CN110557638A - communication network switching method and system for inspection unmanned aerial vehicle - Google Patents
communication network switching method and system for inspection unmanned aerial vehicle Download PDFInfo
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- H04W36/30—Reselection being triggered by specific parameters by measured or perceived connection quality data
Abstract
the application provides a method and a system for switching a communication network of an inspection unmanned aerial vehicle, wherein the method comprises the following steps: acquiring the real-time signal intensity of each wireless communication network received by the inspection unmanned aerial vehicle; determining a target wireless communication network with the maximum real-time signal intensity from the real-time signal intensities; and matching a preset image compression algorithm and a preset communication module corresponding to the target wireless communication network according to the communication protocol type of the target wireless communication network, and compressing and transmitting image data acquired by the inspection unmanned aerial vehicle through the preset image compression algorithm and the preset communication module. The unmanned aerial vehicle that makes to patrol and examine when carrying out the task of patrolling and examining, even the position to and signal strength constantly changes also can keep the optimal communication quality, solved current unmanned aerial vehicle that patrols and examines the efficiency and the quality of passback image when position change signal strength changes and easily influenced technical problem.
Description
Technical Field
The application relates to the field of wireless communication, in particular to a method and a system for switching communication networks of inspection unmanned aerial vehicles.
background
Along with the continuous expansion of the application range of the unmanned aerial vehicle, the unmanned aerial vehicle is more and more widely applied to the fields of emergency rescue and disaster relief, forest fire prevention, fire control command, electric power inspection and the like, and the picture transmitted at a high altitude in a long distance is clear, bright in color and smooth in image, so that commanders can obtain the truest field information at the first time.
However, the existing inspection unmanned aerial vehicle carries out image return based on a single transmission protocol, when the signal intensity of the position where the inspection unmanned aerial vehicle is located is weak, the efficiency and the quality of the image returned by the unmanned aerial vehicle are greatly reduced, and therefore how to reduce the influence of the position change on the efficiency and the quality of the image returned by the inspection unmanned aerial vehicle becomes a technical problem which needs to be solved urgently by technical personnel in the field.
Disclosure of Invention
The application provides a communication network switching method and system for an inspection unmanned aerial vehicle, which are used for solving the technical problem that the efficiency and the quality of a returned image are easily influenced when the position change signal intensity of the conventional inspection unmanned aerial vehicle changes.
In view of this, the first aspect of the present application provides a method for switching a communication network of an inspection unmanned aerial vehicle, including:
Acquiring the real-time signal intensity of each wireless communication network received by the inspection unmanned aerial vehicle;
determining a target wireless communication network with the maximum real-time signal strength from the real-time signal strengths;
And matching a preset image compression algorithm and a preset communication module corresponding to the target wireless communication network according to the communication protocol type of the target wireless communication network, and compressing and transmitting image data acquired by the inspection unmanned aerial vehicle through the preset image compression algorithm and the preset communication module.
optionally, the wireless communication network comprises: a 4G communication network, a 5G communication network, and a radio station communication network.
optionally, the matching, according to the communication protocol type of the target wireless communication network, a preset image compression algorithm and a preset communication module corresponding to the target wireless communication network, and compressing and transmitting the image data acquired by the inspection unmanned aerial vehicle through the preset image compression algorithm and the preset communication module specifically include:
If the communication protocol type of the target wireless communication network is a wireless radio station communication network, matching a preset mpeg2 image compression algorithm with a preset wireless radio station communication module, compressing image data acquired by the inspection unmanned aerial vehicle through the mpeg2 image compression algorithm, and transmitting the image data through the wireless radio station communication module.
Optionally, the matching, according to the communication protocol type of the target wireless communication network, a preset image compression algorithm and a preset communication module corresponding to the target wireless communication network, and compressing and transmitting the image data acquired by the inspection unmanned aerial vehicle through the preset image compression algorithm and the preset communication module specifically include:
And if the communication protocol type of the target wireless communication network is a 4G communication network, matching a preset JPEG image compression algorithm with a preset 4G communication module, compressing the image data acquired by the inspection unmanned aerial vehicle through the JPEG image compression algorithm, and transmitting the image data through the 4G communication module.
