CN117082241A - High-speed industrial camera image transmission optimization method - Google Patents
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- CN117082241A CN117082241A CN202311322975.1A CN202311322975A CN117082241A CN 117082241 A CN117082241 A CN 117082241A CN 202311322975 A CN202311322975 A CN 202311322975A CN 117082241 A CN117082241 A CN 117082241A
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- 238000000034 method Methods 0.000 title claims abstract description 49
- 230000005540 biological transmission Effects 0.000 title claims abstract description 48
- 238000005457 optimization Methods 0.000 title claims abstract description 9
- 230000006835 compression Effects 0.000 claims abstract description 70
- 238000007906 compression Methods 0.000 claims abstract description 70
- 238000004891 communication Methods 0.000 claims abstract description 8
- 238000007781 pre-processing Methods 0.000 claims abstract description 8
- 230000003044 adaptive effect Effects 0.000 claims abstract description 6
- 238000003708 edge detection Methods 0.000 claims description 12
- 238000005516 engineering process Methods 0.000 claims description 3
- 238000011946 reduction process Methods 0.000 claims description 2
- 238000012545 processing Methods 0.000 description 7
- 238000007796 conventional method Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
- H04N19/167—Position within a video image, e.g. region of interest [ROI]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/80—Camera processing pipelines; Components thereof
- H04N23/84—Camera processing pipelines; Components thereof for processing colour signals
- H04N23/88—Camera processing pipelines; Components thereof for processing colour signals for colour balance, e.g. white-balance circuits or colour temperature control
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The invention relates to an image transmission method, in particular to a high-speed industrial camera image transmission optimization method, which comprises the steps of preprocessing an original image at a camera end, and compressing the image by adopting an adaptive compression method to obtain image compression data; encoding the image compression data and then transmitting the encoded image compression data through wireless communication; after receiving the image compression data, the receiving end decodes and restores the original image; the self-adaptive compression method is automatically switched into a lossless or lossy compression method according to the transmission speed or dynamic ROI area in the last time segment.
Description
Technical Field
The invention relates to an image transmission method, in particular to a high-speed industrial camera image transmission optimization method.
Background
Industrial camera image transmission refers to the process of transmitting image data captured by an industrial camera (also referred to as a machine vision camera or an industrial video camera) from an acquisition device to a processing or display device. Such transmissions are commonly used in industrial automation, quality control, production monitoring, and machine vision applications.
In the field of industrial camera image transmission, conventional methods generally employ a fixed compression rate or compression method, which have some drawbacks as follows:
1. fixed compression ratio: conventional methods typically use a fixed compression rate, which means that the compression rate of the image is constant under different transmission conditions. This may lead to waste of bandwidth resources at high transmission speeds, and may lead to degradation of image quality at low transmission speeds.
2. Lack of real-time: conventional methods often fail to accommodate real-time transmission requirements. They do not take into account current transmission speed or image quality requirements and therefore may not provide optimal performance in different scenarios.
3. Lack of adaptability: the conventional method generally does not have self-adaptive capability, and cannot adjust the compression method according to the characteristics of the image content. This results in that an optimal image transmission effect is not achieved under different image types.
Disclosure of Invention
The invention mainly aims to provide a high-speed industrial camera image transmission optimization method for solving the problems in the related art.
In order to achieve the above object, according to one aspect of the present invention, there is provided a high-speed industrial camera image transmission optimizing method including:
preprocessing an original image at a camera end, and compressing the image by adopting an adaptive compression method to obtain image compression data;
encoding the image compression data and then transmitting the encoded image compression data through wireless communication;
after receiving the image compression data, the receiving end decodes and restores the original image;
the self-adaptive compression method is automatically switched into a lossless or lossy compression method according to the transmission speed or dynamic ROI area in the last time segment.
Further, the method for automatically switching to lossless or lossy compression according to the transmission speed in the previous time slice specifically comprises the following steps:
calculating the compression ratio:wherein (1)>For compression rate->For compression delay, ++>Is the current transmission speed;
computing real timeSex index:wherein->Is a real-time index, is->Is a real-time threshold;
calculating an image quality index:wherein->For image quality index, < >>Is the image quality weight;
if it isAnd->Selecting lossless compression;
otherwise, lossy compression is selected.
