CN114882042B - Internet of things transmission method based on image quality evaluation - Google Patents

Internet of things transmission method based on image quality evaluation Download PDF

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CN114882042B
CN114882042B CN202210811735.7A CN202210811735A CN114882042B CN 114882042 B CN114882042 B CN 114882042B CN 202210811735 A CN202210811735 A CN 202210811735A CN 114882042 B CN114882042 B CN 114882042B
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CN114882042A (en
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黄舒琴
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Jiangsu Bro Information Technology Co ltd
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Abstract

The invention relates to the field of image transmission, in particular to an internet of things transmission method based on image quality evaluation. Carrying out gray processing on an acquired image to obtain a gray image, and obtaining a gray histogram by using the gray image; dividing the gray level histogram into different region intervals according to the gray level, acquiring the frequency number of each gray level and the frequency of each region interval in the gray level histogram, and calculating the contrast and the distribution degree of the acquired image according to the frequency number of each gray level and the frequency of each region interval; evaluating the image quality by using the acquired image contrast and distribution; and selecting a proper image frame number according to the evaluation result to transmit the image. The invention evaluates the image quality according to the characteristics of the image gray level image, selectively transmits the image, solves the problem of overlarge image transmission quantity, has fewer and simple factors related to the gray level image, can reduce the complexity of calculation, and solves the problem that a complex calculation method cannot be carried out due to limited calculation capability of a camera acquisition end.

Description

Internet of things transmission method based on image quality evaluation
Technical Field
The invention relates to the field of image transmission, in particular to an internet of things transmission method based on image quality evaluation.
Background
In industrial production, images on a production line are often required to be acquired, a real-time image transmission technology is required, products in the production process can be monitored in real time through the real-time image transmission technology, problems occurring in the production process can be found in time, and therefore real-time monitoring on the product line is very necessary.
The image real-time transmission technology is to connect a computer and an industrial camera and transmit images in a multi-end acquisition camera to the computer by using a broadband, but in the transmission process, the limitation of the broadband causes that the data volume of the images acquired by a plurality of camera acquisition ends at the same time is too large, the problems that the normal operation cannot be performed or the operation time is too long may occur in the transmission process, and at this time, a plurality of images acquired by the camera are selectively transmitted according to the data volume which can be transmitted by the bandwidth.
However, when some existing technologies transmit images and cannot transmit all the images at the same time under the influence of broadband, the existing technologies transmit the images by compressing the images or cutting the images into blocks and restoring the images after transmission; some adopted calculation methods are too complex, the calculation capacity of the camera acquisition end is limited, the problem that the transmission working efficiency is low due to the fact that the calculation capacity of the camera acquisition end is not enough to support an excessively complex algorithm is solved, and therefore a simple algorithm is needed to support the camera acquisition end to evaluate the image quality, and the transmission efficiency is improved.
Disclosure of Invention
The invention provides an Internet of things transmission method based on image quality evaluation, which aims to solve the problems that the image transmission process cannot normally run or the running time is too long due to the fact that the existing broadband transmission is limited and the image data volume acquired by a plurality of cameras at the same time exceeds the transmission data volume which can be supported by the broadband, and the image transmission efficiency is improved.
Furthermore, because the computing power of the camera acquisition end is limited, and too complex computation needs a large amount of time to be carried out, the method utilizes the characteristics of the gray-scale map to analyze the image quality, has fewer related parameters, is simple and convenient to use, can effectively reduce the complexity of the algorithm of the camera acquisition end, and reduces the computation time of the camera acquisition end.
The internet of things transmission method based on image quality evaluation adopts the following technical scheme, and comprises the following steps:
carrying out graying processing on images acquired by a plurality of sections of cameras at the same time to obtain gray level images, and calculating by utilizing the gray level images to obtain gray level histograms of all the acquired images;
dividing each gray level histogram into different areas according to the gray level;
calculating the contrast of each acquired image according to the range of the gray level in each gray level histogram;
calculating the cutting rate of each gray histogram according to the frequency of the gray level of each gray histogram and the prediction frequency;
after smoothing processing is carried out on each gray level histogram, interval division is carried out according to the trough of the smoothed gray level histogram, and the approximate normal distribution rate of each gray level histogram is calculated according to the skewness and the kurtosis of each interval;
calculating the brightness difference rate of each gray level histogram by using the frequency of the gray level of pure white and pure black in each gray level histogram and the frequency of the white area and the black area;
acquiring the distribution degree of the acquired image corresponding to each gray level histogram by using the cutoff rate, the approximate normal distribution rate and the brightness difference rate of each gray level histogram;
evaluating the quality of all the acquired images by using the obtained contrast and distribution of each acquired image;
and carrying out priority sequencing on the collected images by utilizing the image quality evaluation information, and transmitting the collected images according to the priority sequence, wherein the number of the transmitted images is the data amount which can be transmitted by the bandwidth.
The evaluation parameters of the image quality are the contrast and the distribution degree of each acquired image, and the image quality evaluation value is the sum of the contrast and the distribution degree of each acquired image.
The calculation formula for acquiring the image contrast is as follows:
Figure 883205DEST_PATH_IMAGE002
in the formula:
Figure DEST_PATH_IMAGE003
in order to acquire the contrast of the image,
Figure 155923DEST_PATH_IMAGE004
is the maximum value of the grey levels in the grey histogram,
Figure DEST_PATH_IMAGE005
is the minimum value of the gray levels in the gray histogram.