Optionally, the matching, according to the communication protocol type of the target wireless communication network, a preset image compression algorithm and a preset communication module corresponding to the target wireless communication network, and compressing and transmitting the image data acquired by the inspection unmanned aerial vehicle through the preset image compression algorithm and the preset communication module specifically include:
if the communication protocol type of the target wireless communication network is a 5G communication network, matching a preset H.265 image compression algorithm with a preset 5G communication module, compressing image data acquired by the inspection unmanned aerial vehicle through the H.265 image compression algorithm, and transmitting the image data through the 5G communication module.
Optionally, the determining, from the respective real-time signal strengths, a target wireless communication network with the largest real-time signal strength further includes:
if the real-time signal strength is the same, the priority of the wireless communication network is from high to low: a radio station communication network, a 5G communication network, and a 4G communication network.
this application second aspect provides an unmanned aerial vehicle communication network switched systems patrols and examines, includes:
The signal acquisition unit is used for acquiring the real-time signal intensity of each wireless communication network received by the inspection unmanned aerial vehicle;
A target communication network determining unit, configured to determine, from the real-time signal strengths, a target wireless communication network with the largest real-time signal strength;
And the image data processing unit is used for matching a preset image compression algorithm and a preset communication module corresponding to the target wireless communication network according to the communication protocol type of the target wireless communication network, and compressing and transmitting the image data acquired by the inspection unmanned aerial vehicle through the preset image compression algorithm and the preset communication module.
Optionally, the image data processing unit is specifically configured to:
If the communication protocol type of the target wireless communication network is a wireless radio station communication network, matching a preset mpeg2 image compression algorithm with a preset wireless radio station communication module, compressing image data acquired by the inspection unmanned aerial vehicle through the mpeg2 image compression algorithm, and transmitting the image data through the wireless radio station communication module.
Optionally, the image data processing unit is specifically configured to:
And if the communication protocol type of the target wireless communication network is a 4G communication network, matching a preset JPEG image compression algorithm with a preset 4G communication module, compressing the image data acquired by the inspection unmanned aerial vehicle through the JPEG image compression algorithm, and transmitting the image data through the 4G communication module.
Optionally, the image data processing unit is specifically configured to:
If the communication protocol type of the target wireless communication network is a 5G communication network, matching a preset H.265 image compression algorithm with a preset 5G communication module, compressing image data acquired by the inspection unmanned aerial vehicle through the H.265 image compression algorithm, and transmitting the image data through the 5G communication module.
According to the technical scheme, the method has the following advantages:
the application provides a routing inspection unmanned aerial vehicle communication network switching method, which comprises the following steps: acquiring the real-time signal intensity of each wireless communication network received by the inspection unmanned aerial vehicle; determining a target wireless communication network with the maximum real-time signal strength from the real-time signal strengths; and matching a preset image compression algorithm and a preset communication module corresponding to the target wireless communication network according to the communication protocol type of the target wireless communication network, and compressing and transmitting image data acquired by the inspection unmanned aerial vehicle through the preset image compression algorithm and the preset communication module.
This application is through obtaining the real-time signal intensity of patrolling and examining each wireless communication network that unmanned aerial vehicle received, according to the size of real-time signal intensity, the communication network that the selection signal intensity is the biggest, and with this communication network assorted image processing algorithm handle the image data and the transmission data of gathering, make patrolling and examining unmanned aerial vehicle when carrying out the task of patrolling and examining, even the position, and signal intensity constantly changes and also can keep the optimal communication quality, the current efficiency and the quality of patrolling and examining the unmanned aerial vehicle passback image when position change signal intensity changes have been solved and have been influenced technical problem easily.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flow chart of an embodiment of a method for switching a communication network of an inspection unmanned aerial vehicle according to the present application;
fig. 2 is a schematic structural diagram of an embodiment of the routing inspection unmanned aerial vehicle communication network switching device provided in the present application;
Fig. 3 is a block diagram of a motion compensation prediction coding in the method for switching the communication network of the inspection unmanned aerial vehicle according to the present application.
Detailed Description
the embodiment of the application provides a communication network switching method and system for an inspection unmanned aerial vehicle, which are used for solving the technical problem that the efficiency and the quality of a returned image of the existing inspection unmanned aerial vehicle are easily influenced when the position change signal intensity changes.
in order to make the objects, features and advantages of the present invention more apparent and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the embodiments described below are only a part of the embodiments of the present application, 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 application.
