Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
IMAGE QUALITY DETERMINATION METHOD, APPARATUS, AND SYSTEM
Field
[0001] The present disclosure relates to image processing field, particularly
to an image quality
determination method, apparatus, and system.
Background
[0002] In financial business, such as when a company applies for a loan form a
financial institution, the
company business license needs to be provided to enter the system for
subsequent risk management
procedures. Using automatic machinery identification instead of manually entry
can greatly reduce entry
costs and improve entry efficiency.
[0003] After the existing system performs image correction, red badge removing
and other pre-processing,
then recognizing the image content, the pre-processing can improve the image
quality which the original
image is slanted and with red badge, thereby effectively improving the
recognition accuracy. However,
image quality suffers from the influence of miscellaneous factors, blur caused
by out of focus, paper texture,
excessive light and other factors will significantly reduce the image quality
and make the image content
difficult to accurately recognize.
[0004] In order to reduce the recognition errors caused by poor image quality
, before using the recognition
system to recognize the image content, firstly analyzing of the merits of
image quality, do not perform
processing on lower quality image, prompting the user to re-upload, therefore,
the accuracy of image quality
determination is very important, and the methods for determining image quality
in the prior art are
generalized image quality determination, although the image quality of the
license document can also be
determined, the image quality of the license document cannot be accurately
determined.
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Invention Content
[0005] The present application provides an image quality determination method,
apparatus and system
which can accurately determine the image quality of license document.
[0006] The present application provides the following solutions:
[0007] The first aspect provides an image quality determination method, the
method comprises:
[0008] Converting a received license document image into a grayscale image;
[0009] Determining edge pixels of the grayscale image, respectively judging
whether all edge pixels with
Roof structure in the edge pixels meet blur condition, calculating number of
edge pixels with Roof structure,
wherein the edge pixels meet the blur condition, and a ratio of the number of
this edge pixels to the number
of all edge pixels with Roof structure is an indicator of image blurriness;
[0010] Calculating an indicator of image texture noise according to pre-set
first calculation rule;
[0011] Calculating an indicator of image contrast according to pre-set second
calculation rule;
[0012] Determining image quality by using the indicator of blurriness, the
indicator of texture noise and
the indicator of contrast.
[0013] Furthermore, wherein the determination of edge pixels of the grayscale
image comprises:
[0014] Performing a third-order wavelet transform on the grayscale image,
extracting low frequency
components obtained by wavelet transform of each order;
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[0015] Determining edge pixels according to pre-set rule by using three low
frequency components.
[0016] Furthermore, wherein calculating an indicator of image texture noise
according to pre-set first
calculation rule comprises:
[0017] When performing the third-order wavelet transformation on the grayscale
image, extracting
diagonal direction component H Hi, horizontal direction component HLi and
vertical direction component
LHi obtained by performing first-order wavelet transformation on the grayscale
image;
[0018] Calculating the texture noise indicator 8,, by using following formula:
max[median(IHH11),medianaHL11),median(ILH11)]
[ 0019] 8 =
0.6745
[0020] Furthermore, wherein calculating an indicator of image contrast
according to pre-set second
calculation rule comprises:
[0021] After dividing the grayscale image into k1 x k2 blocks, respectively
calculating standard
deviation of each block by using the following formula:
E7_21(ii-02
[0022] a =
n2
[0023] Wherein, a is intensity standard deviation of all pixels in block, n is
number of pixels in block,
refers to intensity of each pixel in block, I refers to average intensity of
all pixels in block;
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[0024] Comparing the standard deviation of each block with a pre-set
sensitivity threshold, the blocks
with less standard deviation than the pre-set sensitivity threshold are
filtered to delete;
[0025] Calculating contrast indicator RME for the remaining blocks by using
following formular:
11+12: +inl
[0026] RME =
k
k1k2 i=1 1 loglIti-1-11+12+
[0027] Wherein, RME refers to roots' improvement, k1 and k2 respectively
refers to number of blocks
in each row and each column; i and j respectively refers to X-axis and Y-axis
of block in grayscale image;
refers to pixel intensity at midpoint of block; n refers to number of pixels
in each block, and /2 in
refers to intensity of each pixel in block.
[0028] Furthermore, determining image quality by using the indicator of
blurriness, the indicator of
texture noise and the indicator of contrast comprises:
[0029] Judging whether the indicator of blurriness is greater than a first
threshold, whether the indicator
of texture noise is greater than a second threshold, and whether the indicator
of contrast is less than the third
threshold, if yes, determining the image as low quality, if not, then
determining the image as high quality.
