
Drawings
Fig. 1 is a block diagram of a hardware configuration of a mobile terminal of an image quality value determination method according to an embodiment of the present invention;
fig. 2 is a flowchart of an image quality value determination method according to an embodiment of the present invention;
FIG. 3 is a flowchart of an image quality value determination method according to a specific embodiment of the present invention;
fig. 4 is a block diagram of the structure of an image quality value determination apparatus according to an embodiment of the present invention.
Detailed Description
Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings in conjunction with the embodiments.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The method embodiments provided in the embodiments of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Taking an example of the method running on a mobile terminal, fig. 1 is a block diagram of a hardware structure of the mobile terminal of a method for determining an image quality value according to an embodiment of the present invention. As shown in fig. 1, the mobile terminal may include one or more (only one shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), and a memory 104 for storing data, wherein the mobile terminal may further include a transmission device 106 for communication functions and an input-output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration, and does not limit the structure of the mobile terminal. For example, the mobile terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used to store a computer program, for example, a software program and a module of application software, such as a computer program corresponding to the image quality value determination method in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by executing the computer program stored in the memory 104, thereby implementing the method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the mobile terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.
In the present embodiment, there is provided an image quality value determination method, and fig. 2 is a flowchart of an image quality value determination method according to an embodiment of the present invention, as shown in fig. 2, the flowchart including the steps of:
step S202, carrying out graying processing on the acquired target image to obtain a grayed image;
step S204, determining the fuzziness of the target image based on the grayed image;
step S206, determining the shielding degree of the target image based on the grayed image;
step S208, determining a target quality value of the target image based on the ambiguity and the occlusion degree.
For example, the executing subject of the above steps may be a camera device, a background processor, or other devices with similar processing capabilities, and may also be a machine integrated with at least an image acquisition device and a data processing device, where the image acquisition device may include a graphics acquisition module such as a camera, and the data processing device may include a terminal such as a computer, a mobile phone, and the like, but is not limited thereto.
In the above-described embodiment, the target quality value of the target image may be determined by the image capturing apparatus, and the target image may be an image captured by the image capturing apparatus or an image in the input value image capturing apparatus. The target image may include a license plate, a person, an animal, and other objects.
According to the invention, after the target image is acquired, the target image is subjected to graying processing to obtain a grayed image, the fuzziness and the shielding degree of the target image are determined according to the grayed image, and the quality value of the target image is determined according to the fuzziness and the shielding degree. The quality value of the target image can be efficiently and accurately determined through the fuzziness and the shielding degree of the target image, and the quality value can be embedded into various systems, so that the problems of low accuracy, poor universality and low robustness of the determination of the quality value of the image in the related technology can be solved, and the effects of efficiently and accurately determining the quality value of the image, strong universality and high robustness are achieved.
In one exemplary embodiment, determining the degree of blur of the target image based on the grayed-out image comprises: determining a pixel matrix of the grayed image; acquiring a predetermined parameter matrix; performing convolution processing on the pixel matrix and the parameter matrix to obtain a target matrix; determining the ambiguity based on the objective matrix. In this embodiment, a pixel matrix of a grayed image can be obtained by performing graying processing on the target image. Then, convolution processing can be carried out on the pixel matrix and a predetermined parameter matrix to obtain a target matrix, and then the fuzziness of the target image is determined through the target matrix. The parameter matrix may be a second-order laplacian operator, for example, a 3 × 3 second-order laplacian operator. Performing convolution operation on a pixel matrix by using a 3 multiplied by 3 second-order Laplacian operator to obtain a target matrix, detecting image edge texture information, and determining the fuzziness of a target image through the target matrix. Wherein the second-order Laplace operator can be
It should be noted that the parameter matrix is only an implementation manner, the parameter matrix is not limited in the present invention, and the parameter matrix may be a self-defined parameter matrix, and may be adjusted according to an application scenario.
In one exemplary embodiment, determining the ambiguity based on the objective matrix comprises: determining a target pixel value of which a pixel value included in the target matrix is greater than a predetermined threshold; summing all pixel values included in the target pixel value to obtain a target pixel value sum; determining the blurriness based on the target pixel value sum. In the present embodiment, when determining the degree of blur from the target matrix, a target pixel value whose pixel value included in the target matrix is greater than a predetermined threshold may be determined first. The predetermined threshold may be a self-defined value, for example, the pixel value is 180 (this value is merely an exemplary illustration, and the present invention does not limit the predetermined threshold, for example, 170, 190, 200, etc. may also be taken). Summing the pixel values of which the pixel values are larger than a preset threshold value to obtain a target pixel value sum, and determining the fuzziness of the target image according to the target pixel value sum.