Referring to fig. 1, an embodiment of the present application provides a method for switching a communication network of an inspection unmanned aerial vehicle, including:
Step 101, acquiring the real-time signal intensity of each wireless communication network received by the inspection unmanned aerial vehicle;
Step 102, determining a target wireless communication network with the maximum real-time signal intensity from the real-time signal intensities;
and 103, matching a preset image compression algorithm and a preset communication module corresponding to the target wireless communication network according to the communication protocol type of the target wireless communication network, and compressing and transmitting image data acquired by the inspection unmanned aerial vehicle through the preset image compression algorithm and the preset communication module.
more specifically, the wireless communication network comprises: a 4G communication network, a 5G communication network, and a radio station communication network.
more specifically, matching a preset image compression algorithm and a preset communication module corresponding to the target wireless communication network according to the communication protocol type of the target wireless communication network, and compressing and transmitting image data acquired by the inspection unmanned aerial vehicle through the preset image compression algorithm and the preset communication module specifically comprises:
If the communication protocol type of the target wireless communication network is the wireless radio station communication network, matching a preset mpeg2 image compression algorithm with a preset wireless radio station communication module, compressing image data acquired by the inspection unmanned aerial vehicle through the mpeg2 image compression algorithm, and transmitting the image data through the wireless radio station communication module.
It should be noted that the mpeg2 image processing algorithm combines Discrete Cosine Transform (DCT), Huffman coding and Motion Compensation (Motion Compensation). DCT reduces the spatial redundancy of the image, motion compensation reduces the temporal redundancy of the image, whereas Huffman coding reduces the redundancy of the image in terms of information entropy. The combined use of these several techniques results in a high compression ratio for MPEG.
The DCT is performed in units of 8 × 8 blocks in MPEG-2, and generates 8 × 8DCT coefficient data blocks. The DCT transform has the greatest characteristic that for a general image, the energy of the block can be concentrated on a few low-frequency DCT coefficients, that is, in the 8 × 8DCT coefficient block generated, only a few low-frequency coefficients at the upper left corner have larger values, and the values of the remaining coefficients are small, so that only a few coefficients can be encoded and transmitted without seriously affecting the image quality.
the quantization is performed on the DCT transform coefficients, and the quantization process is to remove the DCT coefficients in a certain quantization step size. The size of the quantization step is called quantization precision, and the smaller the quantization step, the finer the quantization precision, the more information is contained, but the higher the required transmission band. The importance of different DCT transform coefficients to human visual perception is different, so the encoder applies different quantization precisions to 64 DCT transform coefficients in an 8x8DCT transform block according to the visual perception criterion to ensure that specific DCT spatial frequency information is contained as much as possible, while the quantization precision does not exceed the requirement. Among the DCT transform coefficients, a low-frequency coefficient has a high importance to visual sense, and therefore, assigned quantization precision is fine, a high-frequency coefficient has a low importance to visual sense, and assigned quantization precision is coarse, and in general, most of the high-frequency coefficients in one DCT transform block become zero after quantization.
Huffman coding is a coding method for statistical independent information sources to achieve the minimum average code length, namely the optimal code, and the specific coding method is as follows:
1. Messages are arranged in a sequence of probabilities according to their probability of occurrence:
2. dividing two messages with the minimum probability into a group, wherein one message is coded as 0, and the other message is coded as 1, then calculating the probability sum, and rearranging the new probability and other probability which is not processed into a new probability sequence according to the probability from large to small;
3. Repeating the steps until all probabilities are jointly processed:
4. Starting from the left, going to the rightmost side along the route taking the message as the starting point, the binary digit sequence obtained by writing the encountered binary digits to the highest position in sequence is the optimal binary non-continuous code.
Run Length Coding (Run Length Coding) is mainly used in the case where a large amount of identical data that continuously and repeatedly appears after quantization, and the amount of data representing the continuously identical data can be reduced by using runs to represent the continuously identical data. This encoding method uses a count bit followed by a binary number to represent a repeating series of binary numbers.