[0030] Determining image quality by using the indicator of blurriness, the
indicator of texture noise and
the indicator of contrast comprises:
[0031] Inputting the indicator of blurriness, the indicator of texture noise,
and the indicator of the contrast
as variables into a pre-trained first machine learning model, wherein a
training set in this pre-trained first
machine learning model is a collection of image sample data marked with image
quality;
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[0032] Determining the image quality by the pre-trained first machine learning
model.
[0033] Furthermore, determining image quality by using the indicator of
blurriness, the indicator of
texture noise and the indicator of contrast comprises:
[0034] Inputting the indicator of blurriness, the indicator of texture noise,
and the indicator of the contrast
as variables into a pre-trained second machine learning model, wherein a
training set in this pre-trained
second machine learning model is a collection of image sample data marked with
OCR accurate rate;
[0035] Calculating the estimated value of the OCR accurate rate of the image
according to the pre-trained
second machine learning model, if the estimated value of OCR accurate rate is
greater than a pre-set fourth
threshold , determining the image as high quality, otherwise, determining the
image as low quality.
[0036] Furthermore, the method also comprises:
[0037] Transmitting images to OCR recognition system for recognizing, wherein
the images meet quality
requirements.
[0038] The second aspect of the present application provides an image quality
determination apparatus,
comprising:
[0039] An image conversion unit configured to convert the received license
document image into a
grayscale image;
[0040] A first calculating unit configured to determine edge pixels of the
grayscale image, respectively
judging whether all edge pixels with Roof structure in the edge pixels meet
blur condition, calculating edge
pixel numbers with Roof structure, wherein the edge pixel meets the blur
condition, and a ratio of this edge
Date recue / Date received 202 1-1 1-25
pixel numbers to all edge pixels numbers with Roof structure is an indicator
of image blurriness;
[0041] A second calculation unit configured to calculate an indicator of image
texture noise according to
pre-set first calculation rule;
[0042] A third calculation unit configured to an indicator of image contrast
according to pre-set second
calculation rule;
[0043] A determining unit configured determining image quality by using the
indicator of blurriness, the
indicator of texture noise and the indicator of contrast.
[0044] The third aspect of the present application provides a computer system,
comprising:
[0045] One or plural processors; and
[0046] A memory associated with one or plural processors, the memory is
configured to store program
commands, if the program commands are executed by one or plural processors,
executing any above-
mentioned method.
[0047] According to the specific implementation provided in this application,
this application discloses
the following technical effects: the ratio of the number of edge pixels of the
conditional Roof structure to
the number of edge pixels of all the Roof structure is taken as the indicator
of blurriness, because most of
the content of the license document images are text content, and the strength
of both sides of the text edge
is the same, most of the edge pixels of the words are Roof structure, and only
considering whether the edge
pixels of the Roof structure meet the blur condition, which accurately judge
the blurriness of the words
font; calculating the indicator of the texture noise according to the pre-set
first calculation rule; calculating
the indicator of contrast according to the pre-set second calculation rule;
and the image quality can be
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Date recue / Date received 202 1-1 1-25
determined more accurately by using the indicator of blurriness, the indicator
of texture noise, and the
indicator of contrast to determine the image quality.
Drawing Description
[0048] In order to describe the technical solutions clearer in the
implementations of the present
application or the prior art, the following are drawings that need to be used
are briefly introduced.
Obviously, the drawings in the following description are only some
implementations of the application, for
those of ordinary skill in the art , without creative work, they can also
obtain other drawings based on these
drawings.
[0049] Figure 1 is a process diagram of an image quality determination method
in implementation 1 of
the present application;
[0050] Figure 2 is a structural diagram of an image quality determination
apparatus in implementation 2
of the present application;
[0051] Figure 3 is a structural diagram of computer system in implementation 3
of the present application.
Specific implementation methods
[0052] The following will describe the technical solutions of the
implementations in the present
application with accompanying drawings, obviously the described
implementations are only a part of the
implementations in the present application. Based on the implementations in
the present application, all
other implementations obtained by those of ordinary skilled in the art will
fall in the protection scope of the
present application.