Further, the method for automatically switching to lossless or lossy compression according to the dynamic ROI area comprises the following steps:
detecting edges in the original image using an edge detection technique;
creating a dynamic ROI according to the edge detection result;
the region within the dynamic ROI selects lossless compression and the region outside the dynamic ROI selects lossy compression.
Further, the detecting the edge in the original image by using the edge detection technology specifically comprises:
calculating the edge intensity of each pixel point in the original imageAnd edge direction->：
Wherein,and->Pixels are respectively->Operators in the x and y directions.
Further, the creating a dynamic ROI according to the edge detection result specifically includes:
calculating direction weighted edge intensities
Wherein,representing the emphasized edge direction;
weighting direction to edge strengthComparing with a preset intensity threshold, ifIs larger than the preset intensity threshold, which indicates +.>There is a special direction +.>Is considered as pixel point +.>Is part of a dynamic ROI.
Further, the encoding the image compression data specifically includes:
s1: creating an empty priority queue Q and adding each data itemAnd its frequency of occurrence->Added as a node to Q;
s2: taking out two nodes with the minimum occurrence frequency from Q, adding the frequencies of the two nodes to obtain a new node, adding the new node into Q, and setting the frequency of the new node as the sum of the frequencies of the two original nodes;
s3: repeating S2 until only one node is left in Q, and setting the node as a coding tree;
s4: obtaining each data item according to the coding treeIs encoded by (a). For each data item->Starting from the root node, walk left to represent 0, walk right to represent 1, until the leaf node is reached, and the path along the way is made +.>Is encoded by (a).
Further, the data itemThe code length of (2) is: />。
Further, the transmission through wireless communication employs multi-threading or multi-channel transmission.
Further, the preprocessing includes white balance and noise reduction processing.
Further, the noise reduction process specifically includes:
wherein,representing the position +.>The pixel points at the positions are noise-reduced,representing the position +.>The value of the pixel of the location,/>Representing the size of the neighborhood region,representing the offset in the row direction of the pixel in the neighborhood relative to the central pixel, +.>Representing the offset in column direction of the pixel in the neighborhood relative to the center pixel,/for each pixel in the neighborhood>。
Compared with the prior art, the invention has the following beneficial effects:
1. real-time optimization: the method ensures the best image transmission instantaneity under different transmission speeds by adaptively selecting a lossless or lossy compression method according to the transmission speed or the dynamic ROI area in the last time slice. This helps meet the need for real-time in industrial applications, such as in line monitoring and machine vision applications.
2. Optimizing the image quality: the method considers the image quality weight and the compression rate to reduce the transmission quantity of the image data to the maximum extent without damaging the image quality. This helps to preserve image quality, avoid information loss, and provide a clearer image.
3. Adaptivity: by using the transmission speed and the dynamic ROI area as adaptive judgment criteria, the method can select the most suitable compression method according to different image contents and transmission conditions. This improves the adaptability and versatility of the system.
4. Noise reduction and white balance processing: and white balance and noise reduction processing are carried out in the preprocessing stage, so that the quality and the visual effect of the image are improved. The white balance processing helps to eliminate color shift, and the noise reduction processing helps to remove noise under low light conditions or under high ISO settings, thereby further improving image quality.
5. Wireless communication optimization: the multi-thread or multi-channel transmission mode is adopted, so that the transmission rate can be effectively improved, the image can be ensured to be transmitted to the receiving end in time, and the requirement of high-speed industrial application is met.
Detailed Description
In order to further describe the technical means and effects adopted for achieving the intended purpose of the present invention, the following detailed description will refer to the specific implementation, structure, characteristics and effects according to the present invention in conjunction with the preferred embodiments.
A high-speed industrial camera image transmission optimization method comprises the following steps:
preprocessing an original image at a camera end, and compressing the image by adopting an adaptive compression method to obtain image compression data;
encoding the image compression data and then transmitting the encoded image compression data through wireless communication;
after receiving the image compression data, the receiving end decodes and restores the original image;
the self-adaptive compression method is automatically switched into a lossless or lossy compression method according to the transmission speed or dynamic ROI area in the last time segment.
Specifically, the adaptive compression method includes two forms, in this embodiment, a real-time compression method is determined by using a transmission speed in a previous time slice, and the method for automatically switching to lossless or lossy compression according to the transmission speed in the previous time slice is specifically as follows:
calculating the compression ratio:wherein (1)>For compression rate->For compression delay, ++>Is the current transmission speed;
calculating a real-time index:wherein->Is a real-time index, is->Is a real-time threshold;
calculating an image quality index:wherein->For image quality index, < >>Is the image quality weight;
if it isAnd->Selecting lossless compression;
otherwise, lossy compression is selected.