Predictive coding is a method of predicting a current value from M pixel values appearing in the past according to the correlation between an image in a frame and an image between frames and the visual characteristics of human eyes, and then quantizing and coding the difference between the current value and the predicted value. Let XNfor the pixel to be encoded, the first N-1 pixels are { Xi1.· N-1 }. In linear predictive coding of image signals, e.g. using the first N-1 pixels to predict the Xth pixelNa pixel having
In the formula (I), the compound is shown in the specification,Is XNThe predicted value is the value of the predicted value,In linear prediction, its prediction coefficient aiIs a constant. The obtained predicted value and the true X to be transmittedNSubtracting to obtain a prediction error enI.e. by
Obviously, the higher the prediction accuracy, the higher the prediction error enthe smaller. What is important is that the transmitting end encodes the transmission exactly this difference en(in practice, e is also required to be addednTransmitted after implementing quantization coding, etc.). Without doubt, the actual amount of data transmitted is greatly reduced by prediction. If the receiving end adopts a similar prediction mode as the transmitting end and one of the advantages of the digital signal is that the error code generated by the channel transmission can be corrected, the Nth predicted value of the receiving end is alsoNamely, it is
In the formula (I), the compound is shown in the specification,Is the 1 st predicted value of the receiving end, and the receiving end recovers the sum of the predicted value and the quantized value decoded by the receiving end, namely
As can be seen from the expressions (5-1) to (5-4), the error q occurs when the signal is recoveredn：
In general digital image processing, for a common still image or a slowly changing image, common prediction coding forms are:
1. One-dimensional prediction, i.e. sets of points { X } and X for predictionNIn the same row of the same frame image:
2. two-dimensional prediction, i.e. sets of points { X } and X for predictionNin the same frame, and { X } is equal to XNRope-like in the same or several previous rows:
3. Three-dimensional prediction, i.e. the set of points { X } used for prediction not only with XNThere are pixels encoded in the same frame and { X } there are also pixels near the corresponding positions of the next few frames.
The method adopts one-dimensional prediction and two-dimensional prediction, namely intra-frame DPCM coding, and is suitable for a system with low compression ratio requirement, wherein the three-dimensional prediction belongs to inter-frame DPCM coding and is suitable for an inter-frame prediction system with high compression ratio requirement and motion compensation, such as televisions or videos.
Referring to fig. 3, the motion compensated predictive coding includes:
1. the estimation of a displacement value, i.e. a displacement vector, of a moving object in an adjacent frame is called motion estimation (prediction) or displacement estimation, etc.
2. the resulting motion estimate is used for inter-frame prediction coding, i.e. motion compensation. Motion compensation is the prediction of a current frame pixel (or block) by displacing the pixel (or block of pixels) in a reference frame.
3. The vector or prediction error is (quantized) coded. Obviously, the prediction error at this time is also a small value, i.e., a large compression is obtained.
the dashed line portion of fig. 3 is a motion compensated predictive coding block diagram, where the motion compensated predictor consists of a frame memory (delay unit), a motion parameter estimator and a motion compensated predictor. Current frame image f1(x, y) entering encoder and predicting frame image f1(x, y) are subtracted to obtain a frame error signal e1(x, y), and quantized by a quantizer to obtain e'1the (x, y) output is encoded. The quantizer will generate a quantization distortion q (x, y) e1(x,y)-e′1(x, y), quantized Signal e'1(x, y) and predicted frame image signalsin addition toAdded in an adderOr without considering quantization distortion, theni.e. the current frame f1(x, y). The simple prediction of the pixels of the current frame by using the pixels adjacent to the relative position of the previous frame can compress the code rate greatly, but when an object moves in the image, the predicted value of the pixels of the moving part is not accurate, and the error value is large, so the efficiency is not high. However, the motion between two frames is mainly translational motion, and the displacement is not very large, so the motion parameter estimator is used to find the displacement of the object motion and the displacement V of the object in the vertical and horizontal directionsXand VYI.e. motion vector V ═ V (V)X+VY) This process is called Motion Estimation (Motion Estimation).
The input to the motion parameter estimator is the current frame f1' (x, y), the second is the image f of the previous frame1-1(x, y) (or images of the first few frames or the last few frames). Then according to the motion vector making correspondent displacement of moving image point of previous frame (or previous several frames or following several frames) to obtain estimation value of current frame, so that the predicted frame of current frame can be obtainedThis process is called Motion Compensation (Motion Compensation). The motion vectors obtained from the motion estimation are not only used for motion compensation, but also transmitted to the decoder for use in image decoding.
The key to motion compensated prediction is the estimation of motion parameters, i.e. how to estimate the best motion vector, and the following is the more important block matching algorithm.