The distribution degree of the collected images is comprehensively calculated according to the cutoff rate, the approximate normal distribution rate and the brightness difference rate of the gray level histogram;
the calculation formula is as follows:
Figure DEST_PATH_IMAGE007
in the formula:
Figure 39566DEST_PATH_IMAGE008
in order to acquire the degree of distribution of the image,
Figure DEST_PATH_IMAGE009
is the cut-off rate of the gray-scale histogram,
Figure 44431DEST_PATH_IMAGE010
is an approximately normal distribution ratio of the gray histogram,
Figure DEST_PATH_IMAGE011
is the light-dark difference rate of the grey level histogram.
The method for calculating the cutoff rate of the gray histogram is as follows:
Figure 810262DEST_PATH_IMAGE012
in the formula:
Figure 836073DEST_PATH_IMAGE009
a cut-off rate that is a gray histogram;
Figure DEST_PATH_IMAGE013
is a natural constant and is the base number of a natural logarithm;
Figure 370959DEST_PATH_IMAGE014
is the frequency count with a gray level of 0,
Figure DEST_PATH_IMAGE015
the prediction frequency is 0 gray level;
Figure 281146DEST_PATH_IMAGE016
for a frequency count with a gray level of 255,
Figure DEST_PATH_IMAGE017
the prediction frequency is 255 gray level;
wherein:
Figure DEST_PATH_IMAGE019
Figure DEST_PATH_IMAGE021
in the formula:
Figure 534273DEST_PATH_IMAGE022
is the frequency count with a gray level of 1,
Figure DEST_PATH_IMAGE023
is the frequency count for a gray scale level of 254,
Figure 820898DEST_PATH_IMAGE024
in order to be a gray scale level,
Figure DEST_PATH_IMAGE025
is a gray scale
Figure 475870DEST_PATH_IMAGE024
The frequency of (c).
The method for calculating the approximate normal distribution rate of the gray level histogram is as follows:
the method comprises the steps of smoothing the gray level histogram to obtain a smoothed gray level histogram by calculating the average frequency of each gray level and the gray levels on the left side and the right side of the gray level histogram, obtaining the gray levels corresponding to wave troughs of the smoothed gray level histogram, arranging the gray levels corresponding to the wave troughs in sequence, and dividing wave trough gray level intervals to obtain an interval set;
and (3) calculating the skewness and kurtosis of the gray level histogram for each divided interval:
the skewness calculation formula is as follows:
Figure DEST_PATH_IMAGE027
in the formula:
Figure 822538DEST_PATH_IMAGE028
is the skewness of the gray-scale histogram,
Figure DEST_PATH_IMAGE029
the gray levels corresponding to the valleys of the image,
Figure 297382DEST_PATH_IMAGE024
in order to be a gray scale level,
Figure 653277DEST_PATH_IMAGE030
is a gray scale
Figure DEST_PATH_IMAGE031
The frequency of (a) to (b) is,
Figure 100439DEST_PATH_IMAGE032
as valley gray level interval
Figure DEST_PATH_IMAGE033
The average of the frequency counts of the gray levels in (a),
Figure 618008DEST_PATH_IMAGE034
as valley gray level interval
Figure 111306DEST_PATH_IMAGE033
Standard deviation of frequency of each gray level;
the kurtosis is calculated as follows:
Figure 146258DEST_PATH_IMAGE036
in the formula:
Figure DEST_PATH_IMAGE037
kurtosis of the gray level histogram;
and calculating the approximate normal distribution rate of the image according to the skewness and kurtosis of the calculated gray level histogram, wherein the calculation formula is as follows:
Figure DEST_PATH_IMAGE039
in the formula:
Figure 306981DEST_PATH_IMAGE010
is an approximately normal distribution ratio of the image,
Figure 526610DEST_PATH_IMAGE040
is the base of the set of intervals,
Figure 382570DEST_PATH_IMAGE013
is the base of the natural logarithm and is a natural constant.
The calculation formula of the brightness difference rate of the gray level histogram is as follows:
Figure 345847DEST_PATH_IMAGE042
in the formula:
Figure 970863DEST_PATH_IMAGE011
is the light-to-dark difference ratio of the gray histogram,
Figure DEST_PATH_IMAGE043
is the frequency of a gray level of 0 i.e. a pure black pixel,
Figure 158131DEST_PATH_IMAGE044
at a grey level of 255 i.e. the frequency of a pure white pixel,
Figure DEST_PATH_IMAGE045
is a white area
Figure 829284DEST_PATH_IMAGE046
The frequency of the interval of the region is,
Figure DEST_PATH_IMAGE047
a region interval frequency that is a black region;
the frequency calculation formula of the gray scale is as follows:
Figure DEST_PATH_IMAGE049
in the formula:
Figure 799514DEST_PATH_IMAGE024
in the form of a gray scale level,
Figure 403670DEST_PATH_IMAGE050
is a gray scale
Figure 840468DEST_PATH_IMAGE024
The frequency of (a) of (b) is,
Figure 530075DEST_PATH_IMAGE025
is a gray scale
Figure 444942DEST_PATH_IMAGE024
The frequency of (a) to (b) is,
Figure DEST_PATH_IMAGE051
in order to be the resolution of the grayscale image,
Figure 638026DEST_PATH_IMAGE052
is the number of gray levels.
The region interval frequency calculation formula is as follows:
Figure 573621DEST_PATH_IMAGE054
in the formula:
Figure DEST_PATH_IMAGE055
is the frequency of the region interval of the region,
Figure 219366DEST_PATH_IMAGE056
is the range of the region.