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[0053] As the above-mentioned in the technical background, in order to reduce
the recognition errors
caused by poor image quality , before using OCR to recognize the image
content, firstly analyzing of the
merits of image quality, do not perform processing on lower quality image,
prompting the user to re-upload,
therefore, the accuracy of image quality determination is very important, and
the methods for determining
image quality in the prior art are generalized image quality determination,
although the image quality of
the license document can also be determined, it is not targeted and the image
quality of the license document
cannot be accurately determined.
[0054] The present application provides an image quality determination method
with calculating the ratio
of the number of edge pixels of the conditional Roof structure to the number
of edge pixels of all
the Roof structure, the ratio is taken as the indicator of blurriness, because
most of the content of the license
document images are text content, and the strength of both sides of the text
edge is the same, most of the
edge pixels of the words are Roof structure, and only considering whether the
edge pixels of
the Roof structure meet the blur condition, which accurately judge the
blurriness of the words font;
calculating the indicator of the texture noise according to the pre-set first
calculation rule; calculating the
indicator of contrast according to the pre-set second calculation rule; and
the image quality can be
determined more accurately by using the indicator of blurriness, the indicator
of texture noise, and the
indicator of contrast to determine the image quality.
[0055] Implementation 1
[0056] The present application provides an image quality determination method,
and the method is applied
to an image quality determination apparatus as an example, the apparatus can
be configured in any computer
device, so that the computer device can execute image quality determination
method.
[0057] As shown in Figure 1, the above-mentioned method comprises:
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[0058] S11, converting a received license document image into a grayscale
image;
[0059] S12, determining edge pixels of the grayscale image, respectively
judging whether all edge pixels
with Roof structure in the edge pixels meet blur condition, calculating number
of edge pixels with Roof
structure, wherein the edge pixels meet the blur condition, and a ratio of the
number of this edge pixels to
the number of all edge pixels with Roof structure is an indicator of image
blurriness;
[0060]
There are four types of edge pixels: Dirac structure, Astep structure, Gstep
structure
and Roof structure, due to most of the image content of the license document
are text content, and the
strength of both sides of the text edge are the same, so the edge of the text
pixels are mostly with
Roof structure, only considering whether the edge pixels of the Roof structure
meet the blur condition,
which can be more accurately determining the font blurriness, so calculating a
ratio of the number of edge
pixels with the Roof structure that meets the blurriness condition to the
number of all edge pixels with Roof
structure, this ratio is taken to make image blun-iness quantization, as an
indicator of the image
blurriness, wherein the determination of whether the edge pixels with Roof
structure meet the blur
condition is based on whether the strongness of the edge pixels in the first-
order low-frequency component
is less than the pre-set threshold which can determine whether it is blurry.
[0061] S13, calculating an indicator of image texture noise according to pre-
set first calculation rule;
[0062] S14, calculating an indicator of image contrast according to pre-set
second calculation rule;
[0063] Because image quality suffers from the influence of miscellaneous
factors, blur caused by out of
focus, paper texture, excessive light and other factors will significantly
reduce the image quality, therefore,
when determining image quality, comprehensively considering is required to
respectively calculate the
indicator of texture noise and contrast.
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[0064] S15, determining image quality by using the indicator of blurriness,
the indicator of texture noise
and the indicator of contrast.
[0065] The determination of the edge pixels of the grayscale image comprises:
[0066] Performing a third-order wavelet transform on the grayscale image,
extracting low frequency
components obtained by wavelet transform of each order;
[0067] Determining edge pixels according to pre-set rule by using three low
frequency components.
[0068] Determining edge pixels of a grayscale image, it is necessary to
perform a third-order wavelet
transform on the grayscale image, each wavelet transformation will obtain one
low-frequency component,
performing third-order wavelet transform will obtain three low-frequency
components, determining the
edge pixels by using the three low-frequency components according to pre-set
rule.
[0069] Calculating an indicator of image texture noise according to pre-set
first calculation rule
comprises:
[0070] When performing the third-order wavelet transformation on the grayscale
image, extracting
diagonal direction component H Hi, horizontal direction component HLi and
vertical direction component
LHi obtained by performing first-order wavelet transformation on the grayscale
image;
[0071] Calculating the texture noise indicator 6,, by using following formula:
0072] = max[median(IH Hil),median(IHL11),median(ILHi 0]
[ 8
0.6745
[0073] Due to factors such as paper folding, moisture or camera wave pattern,
a series of texture noises
Date recue / Date received 202 1-1 1-25
will be generated in the image. These texture noises often block text content,
directly affect the detection
of text content by the detection model, and seriously affect OCR accuracy
rate, because the texture noise
of the image of the license document is directional , the texture noise on the
diagonal, the horizontal and
the vertical directions of the image need to be considered comprehensively ,
taking in the strongest three
texture noise on the three direction as the image texture noise, it can be
more accurately determining the
image quality, so taking the maximum value of the median absolute value among
the diagonal direction
component HHi , horizontal direction component HLi and vertical direction
component LHi when
calculation.