In one exemplary embodiment, determining the degree of blur based on the target pixel value comprises: acquiring the area of the target image; determining a ratio of the target pixel value sum to the area as the degree of blur. In this embodiment, when the target image is acquired, the length and the width of the target image may be determined, and then the area of the target image may be determined, and the ratio between the target pixel value and the area of the target image is determined as the degree of blur of the target image.
In one exemplary embodiment, determining the degree of occlusion of the target image based on the grayed-out image comprises: constructing a gray level histogram of the target image based on the grayed image; and determining the occlusion degree of the target image based on the gray level histogram. In this embodiment, after obtaining the grayed image of the target image, a grayscale histogram of the target image may be determined according to the grayed image, and the degree of occlusion of the target image may be determined according to the grayscale histogram.
In one exemplary embodiment, determining the degree of occlusion of the target image based on the gray histogram comprises: counting the pixel value distribution state in the gray level histogram to obtain a pixel value distribution matrix; determining a pixel value mean value and a pixel value variance of the pixel value distribution matrix; and determining the ratio of the pixel value mean to the pixel value variance as the occlusion degree. In this embodiment, a grayed histogram of the target image may be constructed according to a grayed image of the target image, a distribution state of image pixel values in the grayed histogram is counted to obtain a pixel value distribution matrix, and the degree of occlusion of the target image is determined by using signal-to-noise ratio quantization of the grayed histogram. That is, the pixel value mean and the pixel value variance of the pixel value distribution matrix can be calculated, and the ratio of the pixel value mean to the method is determined as the shielding degree of the target image.
In one exemplary embodiment, determining a target quality value for the target image based on the blurriness and the occlusion comprises: determining a sum of the blurriness and the occlusion as the target quality value; or determining a first weight corresponding to the ambiguity and a second weight corresponding to the occlusion; determining a first product of the ambiguity and the first weight, and a second product of the occlusion and the second weight; determining a sum of the first product and the second product as the target quality value. In this embodiment, after determining the degree of blur and the degree of occlusion of the target image, the sum of the degree of blur and the degree of occlusion of the target image may be determined as the target quality value of the target image. Then, different weights corresponding to the ambiguity and the occlusion degree can be determined according to the target image, and the target quality value of the target image can be determined by performing weighted summation according to the weights. For example, after the target image is acquired, the target image may be identified, the occlusion degree and the blur degree of the target image may be determined, and the blur degree and the weight corresponding to the occlusion degree may be determined according to the occlusion degree and the blur degree.
In one exemplary embodiment, after determining the target quality value of the target image based on the blurriness and the occlusion, the method further comprises: determining other quality values of other images, wherein the other images and the target image are images obtained after the same area is shot; determining the quality value with the maximum value from the other quality values and the target quality value, and determining the image corresponding to the quality value with the maximum value as a first image; determining feature information and/or trajectory information of a target object included in the first image based on the first image. In this embodiment, after the target quality value of the target image is determined, other quality values of other images may be determined, a quality value with the largest value is determined from the other quality values and the target quality value, and the image corresponding to the quality value is processed, for example, feature information of the target object in the image is identified, target tracking is performed according to the feature information, and trajectory information of the target object is determined.
The image quality value determination method is described below with reference to specific embodiments:
fig. 3 is a flowchart of an image quality value determination method according to an embodiment of the present invention, as shown in fig. 3, the method including:
in step S302, a license plate image I (corresponding to the target image) is input.
Step S304, preprocessing the current license plate image, acquiring the length and width information H and W of the image, and graying the image.
Step S306, calculating the ambiguity score S of the image Iblue. The calculation method is as follows:
firstly, convolution operation is carried out on a second-order Laplace operator (corresponding to the parameter matrix) of 3 multiplied by 3 and a gray image pixel matrix of the image I to obtain a matrix L (corresponding to the target matrix), and image edge texture information is detected. Wherein, the second-order laplacian is as follows:
secondly, counting pixel point values of which the pixel values are more than 180 in the matrix L, and summing to obtain SL(corresponding to the target pixel value sum described above). Will SLDividing the image length and width to obtain the ambiguity score S of the license plate image Iblue，
Step S308, calculating the shielding degree score S of the image Iblock. The calculation method is as follows:
firstly, constructing a gray level histogram I of an image IhistogramCounting the distribution of pixel values of the image;
secondly, the signal to noise ratio of the histogram is quantized, the mean value and the variance of the histogram matrix are calculated and divided to obtain an image shielding degree score Sblock，
Step S310, outputting the final image quality Score, Score ═ Sblur+Sblock。
In the foregoing embodiment, the quality score of the target image is quantified by detecting the conditions of blurring and occlusion in the target image; by applying various evaluation schemes such as image fuzzy recognition detection and image shielding degree evaluation, and combining image signals and frequency domain noise analysis, the quality score of the target image can be efficiently and accurately determined, and then the optimal quality image of each target license plate is selected for attribute analysis and recognition, so that the universality is good and the robustness is strong.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
In this embodiment, an image quality value determination apparatus is further provided, which is used to implement the foregoing embodiments and preferred embodiments, and the description of which is already given is omitted. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 4 is a block diagram of the configuration of an image quality value determination apparatus according to an embodiment of the present invention, as shown in fig. 4, the apparatus including:
the processing module 42 is configured to perform graying processing on the acquired target image to obtain a grayed image;
a first determining module 44 for determining a degree of blur of the target image based on the grayed image;
a second determining module 46 for determining the degree of occlusion of the target image based on the grayed image;
a third determination module 48 for determining a target quality value of the target image based on the blurriness and the occlusion.