The method for acquiring the bandwidth transmission data volume comprises the following steps:
Figure 62557DEST_PATH_IMAGE058
in the formula:
Figure DEST_PATH_IMAGE059
the number of frames for the image transmission,
Figure 110147DEST_PATH_IMAGE051
in order to be able to determine the resolution of the image,
Figure 888747DEST_PATH_IMAGE060
for the size of each of the pixels of the image,
Figure DEST_PATH_IMAGE061
in order to be a wide band,
Figure 287368DEST_PATH_IMAGE062
indicating rounding down the resulting frame number.
The invention has the beneficial effects that: the invention utilizes image processing to carry out gray processing on the image collected by the camera, utilizes the gray value histogram of the gray image to analyze the image, evaluates the image quality of the collected image according to the characteristics of the gray image, selectively transmits the image according to the image quality, and can effectively reduce the influence that the image transmission process cannot normally run due to too large image data volume; parameters related to the characteristics of the gray level map are few, and the quality of the image is evaluated by using the characteristics of the gray level map, so that the complexity of calculation can be reduced, and the calculation time of a camera acquisition end can be reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of the process of the present invention;
fig. 2 is a flowchart of an internet of things transmission method based on image quality evaluation according to the embodiment;
fig. 3 is a flowchart of an internet of things transmission method based on image quality evaluation according to the embodiment;
FIG. 4 is a schematic diagram of a gray level histogram in the present embodiment;
FIG. 5 is a gray level histogram of the divided regions in the present embodiment;
FIG. 6 is a diagram of an under-exposed or over-exposed image and its histogram of gray levels in the present embodiment;
fig. 7 shows an image with an excessive difference between brightness and darkness and a histogram of gray levels thereof in this embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
An embodiment of the internet of things transmission method based on image quality evaluation according to the present invention is shown in fig. 1 and fig. 2, and is explained below:
and S101, carrying out gray processing on the acquired collected image to obtain a gray image of the collected image.
The gray scale map is also called a gray scale map. The relationship between white and black is logarithmically divided into several levels, called gray scale. The gray scale is divided into 256 steps. An image represented in grayscale is referred to as a grayscale map.
And S102, acquiring a gray level image of the acquired image, and drawing a gray level histogram of the image according to the gray level image of the acquired image.
The gray histogram is to count the occurrence frequency of all pixels in the digital image according to the size of the gray value.
And S103, dividing the gray level histogram into different region intervals according to the gray level, and calculating the frequency of each gray level and the frequency of each region interval in the gray level histogram.
Different region intervals represent different tones, and seven regions are divided according to a gray level space; and calculating the frequency of each gray level and each area interval according to the frequency of each gray level.
And S104, calculating the contrast of the acquired image by using the range of the gray level.
The contrast refers to the measurement of different brightness levels between the brightest white and the darkest black of a bright and dark area in an image, the contrast of an acquired image is one of the measurement standards of the image quality, and the image quality can be objectively judged by evaluating the quality of the image according to the contrast of the acquired image.
And S105, comprehensively calculating the distribution degree of the acquired image by utilizing the cutoff rate, the approximate normal distribution rate and the brightness difference rate of the gray level histogram.
The distribution degree is one of the characteristics of the gray level histogram, can be used for measuring the quality of the image, and can be comprehensively calculated by calculating the cutoff rate, the approximate normal distribution rate and the brightness difference rate of the gray level histogram, so that the distribution degree of the collected image is calculated in a large range, and the image quality can be effectively judged.
S106, calculating the cutoff rate, the approximate normal distribution rate and the brightness difference rate of the gray level histograms according to the characteristic quantity of each gray level histogram.
When the image has the darkest area or the brightest area, namely the image has an underexposure or overexposure phenomenon, the image is shown to be cut off at the leftmost side or the rightmost side on the gray level histogram of the image, namely the curve is sharply increased, the data of the gray level histogram are analyzed, and the cut-off rate of the gray level histogram is obtained;
when the image is under-exposed or over-exposed, the image shows that a large number of dark areas or bright areas exist, the gray level difference of the gray histogram is analyzed, and the brightness difference rate of the gray histogram is obtained;
the gray level histogram of an ideal image is normally distributed, but in practice, the curve of the gray level histogram is not completely normally distributed, a plurality of peaks and troughs exist, the gray level histogram is divided into a plurality of intervals by the aid of the peaks and the troughs, the conditions of the intervals are analyzed, and the approximate normal distribution rate of the intervals is obtained;
and comprehensively calculating the distribution degree of the acquired image according to the cutoff rate, the brightness difference rate and the approximate normal distribution rate of the obtained gray level histogram to evaluate the image quality.
And S107, selectively transmitting the image according to the priority of the image quality.
The contrast and the distribution of the collected images are utilized to comprehensively evaluate the image quality, the collected images are subjected to priority sequencing according to the image quality evaluation value, and the images are selectively transmitted according to the transmittable data volume of the bandwidth.
The beneficial effect of this embodiment lies in:
the characteristics of the image gray level image are integrated, the gray level histogram is analyzed to obtain the contrast and the distribution degree of the collected image, the image quality is evaluated by utilizing the contrast and the distribution degree of the collected image, and the image quality is judged, so that the collected image is selectively transmitted, the adverse effect of image transmission with poor quality on the image transmission is reduced, and the transmission efficiency is improved.
Example 2
The embodiment of the invention provides an internet of things transmission method based on image quality evaluation, as shown in fig. 1 and 3, comprising the following steps:
s201, carrying out graying processing on the acquired collected image to obtain a grayscale image of the collected image.