In another preferred embodiment, the dynamic ROI area is used to determine a real-time compression method, and the automatic switching to a lossless or lossy compression method according to the dynamic ROI area includes:
detecting edges in the original image using an edge detection technique;
creating a dynamic ROI according to the edge detection result;
the region within the dynamic ROI selects lossless compression and the region outside the dynamic ROI selects lossy compression.
Further, the detecting the edge in the original image by using the edge detection technology specifically comprises:
calculating the edge intensity of each pixel point in the original imageAnd edge direction->：
Wherein,and->Pixels are respectively->Operators in the x and y directions.
Further, the creating a dynamic ROI according to the edge detection result specifically includes:
calculating direction weighted edge intensities
Wherein,representing the emphasized edge direction;
weighting direction to edge strengthComparing with a preset intensity threshold, ifIs larger than the preset intensity threshold, which indicates +.>There is a special direction +.>Is considered as pixel point +.>Is part of a dynamic ROI.
Further, the encoding the image compression data specifically includes:
s1: creating an empty priority queue Q and adding each data itemAnd its frequency of occurrence->Added as a node to Q;
s2: taking out two nodes with the minimum occurrence frequency from Q, adding the frequencies of the two nodes to obtain a new node, adding the new node into Q, and setting the frequency of the new node as the sum of the frequencies of the two original nodes;
s3: repeating S2 until only one node is left in Q, and setting the node as a coding tree;
s4: obtaining each data item according to the coding treeIs encoded by (a). For each data item->Starting from the root node, walk left to represent 0, walk right to represent 1, until the leaf node is reached, and the path along the way is made +.>Is encoded by (a).
Wherein the data itemThe code length of (2) is: />。
Further, the transmission through wireless communication employs multi-threading or multi-channel transmission. The transmission rate can be effectively improved.
In this embodiment, the preprocessing includes white balance and noise reduction processing. White balance makes the image more natural by adjusting the temperature of the colors in the image to eliminate color shift.
An image taken under low light conditions or under high ISO settings may contain noise, specifically:
wherein,representing the position +.>The pixel points at the positions are noise-reduced,representing the position +.>The value of the pixel of the location,/>Representing the size of the neighborhood region,representing the offset in the row direction of the pixel in the neighborhood relative to the central pixel, +.>Representing the offset in column direction of the pixel in the neighborhood relative to the center pixel,/for each pixel in the neighborhood>。
Examples are as follows:
in this embodiment, there is a 5×5 image, and the pixel values are as follows:
12 18 24 30 36
18 24 30 36 42
24 30 36 42 48
30 36 42 48 54
36 42 48 54 60
a 3x3 neighborhood is selected for mean filtering. For the upper left pixel (value 12), the pixels in its neighborhood are:
12 18 24
18 24 30
24 30 36
the average value of all pixels in the neighborhood is:
(12 + 18 + 24 + 18 + 24 + 30 + 24 + 30 + 36) / 9 = 26.67
thus, the pixel value in the upper left corner is replaced with 26.67. In the same way, each pixel in the image is mean filtered. The final image was as follows:
26.67 26.67 26.67 30.00 30.00
26.67 26.67 26.67 30.00 30.00
26.67 26.67 26.67 30.00 30.00
30.00 30.00 30.00 33.33 33.33
30.00 30.00 30.00 33.33 33.33。
the present invention is not limited to the above embodiments, but is capable of modification and variation in detail, and other modifications and variations can be made by those skilled in the art without departing from the scope of the present invention.
Claims (8)
1. A high-speed industrial camera image transmission optimization method, comprising:
preprocessing an original image at a camera end, and compressing the image by adopting an adaptive compression method to obtain image compression data;
encoding the image compression data and then transmitting the encoded image compression data through wireless communication;
after receiving the image compression data, the receiving end decodes and restores the original image;
the self-adaptive compression method is automatically switched into a lossless or lossy compression method according to the transmission speed or dynamic ROI area in the last time segment;
the method for automatically switching to lossless or lossy compression according to the transmission speed in the last time segment comprises the following steps:
calculating the compression ratio:wherein->For compression rate->For compression delay, ++>Is the current transmission speed;
calculating a real-time index:wherein->Is a real-time index, is->Is a real-time threshold;
calculating an image quality index:wherein->For image quality index, < >>Is the image quality weight;
if it isAnd->Selecting lossless compression;
otherwise, selecting lossy compression; the method for automatically switching to lossless or lossy compression according to the dynamic ROI area comprises the following steps:
detecting edges in the original image using an edge detection technique;
creating a dynamic ROI according to the edge detection result;
the region within the dynamic ROI selects lossless compression and the region outside the dynamic ROI selects lossy compression.