[0074] Calculating an indicator of image contrast according to pre-set second
calculation rule comprises:
[0075] After dividing the grayscale image into k1 x k2 blocks, respectively
calculating standard
deviation of each block by using the following formula:
n2 E =
[0076] a = _
n2
[0077] Wherein, a is intensity standard deviation of all pixels in block, n is
number of pixels in block,
refers to intensity of each pixel in block, I refers to average intensity of
all pixels in block;
[0078] Comparing the standard deviation of each block with a pre-set
sensitivity threshold, the blocks
with less standard deviation than the pre-set sensitivity threshold are
filtered to delete;
[0079] Calculating contrast indicator RME for the remaining blocks by using
following formular:
11+12: +inl
[0080] RME =
k
kik2 i=1 loglIti+11+12+ 11
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[0081] Wherein, RME(Root Mean Enhance) refers to roots' improvement, k1 and k2
respectively
refers to number of blocks in each row and each column; i and j respectively
refers to X-axis and Y-axis of
block in grayscale image; I refers to pixel intensity at midpoint of block; n
refers to number of pixels in
each block, and 12 In refers to intensity of each pixel in block.
[0082] In a low-contrast document image, the text is generally lighter, and
the distinction between the
paper background is low, and it can generally be detected correctly, but it
will affect the accuracy of
recognition. For the image of license documents, the part without text content
often have no big difference,
and the contrast of this part does not need to be considered, so after
dividing the grayscale image
into k1 x k2 blocks , calculating the standard deviation of each block,
comparing the standard deviation of
each block with a pre-set sensitivity threshold, the blocks with less standard
deviation than the pre-set
sensitivity threshold are filtered to delete, regardless of the contrast of
this part which can filter out the pure
paper background part and focus on calculating the contrast between the text
and the background to more
accurately determine the image quality.
[0083] Determining image quality by using the indicator of blurriness, the
indicator of texture noise and
the indicator of contrast comprises:
[0084] Judging whether the indicator of blurriness is greater than a first
threshold, whether the indicator
of texture noise is greater than a second threshold, and whether the indicator
of contrast is less than the third
threshold, if yes, determining the image as low quality, if not, then
determining the image as high quality.
[0085] When determining the image quality, respectively determining the
indicator of blurriness, the
indicator of texture noise and the indicator of contrast, and the indicator of
the first threshold value, the
second threshold value and the third threshold value, which are suitable for
large amount of data and no
marked image source.
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[0086] Determining image quality by using the indicator of blurriness, the
indicator of texture noise and
the indicator of contrast comprises:
[0087] Inputting the indicator of blurriness, the indicator of texture noise,
and the indicator of the contrast
as variables into a pre-trained first machine learning model, wherein a
training set in this pre-trained first
machine learning model is a collection of image sample data marked with image
quality;
[0088] Determining the image quality by the pre-trained first machine learning
model.
[0089] When image quality is judged by threshold, only a single indicator is
considered each time,
which will cause inaccuracy of the determination of image quality, for
example, an image has a certain
level of texture noise , blurriness and low contrast at the same time, but
which have not reached the
threshold, the image will be determined to be high quality , but not easy to
recognize, for OCR recognition
system, the image is low quality, therefore, at the beginning of the
development of the OCR recognition
system, by marking the image as 'high quality' and low quality' manually to
transform the problem to the
machine learning's binary classification task which can quickly and accurately
determine the image quality.
[0090] Determining image quality by using the indicator of blurriness, the
indicator of texture noise and
the indicator of contrast comprises:
[0091] Inputting the indicator of blurriness, the indicator of texture noise,
and the indicator of the contrast
as variables into a pre-trained second machine learning model, wherein a
training set in this pre-trained
second machine learning model is a collection of image sample data marked with
OCR accurate rate;
[0092] Calculating the estimated value of the OCR accurate rate of the image
according to the pre-trained
second machine learning model, if the estimated value of OCR accurate rate is
greater than a pre-set fourth
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threshold , determining the image as high quality, otherwise, determining the
image as low quality.