The Block Matching algorithm (Block Matching Aigorithm) is to divide a frame image into a number of blocks of M N pixels, assuming the maximum horizontal and vertical displacements between frames are W, respectivelyXAnd WYPixels, corresponding position in the previous frame for each block of the current frameOpening up to the size of (M + 2W)x)×(N+2Wy) In the search area, the best matching block of the current frame is obtained, and then the motion vector (V) is obtainedx,Vy)。
the motion vector calculated by motion estimation is used to move the macro block in the reference frame image to the corresponding position in the horizontal and vertical directions, so as to generate the prediction of the compressed image. Motion is ordered in most natural scenes. The difference between the predicted image generated by such motion compensation and the compressed image is small. Subjective evaluation of digital image quality.
The MPEG-2 video standard coding and decoding adopts several compression coding techniques of motion compensation, discrete cosine transform, quantizer and variable length coding, etc., it not only has higher resolution, but also its image quality is good, before the video signal is sent to the compression coding, it must first be undergone the video processing to make clutter elimination, and according to the application requirements it can reduce resolution and reduce colour component or make interlaced continuous 3-dimensional transformation, etc., then make motion estimation to guide the motion compensation for eliminating time redundancy component in the image, then eliminate space redundancy component in the image, then make quantization and variable length coding, then output the bit stream after compression coding by means of buffer for channel coding transmission.
The standard adopts inter-frame prediction with motion compensation to utilize the correlation of an image in a time domain, then performs (8x8) pixel DCT on an inter-frame prediction error to utilize the correlation of the image in a space domain, and then performs adaptive quantization on DCT transform coefficient setting to fully utilize the visual characteristics of people and then adopts Huffman variable length coding to realize entropy coding; and finally, smoothing the digital code stream by adopting an output buffer register so as to keep the output digital code rate as a constant value.
More specifically, matching a preset image compression algorithm and a preset communication module corresponding to the target wireless communication network according to the communication protocol type of the target wireless communication network, and compressing and transmitting image data acquired by the inspection unmanned aerial vehicle through the preset image compression algorithm and the preset communication module specifically comprises:
if the communication protocol type of the target wireless communication network is the 4G communication network, matching a preset JPEG image compression algorithm with a preset 4G communication module, compressing image data acquired by the inspection unmanned aerial vehicle through the JPEG image compression algorithm, and transmitting the image data through the 4G communication module.
It should be noted that the JPEG image compression algorithm of this embodiment specifically includes:
First, image segmentation divides the image into small blocks of size 8X8, which are processed separately throughout the compression process.
the conversion is followed by color space conversion, so-called "color space", which means a mathematical model for expressing color, such as the "RGB" model commonly used by us, that is, decomposing color into three components of red, green and blue, so that a picture can be decomposed into three gray-scale images, and mathematically, each 8X8 pattern can be expressed as three 8X8 matrices, wherein the range of values is generally between [0,255 ].
different color models have different application scenarios, for example, an RGB model is suitable for a self-luminous pattern such as a display, while in the printing industry, printing with ink is used, the color of the pattern is generated by reflecting light, a CMYK model is generally used, and in the JPEG compression algorithm, the pattern needs to be converted into a YCbCr model, where Y denotes Luminance (luminence), and Cb and Cr denote "color difference values" of blue and red, respectively.
The concept of 'color difference' originates from the television industry, the earliest televisions are black and white, only brightness signals, namely Y signals, need to be transmitted when television signals are transmitted, after color televisions appear, two color difference signals are added outside the Y signals to transmit color information, and the purpose of doing so is to be compatible with black and white televisions, because the black and white televisions only need to process the Y signals in the signals.
According to the principle of three primary colors, the brightness contributed by the red, green and blue colors is different, the brightness of the green color is the maximum, the brightness of the blue color is the minimum, and the share of the brightness contributed by the red color is set as KRBlue color instituteThe contribution of the luminance is KBThen brightness is
Y＝KR*R+(1-KR-KB)*G+KR*B (1)
according to experience, KR＝0.299，KB0.114, then
Y＝0.299*R+0.587*G+0.114*B (2)
the color difference between blue and red is defined as follows
The mathematical formula for finally obtaining the conversion from RGB to YCbCr is
Y＝0.299*R+0.5870*G+0.114*B
Cb＝-0.1687*R-0.3313*G+0.5*B
Cr＝0.5*R-0.4187*G-0.0813*B (5)
Next, the digital signal is reconstructed by a cosine combining function based on Discrete Cosine Transform (DCT).