In an exemplary embodiment, the first determination module 44 may determine the blur degree of the target image based on the grayed image by: determining a pixel matrix of the grayed image; acquiring a predetermined parameter matrix; performing convolution processing on the pixel matrix and the parameter matrix to obtain a target matrix; determining the ambiguity based on the objective matrix.
In an exemplary embodiment, the first determination module 44 may determine the ambiguity based on the objective matrix by: determining a target pixel value of which a pixel value included in the target matrix is greater than a predetermined threshold; summing all pixel values included in the target pixel value to obtain a target pixel value sum; determining the blurriness based on the target pixel value sum.
In an exemplary embodiment, the first determination module 44 may implement determining the blurriness based on the target pixel value and the sum of: acquiring the area of the target image; determining a ratio of the target pixel value sum to the area as the degree of blur.
In an exemplary embodiment, the second determination module 46 may determine the degree of occlusion of the target image based on the grayed image by: constructing a gray level histogram of the target image based on the grayed image; and determining the occlusion degree of the target image based on the gray level histogram.
In an exemplary embodiment, the second determining module 46 may determine the degree of occlusion of the target image based on the gray histogram by: counting the pixel value distribution state in the gray level histogram to obtain a pixel value distribution matrix; determining a pixel value mean value and a pixel value variance of the pixel value distribution matrix; and determining the ratio of the pixel value mean to the pixel value variance as the occlusion degree.
In an exemplary embodiment, the third determination module 48 may determine the target quality value of the target image based on the blurriness and the occlusion by: determining a sum of the blurriness and the occlusion as the target quality value; or determining a first weight corresponding to the ambiguity and a second weight corresponding to the occlusion; determining a first product of the ambiguity and the first weight, and a second product of the occlusion and the second weight; determining a sum of the first product and the second product as the target quality value.
In an exemplary embodiment, the apparatus may be configured to determine other quality values of other images after determining a target quality value of the target image based on the blurriness and the occlusion, where the other images and the target image are both images obtained after shooting the same area; determining the quality value with the maximum value from the other quality values and the target quality value, and determining the image corresponding to the quality value with the maximum value as a first image; determining feature information and/or trajectory information of a target object included in the first image based on the first image.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program is arranged to perform the steps of any of the above-mentioned method embodiments when executed.
In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
In an exemplary embodiment, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary embodiments, and details of this embodiment are not repeated herein.
It will be apparent to those skilled in the art that the various modules or steps of the invention described above may be implemented using a general purpose computing device, they may be centralized on a single computing device or distributed across a network of computing devices, and they may be implemented using program code executable by the computing devices, such that they may be stored in a memory device and executed by the computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into various integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (11)
1. An image quality value determination method, characterized by comprising:
carrying out graying processing on the acquired target image to obtain a grayed image;
determining a degree of blur of the target image based on the grayed image;
determining the degree of occlusion of the target image based on the grayed image;
determining a target quality value for the target image based on the blurriness and the occlusion.
2. The method of claim 1, wherein determining the blurriness of the target image based on the grayed-out image comprises:
determining a pixel matrix of the grayed image;
acquiring a predetermined parameter matrix;
performing convolution processing on the pixel matrix and the parameter matrix to obtain a target matrix;
determining the ambiguity based on the objective matrix.
3. The method of claim 2, wherein determining the ambiguity based on the objective matrix comprises:
determining a target pixel value of which a pixel value included in the target matrix is greater than a predetermined threshold;
summing all pixel values included in the target pixel value to obtain a target pixel value sum;
determining the blurriness based on the target pixel value sum.
4. The method of claim 3, wherein determining the blurriness based on the target pixel value and the determined blurriness comprises:
acquiring the area of the target image;
determining a ratio of the target pixel value sum to the area as the degree of blur.
5. The method of claim 1, wherein determining the degree of occlusion of the target image based on the grayed-out image comprises:
constructing a gray level histogram of the target image based on the grayed image;
and determining the occlusion degree of the target image based on the gray level histogram.
6. The method of claim 5, wherein determining the degree of occlusion of the target image based on the histogram of gray levels comprises:
counting the pixel value distribution state in the gray level histogram to obtain a pixel value distribution matrix;
determining a pixel value mean value and a pixel value variance of the pixel value distribution matrix;
and determining the ratio of the pixel value mean to the pixel value variance as the occlusion degree.