[0093] When manually marking image as 'high quality' and low quality', there
will be problem of
subjective judgments by observers, when a poor-quality image appears after a
poor-quality image, usually
will be marked as 'high quality', in addition, some low-quality features for
people may not affect the
recognition effect of the OCR recognition system, such as slanted angle, old
papers and so on. Therefore,
the OCR accuracy rate can be used as a mark to convert the problem into
machine
learning's OCR accuracy rate prediction, the OCR recognition system recognizes
the image, the image has
been marked with the correct text content, and calculating the accurate rate
of OCR by comparing the
recognized text content and marked text content, regarding the OCR accurate
rate as a tag image of the
sample, using the image sample data set that already marked with OCR accurate
rate to train the second
machine learning model, using the trained second machine learning model to
calculate the OCR accurate
rate of the image, the higher OCR accurate rate, the higher image quality.
[0094] The method also comprises:
[0095] Transmitting images to OCR recognition system for recognizing, wherein
the images meet quality
requirements.
[0096] Implementation 2
[0097] Corresponding to the above-mentioned method, the present application
provides an image quality
determination apparatus, as shown in Figure 2, comprising:
[0098] An image conversion unit 21 configured to convert the received license
document image
into a grayscale image;
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[0099]
A first calculating unit 22 configured to determine edge pixels of the
grayscale image,
respectively judging whether all edge pixels with Roof structure in the edge
pixels meet blur condition,
calculating edge pixel numbers with Roof structure, wherein the edge pixel
meets the blur condition, and a
ratio of this edge pixel numbers to all edge pixels numbers with Roof
structure is an indicator of image
blurriness;
[0100]
There are four types of edge pixels: Dirac structure, Astep structure, Gstep
structure
and Roof structure, due to most of the image content of the license document
are text content, and the
strength of both sides of the text edge are the same, so the edge of the text
pixels are mostly with
Roof structure, only considering whether the edge pixels of the Roof structure
meet the blur condition,
which can be more accurately determining the font blurriness, so calculating a
ratio of the number of edge
pixels with the Roof structure that meets the blurriness condition to the
number of all edge pixels with Roof
structure, this ratio is taken to make image blurriness quantization, as the
indicator of image blurriness.
[0101] A second calculation unit 23 configured to calculate an indicator of
image texture noise according
to pre-set first calculation rule;
[0102] A third calculation unit 24 configured to an indicator of image
contrast according to pre-set second
calculation rule;
[0103] Because image quality suffers from the influence of miscellaneous
factors, blur caused by out of
focus, paper texture, excessive light and other factors will significantly
reduce the image quality, therefore,
when determining image quality, comprehensively considering is required to
respectively calculate the
indicator of texture noise and contrast.
[0104] A determining unit 25 configured determining image quality by using the
indicator of blurriness,
the indicator of texture noise and the indicator of contrast.
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[0105] The implementation of the present application provides an image quality
determining apparatus,
which belongs to the same application concept with the image quality method
provided by the
implementation of the present application, the image quality determination
method provided in the
implementation of this application can be executed, which will have
correspondingly functional modules
and beneficial effects of this image quality determination method. For the
technical details that are not
described in this implementation can refer to the image quality determination
method provided in the
implementation of this application, which will not be repeated here.
[0106] Implementation 3
[0107] Corresponding to the above-mentioned method and apparatus, the
implementation 3 of the present
application provides a computer system, comprising:
[0108] One or plural processors; and
[0109] A memory associated with one or plural processors, the memory is
configured to store program
commands, if the program commands are executed by one or plural processors,
executing the method step
in implementation 1, such as following steps:
[0110] Converting a received license document image into a grayscale image;
[0111] Determining edge pixels of the grayscale image, respectively judging
whether all edge pixels with
Roof structure in the edge pixels meet blur condition, calculating number of
edge pixels with Roof structure,
wherein the edge pixels meet the blur condition, and a ratio of the number of
this edge pixels to the number
of all edge pixels with Roof structure is an indicator of image blurriness;
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[0112] Calculating an indicator of image texture noise according to pre-set
first calculation rule;
[0113] Calculating an indicator of image contrast according to pre-set second
calculation rule;
[0114] Determining image quality by using the indicator of blurriness, the
indicator of texture noise and
the indicator of contrast.