And carrying out graying processing on each image acquired by the camera acquisition end at the same time to obtain a grayscale image. The image expressed by gray scale is called gray scale image, and the gray scale image divides white and black into a plurality of grades according to logarithmic relation, which is called gray scale; the gray scale is divided into 256 steps.
S202, acquiring a gray level image of the acquired image, and drawing a gray level histogram of the image according to the gray level image of the acquired image.
The image is represented by luminance, which is divided into 256 values from 0 to 255, and the higher the value is, the higher the luminance is represented. Where 0 represents the darkest area in solid black, 255 represents the brightest area in solid white, and the middle number is the gray of different brightness.
The horizontal axis represents luminance values of 0 to 255 and the vertical axis represents the number of pixels corresponding to luminance in the image, and this functional image is called a histogram, as shown in fig. 4. The height of the column in the histogram represents the number of pixels of the brightness in the image, and the distribution and proportion of the brightness in the image can be obtained.
Establishing a coordinate axis, wherein the horizontal axis represents the gray level of an image
Figure 934250DEST_PATH_IMAGE024
Expressed by integers from 0 to 255, the vertical axis representing the corresponding grey level
Figure 242871DEST_PATH_IMAGE024
Frequency of
Figure 317006DEST_PATH_IMAGE025
And drawing a function image to obtain a corresponding gray level histogram.
Thus, the gray level histograms of all the images acquired by the camera acquisition end at the same moment are obtained.
And S203, dividing the gray level histogram into different region intervals according to the gray level, and calculating the frequency of each gray level and the frequency of each region interval in the gray level histogram.
1. Dividing each gray level histogram into different region intervals:
dividing the gray histogram into different colors, and dividing the gray level space by the divided regions
Figure DEST_PATH_IMAGE063
Sequentially comprises the following steps: pure black color
Figure 671764DEST_PATH_IMAGE064
Black region of the color
Figure DEST_PATH_IMAGE065
Shadow interval
Figure 806160DEST_PATH_IMAGE066
Intermediate interval of tone
Figure DEST_PATH_IMAGE067
High light interval
Figure 562763DEST_PATH_IMAGE068
And white interval
Figure DEST_PATH_IMAGE069
Pure white interval
Figure 276641DEST_PATH_IMAGE070
As shown in fig. 5; the corresponding images are seven areas including a pure black area, a shadow area, a normal brightness area, a highlight area, a white area and a pure white area.
2. Calculating the frequency of each gray level in the gray level histogram:
acquiring the frequency number of each gray level in the gray level histogram, and calculating the frequency of each gray level according to the frequency number of each gray level, wherein the frequency of each gray level is as follows:
Figure 915433DEST_PATH_IMAGE072
in the formula:
Figure 779484DEST_PATH_IMAGE024
in order to be a gray scale level,
Figure 187332DEST_PATH_IMAGE050
is a gray scale
Figure 744215DEST_PATH_IMAGE024
The frequency of (a) of (b) is,
Figure 604724DEST_PATH_IMAGE025
is a gray scale
Figure 6886DEST_PATH_IMAGE024
The frequency of (a) to (b) is,
Figure 269240DEST_PATH_IMAGE051
the resolution of the grayscale image.
3. And calculating the frequency corresponding to each region interval by using the frequency of each gray level:
the gray scale interval corresponding to each region interval is
Figure DEST_PATH_IMAGE073
Then, the frequency of each region interval is:
Figure 590500DEST_PATH_IMAGE074
in the formula:
Figure 548092DEST_PATH_IMAGE055
is the frequency of the region interval of the region,
Figure 347420DEST_PATH_IMAGE050
is a gray scale
Figure 464281DEST_PATH_IMAGE024
The frequency of (a) of (b) is,
Figure 97388DEST_PATH_IMAGE056
the range of the region.
Thus, the frequency of each gray level and the frequency of each region interval in all the gray level histograms are obtained.
The gray level histogram of a normally exposed image is mainly piled up in the middle part and slowly transits to two sides (the gray level histogram is in normal distribution), and the leftmost side and the rightmost side are not cut off, namely the leftmost side and the rightmost side are not piled up in a large amount; a severely underexposed image with the leftmost side of the gray histogram cut off, i.e., a large number of pixels stacked on the leftmost side, as shown in the left image of fig. 6; for a heavily overexposed image, the rightmost side of the histogram is cut off, i.e., a large number of pixels are piled up on the rightmost side, as shown in the right diagram of fig. 6.
The quality of the image is poor whether the image is over-exposed and under-exposed simultaneously due to incorrect setting of the exposure parameters or due to the ambient light ratio being greater than the camera's tolerance. And two indexes of image contrast and distribution degree are constructed and collected through the distribution characteristics of the gray level histogram to measure the image quality.
And S204, calculating the contrast of each acquired image by using the range of the gray level.
The scale of the brightness difference between the bright area and the dark area in the image indicates the hierarchy of the image, and the wider the brightness distribution, the more hierarchical the image is; expressed on the gray level histogram, a wider histogram reflects wider brightness distribution of the image, whereas a narrower histogram reflects narrow brightness distribution of the image, as shown in the histogram in fig. 6; the image brightness distribution width is noted as contrast.
The contrast of the acquired image is represented by calculating the range of the gray level on the gray level histogram, namely the difference between the maximum value of the gray level and the minimum value of the gray level, so as to measure the image quality.
The acquired image contrast is then:
Figure 932489DEST_PATH_IMAGE002
in the formula:
Figure 942033DEST_PATH_IMAGE003
in order to acquire the contrast of an image,
Figure 913400DEST_PATH_IMAGE004
is the maximum value of the grey levels in the grey histogram,
Figure 982987DEST_PATH_IMAGE005
is the minimum value of the gray levels in the gray histogram.