7. The method of claim 1, wherein determining a target quality value for the target image based on the blurriness and the occlusion comprises:
determining a sum of the blurriness and the occlusion as the target quality value;
or,
determining a first weight corresponding to the ambiguity and a second weight corresponding to the occlusion; determining a first product of the ambiguity and the first weight, and a second product of the occlusion and the second weight; determining a sum of the first product and the second product as the target quality value.
8. The method according to any one of claims 1 to 7, wherein after determining an object quality value for the target image based on the blurriness and the occlusion, the method further comprises:
determining other quality values of other images, wherein the other images and the target image are images obtained after the same area is shot;
determining the quality value with the maximum value from the other quality values and the target quality value, and determining the image corresponding to the quality value with the maximum value as a first image;
determining feature information and/or trajectory information of a target object included in the first image based on the first image.
9. An image quality value determination apparatus, characterized by comprising:
the processing module is used for carrying out graying processing on the acquired target image to obtain a grayed image;
a first determining module for determining a degree of blur of the target image based on the grayed image;
a second determination module for determining the degree of occlusion of the target image based on the grayed image;
a third determination module to determine a target quality value of the target image based on the blurriness and the occlusion.
10. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the method of any of claims 1 to 8 when executed.
11. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 8.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113962974A (en) * | 2021-10-27 | 2022-01-21 | 阿波罗智联(北京)科技有限公司 | Image processing method, image processing apparatus, electronic device, and medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8441548B1 (en) * | 2012-06-15 | 2013-05-14 | Google Inc. | Facial image quality assessment |
| CN107465912A (en) * | 2016-06-03 | 2017-12-12 | 中兴通讯股份有限公司 | A kind of imaging difference detection method and device |
| CN108269250A (en) * | 2017-12-27 | 2018-07-10 | 武汉烽火众智数字技术有限责任公司 | Method and apparatus based on convolutional neural networks assessment quality of human face image |
| CN109829859A (en) * | 2018-12-05 | 2019-05-31 | 平安科技（深圳）有限公司 | Image processing method and terminal device |
| CN111160284A (en) * | 2019-12-31 | 2020-05-15 | 苏州纳智天地智能科技有限公司 | Method, system, equipment and storage medium for evaluating quality of face photo |
| CN111160380A (en) * | 2018-11-07 | 2020-05-15 | 华为技术有限公司 | Method for generating video analysis model and video analysis system |
| CN111241925A (en) * | 2019-12-30 | 2020-06-05 | 新大陆数字技术股份有限公司 | Face quality evaluation method, system, electronic equipment and readable storage medium |
| CN111815606A (en) * | 2020-07-09 | 2020-10-23 | 浙江大华技术股份有限公司 | Image quality evaluation method, storage medium, and computing device |
-
2020
- 2020-11-24 CN CN202011334196.XA patent/CN112529845B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8441548B1 (en) * | 2012-06-15 | 2013-05-14 | Google Inc. | Facial image quality assessment |
| CN107465912A (en) * | 2016-06-03 | 2017-12-12 | 中兴通讯股份有限公司 | A kind of imaging difference detection method and device |
| CN108269250A (en) * | 2017-12-27 | 2018-07-10 | 武汉烽火众智数字技术有限责任公司 | Method and apparatus based on convolutional neural networks assessment quality of human face image |
| CN111160380A (en) * | 2018-11-07 | 2020-05-15 | 华为技术有限公司 | Method for generating video analysis model and video analysis system |
| CN109829859A (en) * | 2018-12-05 | 2019-05-31 | 平安科技（深圳）有限公司 | Image processing method and terminal device |
| CN111241925A (en) * | 2019-12-30 | 2020-06-05 | 新大陆数字技术股份有限公司 | Face quality evaluation method, system, electronic equipment and readable storage medium |
| CN111160284A (en) * | 2019-12-31 | 2020-05-15 | 苏州纳智天地智能科技有限公司 | Method, system, equipment and storage medium for evaluating quality of face photo |
| CN111815606A (en) * | 2020-07-09 | 2020-10-23 | 浙江大华技术股份有限公司 | Image quality evaluation method, storage medium, and computing device |
Non-Patent Citations (2)
| Title |
|---|
| NATHAN D.KALLA 等: "Image quality assessment for iris biometric", 《SPIE》, vol. 6202, 17 April 2006 (2006-04-17) * |
| 陈天文: "监控图像质量的无参评估方法研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》, vol. 2020, no. 03, 15 March 2020 (2020-03-15) * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113962974A (en) * | 2021-10-27 | 2022-01-21 | 阿波罗智联(北京)科技有限公司 | Image processing method, image processing apparatus, electronic device, and medium |
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