2. The method for optimizing image transmission of high-speed industrial camera according to claim 1, wherein the detecting edges in the original image by using the edge detection technology specifically comprises:
calculating the edge intensity of each pixel point in the original imageAnd edge direction->：
Wherein (1)>And->Pixels are respectively->Operators in the x and y directions.
3. The method for optimizing image transmission of high-speed industrial camera according to claim 2, wherein the creating a dynamic ROI based on the edge detection result is specifically:
calculating direction weighted edge intensities Wherein (1)>Representing the emphasized edge direction;
weighting direction to edge strengthComparing with a preset intensity threshold, ifIf the intensity is larger than the preset intensity threshold value, the pixel point is considered to be +.>Is part of a dynamic ROI.
4. The method for optimizing image transmission of a high-speed industrial camera according to claim 1, wherein the encoding of the image compression data specifically comprises:
s1: creating an empty priority queue Q and adding each data itemAnd its frequency of occurrence->Added as a node to Q;
s2: taking out two nodes with the minimum occurrence frequency from Q, adding the frequencies of the two nodes to obtain a new node, adding the new node into Q, and setting the frequency of the new node as the sum of the frequencies of the two original nodes;
s3: repeating S2 until only one node is left in Q, and setting the node as a coding tree;
s4: obtaining each data item according to the coding treeFor each data item +.>Starting from the root node, walk left to represent 0, walk right to represent 1, until the leaf node is reached, and the path along the way is made +.>Is encoded by (a).
5. The method of claim 4, wherein the data items areThe code length of (2) is: />。
6. The method of claim 1, wherein the transmitting via wireless communication uses multi-threading or multi-channel transmission.
7. The method of claim 1, wherein the preprocessing includes white balancing and noise reduction.
8. The method for optimizing image transmission of a high-speed industrial camera according to claim 7, wherein the noise reduction process is specifically:
wherein (1)>Representing the position +.>Pixel points of the position after noise reduction are added with +.>Representing the position +.>The value of the pixel of the location,/>Representing the size of the neighborhood, +.>Representing the offset in the row direction of the pixel in the neighborhood relative to the central pixel, +.>Representing the offset in column direction of the pixels in the neighborhood relative to the center pixel,。
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| US20080193028A1 (en) * | 2007-02-13 | 2008-08-14 | Yin-Chun Blue Lan | Method of high quality digital image compression |
| CN101908891A (en) * | 2010-08-23 | 2010-12-08 | 南京信息工程大学 | Medical image ROI (Region of Interest) compression method based on lifting wavelet and PCNN (Pulse Coupled Neural Network) |
| CN104270638A (en) * | 2014-07-29 | 2015-01-07 | 武汉飞脉科技有限责任公司 | Compression and quality evaluation method for region of interest (ROI) of CT (Computed Tomography) image |
| CN109982085A (en) * | 2017-12-28 | 2019-07-05 | 新岸线(北京)科技集团有限公司 | A kind of method of high precision image mixing compression |
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- 2023-10-13 CN CN202311322975.1A patent/CN117082241A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080193028A1 (en) * | 2007-02-13 | 2008-08-14 | Yin-Chun Blue Lan | Method of high quality digital image compression |
| CN101908891A (en) * | 2010-08-23 | 2010-12-08 | 南京信息工程大学 | Medical image ROI (Region of Interest) compression method based on lifting wavelet and PCNN (Pulse Coupled Neural Network) |
| CN104270638A (en) * | 2014-07-29 | 2015-01-07 | 武汉飞脉科技有限责任公司 | Compression and quality evaluation method for region of interest (ROI) of CT (Computed Tomography) image |
| CN109982085A (en) * | 2017-12-28 | 2019-07-05 | 新岸线(北京)科技集团有限公司 | A kind of method of high precision image mixing compression |
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