Thus, the contrast of each acquired image is obtained, and whether the image has an underexposure phenomenon or an overexposure phenomenon is judged according to the obtained contrast.
The contrast of the acquired image is calculated by utilizing the range of the gray level of the image, the calculation method is simple, the calculation amount is small, and the camera acquisition end can more effectively obtain a contrast result so as to evaluate the image quality.
And S205, calculating the cutting rate of the gray level histogram according to the frequency of the gray level and the prediction frequency.
When the image has the darkest area or the brightest area, namely, the image has an underexposure or overexposure phenomenon, the image is shown to have cutting-off on the leftmost side or the rightmost side on the gray level histogram of the image, namely, the curve is increased sharply, the data of the gray level histogram is analyzed, and the cutting-off rate of the gray level histogram is obtained.
Gray level histogram upswingLine explosion, using pure white areas (grey levels)
Figure DEST_PATH_IMAGE075
) And a pure black area (gray scale)
Figure 774226DEST_PATH_IMAGE076
) Frequency of
Figure 712095DEST_PATH_IMAGE025
And prediction frequency
Figure DEST_PATH_IMAGE077
Is expressed by the ratio of (A) to (B).
1. And calculating the prediction frequency of the gray levels of the darkest area and the brightest area.
The prediction frequency is obtained by the distribution characteristics of the gray level frequency of the white area and the black area, and the specific calculation process is as follows:
Figure 272389DEST_PATH_IMAGE078
Figure DEST_PATH_IMAGE079
in the formula:
Figure 840773DEST_PATH_IMAGE022
for the frequency count with a gray level of 1,
Figure 525833DEST_PATH_IMAGE023
is the frequency count for a gray scale level of 254,
Figure 205076DEST_PATH_IMAGE024
in order to be a gray scale level,
Figure 151035DEST_PATH_IMAGE025
is a gray scale
Figure 296846DEST_PATH_IMAGE024
The frequency of (a) to (b) is,
Figure 328255DEST_PATH_IMAGE015
the prediction frequency number for a gray level of 0,
Figure 483293DEST_PATH_IMAGE017
the prediction frequency is 255 gray levels.
2. And calculating the cutting rate of the gray level histogram according to the frequency of the gray level and the obtained prediction frequency.
The cut-off ratio is calculated as follows:
Figure 283759DEST_PATH_IMAGE012
in the formula:
Figure 131629DEST_PATH_IMAGE009
a cut-off rate that is a gray histogram;
Figure 650335DEST_PATH_IMAGE013
is a natural constant and is the base number of a natural logarithm;
Figure 343485DEST_PATH_IMAGE014
is the frequency count with a gray level of 0,
Figure 264036DEST_PATH_IMAGE015
the prediction frequency is 0 gray level;
Figure 17229DEST_PATH_IMAGE016
for a frequency count with a gray level of 255,
Figure 757652DEST_PATH_IMAGE017
the prediction frequency is 255 gray levels.
Therefore, the cutoff rate of the gray level histogram is obtained, and whether the image is under-exposed or over-exposed can be more accurately determined according to the cutoff rate of the gray level histogram.
And S206, after the gray level histogram is subjected to smoothing processing, interval division is carried out according to the trough of the smoothed gray level histogram, and the approximate normal distribution rate of the gray level histogram is calculated according to the skewness and the kurtosis of each interval.
The gray level histogram of the ideal image is normally distributed, but in practice, a plurality of peaks and troughs exist in the curve of the gray level histogram, the gray level is divided into a plurality of intervals through the peaks and the troughs, and whether each interval is approximately normal or not is judged, so that the approximate normal distribution rate of the histogram is obtained.
1. And dividing the gray level into a plurality of intervals according to the positions of the valleys of the gray level histogram.
Smoothing the grey level histogram
And calculating the average frequency of each gray level, namely the average value of the frequency of the gray level and the gray levels on the left and right sides of the gray level, and smoothing the gray level histogram by using the method. The calculation formula is as follows:
Figure DEST_PATH_IMAGE081
in the formula:
Figure 847967DEST_PATH_IMAGE082
is a gray scale
Figure 498392DEST_PATH_IMAGE024
The average frequency of (d).
Secondly, interval division is carried out on the gray level histogram according to the gray level corresponding to the position of the wave trough
For the smoothed gray level histogram, acquiring the gray level corresponding to each wave trough
Figure 547119DEST_PATH_IMAGE029
The valley gray levels are aligned according to the order of the gray levels corresponding to the valleys
Figure 915783DEST_PATH_IMAGE029
Sequencing to obtain a sequence
Figure DEST_PATH_IMAGE083
。
Dividing a gray level histogram into a plurality of gray level histograms according to a sequence
Figure 872107DEST_PATH_IMAGE040
Each interval and each interval set is
Figure 377038DEST_PATH_IMAGE084
。
At this point, the interval division of the gray level histogram is completed, and further each interval is analyzed.
2. And calculating skewness and kurtosis of each interval in the set.
Calculating the skewness of each interval
Skewness of histogram (
Figure 596666DEST_PATH_IMAGE028
) To measure the asymmetry of the distribution. The value range is (- ∞, + ∞) when skewness is higher than
Figure DEST_PATH_IMAGE085
When the average value is larger than the preset value, the data are distributed on two sides of the average value relatively uniformly; degree of deflection
Figure 780523DEST_PATH_IMAGE086
When the probability distribution diagram is biased to the left; degree of deflection
Figure DEST_PATH_IMAGE087
The probability distribution map is right biased.