[0115] Wherein, Figure 3 exemplarily shows the architecture of the computer
system , which can
specifically include a processor 1510, video display adapter 1511, disk driver
1512, input/output
interface 1513, network interface 1514, and memory 1520. The above-mentioned
processor 1510, video
display adapter 1511, disk driver 1512, input/output interface 1513, network
interface 1514 and
memory 1520 can be connected through a communication bus 1530.
[0116] Wherein, the processor 1510 can be achieved by using a general CPU
(Central Processing Unit),
Microprocessor, Application Specific Integrated Circuit (ASIC) , or one or
more integrated circuits, which
are used to execute some relative program to achieve the technical solutions
provided in this application.
[0117]
The memory 1520 can adopt ROM ( Read Only Memory), RAM (Random Access
Memory), static storage devices and dynamic storage devices to achieve. The
memory 1520 can store
operate system 1521 used to control the running of the computer system 1500,
used to control the low-level
operation of the computer system 1500's Basic Input Output System (BIOS) 1522.
In addition, storing a
web browser 1523, data storage management 1524, and icon font processing
system 1525 and so on. The
above-mentioned icon font processing system 1525 can be the specific
application that implements the
above-mentioned steps. To sum up, when achieving the technical solutions
provided by this application
through software or firmware, related program codes are stored in the memory
1520 and executed by a
processor 1510.
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[0118] Input / output interface 1513 is used for connecting input / output
modules to achieve the
information input and output. Input / output module can be configured in the
device as a component ( not
shown in the figure), or it can be connected to the device to provide
corresponding functions. Wherein,
Input devices can include keyboards, mice, touch screens, microphones, various
sensors, etc., and output
devices can include monitors, speakers, vibrators, lights and so on.
[0119] The network interface 1514 is used to connect a communication module
(not shown in the
figure) to achieve the communication interaction between this device and other
devices. Wherein, the
communication module can achieve communication through wired means (such as
USB , network cable,
etc. ) , or through wireless methods ( such as mobile network, WIFI,
Bluetooth, etc.) to achieve
communication.
[0120] The bus 1530 includes a path and transmits information among various
components of the device
(such as the processor 1510 , the video display adapter 1511, the disk driver
1512, the input/output
interface 1513, the network interface 1514, and the memory 1520).
[0121] In addition, the computer system 1500 also can obtain information with
specific receiving
conditions from the virtual resource object's receiving condition information
database 1541 for condition
judgement and son on.
[0122] It should be noted that although the above device only shows the
processor 1510, the video
display adapter 1511, the disk driver 1512, input/output
interface 1513, network
interface 1514, memory 1520, bus 1530, etc., but in the process of the
specific implementation, the device
may also include other essential components for normal operation. In addition,
those skilled in the art can
understand that the above apparatus can comprise only the essential components
of the present application
to achieve the implementation, but there is no need to contain all the
components as shown in figure.
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[O123] Known from the description of the above implementations that those
skilled in the art can clearly
understand that the application can be achieved with the help of software and
essential general hardware
platform. Based on this understanding, the essence of the technical solution
of this application, or in other
words, the part that contributes to the existing technology can be implemented
in the form of a software
product, the computer software product can be stored in storage media, such as
ROM/RAM, magnetic
disks, optical disks, etc., including several commands to make a computer
device (can be a personal
computer, a cloud server, or a network device, etc.) to execute the methods
described in each
implementation or some of the implementations of the present application.
[0124] The various implementations in this description are described in a
progressive manner, the same
and similar parts among the various implementations can be referred to each
other separately, and each
implementation focuses on the differences compared with the other
implementations. Especially for the
concern of the system or the system implementations, since it is basically
similar to the implementation
method, the description is relatively simple. For related details, please
refer to the implementation
method. The system and system implementations described in the above are only
illustrative, and the units
described by separate parts may or may not be physically separate, and the
parts displayed as units may or
may not be physical units, which means, it can be in one place, or it may be
distributed to plural network
units. Some or all the modules are selected according to actual needs to
achieve the implementation's
solution purpose. The ordinary skill in the art can understand and implement
without creative work.
[0125] The image quality determination method, apparatus, and system provided
by this application
are described in detail in the above. Specific examples are used to illustrate
the principle and
implementation of this application. The description of the above examples is
only for helping to understand
the methods and core ideas of this application; at the same time, for those of
ordinary skill in the art,
according to the ideas of this application, there will be changes in the
specific implementations and the
scopes. In summary, the content of this description should not be taken to be
the restrictions of the present
application.
19
Date recue / Date received 202 1-1 1-25