For skewness of the set interval, the calculation formula is as follows:
Figure 478221DEST_PATH_IMAGE027
in the formula:
Figure 103237DEST_PATH_IMAGE028
is the skewness of the gray-level histogram,
Figure 493767DEST_PATH_IMAGE029
the gray levels corresponding to the valleys of the image,
Figure 571445DEST_PATH_IMAGE024
in order to be a gray scale level,
Figure 72833DEST_PATH_IMAGE030
is a gray scale
Figure 552356DEST_PATH_IMAGE031
The frequency of (a) to (b) is,
Figure 125506DEST_PATH_IMAGE032
as valley gray level interval
Figure 18376DEST_PATH_IMAGE033
The average of the frequency counts of the gray levels in (a),
Figure 198821DEST_PATH_IMAGE034
as valley gray level interval
Figure 923064DEST_PATH_IMAGE033
The standard deviation of the frequency of each gray level.
Calculating kurtosis of each interval in the set
Kurtosis of histogram (
Figure 530763DEST_PATH_IMAGE037
) To measure how steep the distribution is. Kurtosis of data with a value range of [1, + ∞) that is completely fit to a normal distribution
Figure 442087DEST_PATH_IMAGE088
The larger the kurtosis value is, the higher the profile is, and the smaller the kurtosis value is, the shorter the profile is.
For the kurtosis of the aggregation interval, the calculation formula is as follows:
Figure DEST_PATH_IMAGE089
in the formula:
Figure 285278DEST_PATH_IMAGE037
is the kurtosis of the grey level histogram,
Figure 739393DEST_PATH_IMAGE029
the gray levels corresponding to the valleys of the image,
Figure 642627DEST_PATH_IMAGE024
in order to be a gray scale level,
Figure 916613DEST_PATH_IMAGE030
is a gray scale
Figure 563495DEST_PATH_IMAGE031
The frequency of (a) to (b) is,
Figure 872117DEST_PATH_IMAGE032
as valley gray level interval
Figure 946252DEST_PATH_IMAGE033
The average value of the frequency counts of the respective gray levels,
Figure 973114DEST_PATH_IMAGE034
is a trough gray scale interval
Figure 361370DEST_PATH_IMAGE033
The standard deviation of the frequency of each gray level.
3. Calculating the approximate normal distribution rate of the gray level histogram by using the skewness and kurtosis of each interval in the set
And judging whether the image is approximately normally distributed or not on the whole according to the situation of the approximately normally distributed state of each section.
The approximate normal distribution ratio calculation formula of the gray level histogram is as follows:
Figure 914711DEST_PATH_IMAGE039
in the formula:
Figure 35114DEST_PATH_IMAGE010
is an approximately normal distribution ratio of the image,
Figure 408326DEST_PATH_IMAGE040
is the base of the set of intervals,
Figure 272377DEST_PATH_IMAGE013
is the base of the natural logarithm and is a natural constant.
Thus, the approximate normal distribution ratio of each gray level histogram is obtained, and the distribution situation of the gray level of the histogram can be further determined.
And S207, calculating the brightness difference rate of the gray level histogram according to the frequency of the gray level and the frequency of the white and black area intervals.
When a large dark area exists and overexposure exists or a large number of bright areas exist and overexposure and underexposure occur simultaneously on an image, the gray histogram shows that the white or black area is large and left or right cut occurs, as shown in fig. 7.
Determining the image exposure condition through the brightness difference of the image, and further judging the image quality, wherein the brightness difference rate is calculated according to the following formula:
Figure 680225DEST_PATH_IMAGE090
in the formula:
Figure 971529DEST_PATH_IMAGE011
is the light-to-dark difference ratio of the gray histogram,
Figure 97617DEST_PATH_IMAGE043
is the frequency at which the gray level is 0,
Figure 765358DEST_PATH_IMAGE044
is a frequency with a gray scale level of 255,
Figure 27713DEST_PATH_IMAGE045
is a region
Figure DEST_PATH_IMAGE091
The frequency of the interval of the region(s),
Figure 83393DEST_PATH_IMAGE047
is a region
Figure 306564DEST_PATH_IMAGE092
The zone interval frequency of (1).
Thus, the brightness difference rate of all the gray level histograms is obtained.
And S208, comprehensively calculating the distribution degree of each acquired image by utilizing the cutoff rate, the approximate normal distribution rate and the brightness difference rate of the gray level histogram.
The cutoff rate, the approximate normal distribution rate and the brightness difference rate of the gray level histogram represent the gray level distribution condition and the image exposure condition of the image, and the quality of the image can be evaluated better through integration.
Comprehensively calculating the cutoff rate, the approximate normal distribution rate and the brightness difference rate of the gray level histogram to obtain the distribution degree of the acquired image, wherein the calculation formula of the distribution degree is as follows:
Figure 902631DEST_PATH_IMAGE007
in the formula:
Figure 894857DEST_PATH_IMAGE008
in order to acquire the degree of distribution of the image,
Figure 855860DEST_PATH_IMAGE009
is the cut-off rate of the gray-scale histogram,
Figure 690961DEST_PATH_IMAGE010
is an approximately normal distribution ratio of the gray histogram,
Figure 700505DEST_PATH_IMAGE011
is the light-dark difference rate of the grey level histogram.
Therefore, the distribution degree of each acquired image is obtained, the gray level distribution condition of the image can be reflected, and the quality of the image is evaluated according to the gray level distribution condition.
S209, carrying out image quality evaluation by using the obtained contrast and distribution of the acquired images, and selectively transmitting the images acquired by the camera acquisition end at the same time according to the image quality information.
1. And carrying out image quality evaluation by using the obtained contrast and distribution degree of the acquired image.
And comprehensively evaluating the image quality according to the contrast and the distribution degree of each acquired image, wherein the comprehensive evaluation of the two factors can enable the evaluation result to be more objective and more accurate.
The calculation formula of the image quality is as follows:
Figure 671872DEST_PATH_IMAGE094
in the formula:
Figure DEST_PATH_IMAGE095
as an image quality evaluation value, for example,
Figure 334935DEST_PATH_IMAGE003
in order to acquire the contrast of the image,
Figure 267119DEST_PATH_IMAGE008
the distribution degree of the collected image is obtained.
2. The image is selectively transmitted based on the image quality information.
Determining transmission priority of transmitted image
And arranging the image quality evaluation values of all the images acquired at the same moment from large to small according to the image quality information, and carrying out priority ordering on all the images acquired at the same moment according to the image quality.
② selectively transmitting the image according to priority
And calculating the frame number of image transmission to determine the transmitted image. The image transmission frame number calculation formula is as follows:
Figure 470567DEST_PATH_IMAGE058
in the formula:
Figure 171807DEST_PATH_IMAGE059
the number of frames for the image transmission,
Figure 536929DEST_PATH_IMAGE051
in order to be able to determine the resolution of the image,
Figure 956409DEST_PATH_IMAGE060
for the size of each of the pixels it is,
Figure 432390DEST_PATH_IMAGE061
is a broadband, and is characterized by comprising a plurality of groups of antennas,
Figure 253715DEST_PATH_IMAGE062
this indicates that the number of frames obtained is rounded down.
According to the priority sequence of image transmission, all images collected at the same time are preceded by
Figure 789739DEST_PATH_IMAGE062
And transmitting the frame image to finish the image transmission process.
For example: when the data volume which can be transmitted by the bandwidth is 20, the first twenty images in the priority sequence are transmitted according to the priority sequence of all the images acquired by the camera acquisition end at the same moment.
Therefore, the internet of things transmission method based on image quality evaluation is completed.
In the embodiment, the images are selected according to the priority of image transmission, and the transmittable quantity supported by the broadband is calculated to transmit the images, so that the problem that the images cannot normally run in the broadband transmission process is avoided; the characteristics of the gray level histogram are utilized to evaluate and calculate the image quality, the parameters are few, the calculation method is simple and convenient, and the problem that high-complexity calculation cannot be carried out due to the limited calculation capability of a camera acquisition end is solved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (8)

1. An Internet of things transmission method based on image quality assessment is characterized in that: the method comprises the following steps:
carrying out graying processing on images acquired by a plurality of sections of cameras at the same time to obtain gray level images, and calculating by utilizing the gray level images to obtain gray level histograms of all the acquired images;
dividing each gray level histogram into different areas according to the gray level;
calculating the contrast of each acquired image according to the range of the gray level in each gray level histogram;
calculating the cutting rate of each gray histogram according to the frequency of the gray level of each gray histogram and the prediction frequency;
after smoothing processing is carried out on each gray level histogram, interval division is carried out according to the trough of the smoothed gray level histogram, and the approximate normal distribution rate of each gray level histogram is calculated according to the skewness and the kurtosis of each interval;
calculating the brightness difference rate of each gray level histogram by using the frequency of the gray level of pure white and pure black in each gray level histogram and the frequency of the white area and the black area;
acquiring the distribution degree of the acquired image corresponding to each gray level histogram by utilizing the cutoff rate, the approximate normal distribution rate and the brightness difference rate of each gray level histogram;
evaluating the quality of all the acquired images by using the obtained contrast and distribution of each acquired image;
and carrying out priority sequencing on the collected images by utilizing the image quality evaluation information, and transmitting the collected images according to the priority sequence, wherein the number of the transmitted images is the data amount which can be transmitted by the bandwidth.
2. The transmission method of the internet of things based on the image quality assessment as claimed in claim 1, wherein: the evaluation parameters of the image quality are the contrast and the distribution degree of each acquired image, and the image quality evaluation value is the sum of the contrast and the distribution degree of each acquired image.
3. The transmission method of the internet of things based on the image quality assessment as claimed in claim 2, wherein: the calculation formula of the acquired image contrast is as follows:
Figure DEST_PATH_IMAGE002
in the formula:
Figure DEST_PATH_IMAGE004
in order to acquire the contrast of the image,
Figure DEST_PATH_IMAGE006
is the maximum value of the grey levels in the grey histogram,
Figure DEST_PATH_IMAGE008
is the minimum value of the gray levels in the gray histogram.
4. The transmission method of the internet of things based on the image quality assessment as claimed in claim 2, wherein: the distribution degree of the collected images is comprehensively calculated according to the cutoff rate, the approximate normal distribution rate and the brightness difference rate of the gray level histogram;
the calculation formula is as follows:
Figure DEST_PATH_IMAGE010
in the formula:
Figure DEST_PATH_IMAGE012
in order to acquire the degree of distribution of the image,
Figure DEST_PATH_IMAGE014
is the cut-off rate of the gray-scale histogram,
Figure DEST_PATH_IMAGE016
is an approximately normal distribution ratio of the gray level histogram,
Figure DEST_PATH_IMAGE018
is the light-dark difference rate of the grey level histogram.
5. The transmission method of the internet of things based on the image quality assessment as claimed in claim 4, wherein: the method for calculating the cutoff rate of the gray histogram is as follows:
Figure DEST_PATH_IMAGE020
in the formula:
Figure 751599DEST_PATH_IMAGE014
cut-off rate as a histogram of gray levels;
Figure DEST_PATH_IMAGE022
is a natural constant and is the base number of a natural logarithm;
Figure DEST_PATH_IMAGE024
is the frequency count with a gray level of 0,
Figure DEST_PATH_IMAGE026
the prediction frequency is 0 gray level;
Figure DEST_PATH_IMAGE028
for a frequency count with a gray level of 255,
Figure DEST_PATH_IMAGE030
the prediction frequency is 255 gray levels;
wherein:
Figure DEST_PATH_IMAGE032
Figure DEST_PATH_IMAGE034
in the formula:
Figure DEST_PATH_IMAGE036
is the frequency count with a gray level of 1,
Figure DEST_PATH_IMAGE038
for a frequency count with a gray scale level of 254,
Figure DEST_PATH_IMAGE040
in order to be a gray scale level,
Figure DEST_PATH_IMAGE042
is a gray scale
Figure 790443DEST_PATH_IMAGE040
The frequency of (c).
6. The transmission method of the internet of things based on the image quality assessment as claimed in claim 4, wherein: the method for calculating the approximate normal distribution rate of the gray level histogram is as follows:
the method comprises the steps of smoothing the gray level histogram to obtain a smoothed gray level histogram by calculating the average frequency of each gray level and the gray levels on the left side and the right side of the gray level histogram, obtaining the gray levels corresponding to wave troughs of the smoothed gray level histogram, arranging the gray levels corresponding to the wave troughs in sequence, and dividing wave trough gray level intervals to obtain an interval set;
and (3) calculating the skewness and kurtosis of the gray level histogram for each divided interval:
the skewness calculation formula is as follows:
Figure DEST_PATH_IMAGE044
in the formula:
Figure DEST_PATH_IMAGE046
is the skewness of the gray-scale histogram,
Figure DEST_PATH_IMAGE048
the gray levels corresponding to the valleys of the image,
Figure 881765DEST_PATH_IMAGE040
in order to be a gray scale level,
Figure DEST_PATH_IMAGE050
is a gray scale
Figure DEST_PATH_IMAGE052
The frequency of (a) is lower than (b),
Figure DEST_PATH_IMAGE054
as valley gray level interval
Figure DEST_PATH_IMAGE056
The average of the frequency counts of the gray levels in (a),
Figure DEST_PATH_IMAGE058
as valley gray level interval
Figure 401608DEST_PATH_IMAGE056
Standard deviation of frequency of each gray level;
the kurtosis is calculated as follows:
Figure DEST_PATH_IMAGE060
in the formula:
Figure DEST_PATH_IMAGE062
kurtosis of the gray level histogram;
and calculating the approximate normal distribution rate of the image according to the skewness and kurtosis of the calculated gray level histogram, wherein the calculation formula is as follows:
Figure DEST_PATH_IMAGE064
in the formula:
Figure 307990DEST_PATH_IMAGE016
is an approximately normal distribution ratio of the image,
Figure DEST_PATH_IMAGE066
is the base of the set of intervals,
Figure 5819DEST_PATH_IMAGE022
is the base of the natural logarithm and is a natural constant.
7. The transmission method of the internet of things based on the image quality assessment as claimed in claim 4, wherein: the calculation formula of the brightness difference rate of the gray level histogram is as follows:
Figure DEST_PATH_IMAGE068
in the formula:
Figure 459803DEST_PATH_IMAGE018
is the light-to-dark difference ratio of the gray histogram,
Figure DEST_PATH_IMAGE070
at a gray level of 0 i.e. the frequency of a pure black pixel,
Figure DEST_PATH_IMAGE072
at a grey level of 255 i.e. the frequency of a pure white pixel,
Figure DEST_PATH_IMAGE074
is a white area
Figure DEST_PATH_IMAGE076
The frequency of the interval of the region is,
Figure DEST_PATH_IMAGE078
a region interval frequency that is a black region;
the frequency calculation formula of the gray scale is as follows:
Figure DEST_PATH_IMAGE080
in the formula:
Figure 157238DEST_PATH_IMAGE040
in order to be a gray scale level,
Figure DEST_PATH_IMAGE082
is a gray scale
Figure 200019DEST_PATH_IMAGE040
The frequency of (a) is set to be,
Figure 255699DEST_PATH_IMAGE042
is a gray scale
Figure 823078DEST_PATH_IMAGE040
The frequency of (a) to (b) is,
Figure DEST_PATH_IMAGE084
in order to be the resolution of the gray-scale image,
Figure DEST_PATH_IMAGE086
is the number of gray levels;
the region interval frequency calculation formula is as follows:
Figure DEST_PATH_IMAGE088
in the formula:
Figure DEST_PATH_IMAGE090
is the frequency of the region interval of the region,
Figure DEST_PATH_IMAGE092
is the range of the region.
8. The transmission method of the internet of things based on the image quality assessment as claimed in claim 1, wherein: the method for acquiring the data volume capable of being transmitted by the bandwidth comprises the following steps:
Figure DEST_PATH_IMAGE094
in the formula:
Figure DEST_PATH_IMAGE096
the number of frames for the image transmission,
Figure 455964DEST_PATH_IMAGE084
in order to be able to determine the resolution of the image,
Figure DEST_PATH_IMAGE098
for the size of each of the pixels it is,
Figure DEST_PATH_IMAGE100
in order to be a wide band,
Figure DEST_PATH_IMAGE102
indicating rounding down the resulting frame number.
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