Data quantization, after color space conversion and discrete cosine transform, each 8X8 image block becomes three 8X8 floating-point number matrices, representing Y, Cr, Cb data, respectively, and quantization can store these floating-point numbers in less space under the condition that a part of precision can be lost, "JPEG provides a quantization algorithm as follows:
Where G is the image matrix we need to process, Q is called Quantization coefficient matrix (Quantization matrices), and the JPEG algorithm includes two standard Quantization matrices, respectively standard luminance Quantization matrices QYAnd a standard color difference quantization matrix QCfor processing the luminance data Y and the color difference data Cr and Cb, respectively.
The quantized two-dimensional matrix is then converted into a one-dimensional array to facilitate subsequent huffman compression.
The last step of JPEG compression is Huffman coding (Huffman coding) of the data, and the coding length of the elements is adjusted according to the frequency of use of the elements in the data to obtain a higher compression ratio.
More specifically, matching a preset image compression algorithm and a preset communication module corresponding to the target wireless communication network according to the communication protocol type of the target wireless communication network, and compressing and transmitting image data acquired by the inspection unmanned aerial vehicle through the preset image compression algorithm and the preset communication module specifically comprises:
if the communication protocol type of the target wireless communication network is the 5G communication network, matching a preset H.265 image compression algorithm and a preset 5G communication module, compressing image data acquired by the inspection unmanned aerial vehicle through the H.265 image compression algorithm, and transmitting the image data through the 5G communication module.
It should be noted that the h.265 image compression algorithm of the present embodiment specifically includes:
First, image partitioning, h.265 divides an image into "Coding Tree Units (CTUs)" instead of 16 × 16 macroblocks like h.264. The size of the coding tree unit may be set to 64 × 64 or limited 32 × 32 or 16 × 16 according to different coding settings. Many studies have shown that larger coding tree units can provide higher compression efficiency (and also require higher coding rates). Each coding tree unit may be recursively partitioned into 32 × 32, 16 × 16, and 8 × 8 sub-regions using a quadtree structure, and the following figure is an example of a partition of a 64 × 64 coding tree unit. Each image is further distinguished into special sets of tree coding blocks, called cuts (Slices) and Tiles (Tiles).
Each coding tree unit contains 1 luma and 2 chroma coding tree blocks and syntax elements for recording additional information. Generally, most video is compressed by YUV 4:2:0 color samples, so a 16 × 16 code tree unit is taken as an example, which includes 1 16 × 16 luma code tree block and 2 8 × 8 chroma code tree blocks.
following the transform size, each coding unit may be recursively partitioned into transform units in a quadtree fashion. H.265 has several transition sizes: 32 × 32, 16 × 16, 8 × 8, and 4 × 4. From a mathematical point of view, a larger conversion unit may better encode a static signal, while a smaller conversion unit may better encode a smaller "pulse" signal.
A prediction unit, a coding unit, can perform prediction using one of the following eight prediction modes.
Intra-frame prediction: h.265 has 35 different intra prediction modes (including those existing in 9 AVC), including DC mode, Planar (Planar) mode, and 33 directional modes. The intra prediction may follow a partition tree of the conversion unit, so the prediction mode may be applied to the conversion units of 4 × 4, 8 × 8, 16 × 16, and 32 × 32.
Inter-frame prediction: the correlation between the continuous images is utilized, the time redundancy of the video information is eliminated through a coding method of motion estimation and motion compensation, and the reconstructed frame edited previously is used as a reference frame for prediction.
for motion vector prediction, h.265 has two reference tables: l0 and L1. Each with 16 references, but the maximum number of unique pictures is 8. It uses a list index, with two main prediction modes: merge and high-level motion vectors.
Deblocking, which is based on video coding, generates blocking artifacts, and h.265 uses an adaptive loop filter in order to eliminate blocking artifacts caused by block transform as much as possible. The filter adopts different filtering weights according to the block edge information, so that the central effect can be effectively eliminated, and the sharpness of the image cannot be influenced. In addition, the loop filtering is directly carried out on the reference image at the encoder end as a part of the encoder, and compared with the deblocking filtering at the decoder end as the post-processing, the loop filtering can improve the subjective quality and effectively improve the encoding efficiency of the encoder.
The loop filter performs filtering on a 4x4 block boundary during filtering, and performs processing on a 4x4 unit block boundary in a macroblock in the order of horizontal direction and vertical direction during filtering. The filter defines "Block edge strength" for each Block boundary between 4x4 blocks (Block strength. for different Block edge strengths, the selected filter order and coefficients and the pixels to be filtered are different), wherein, for a Block using intra coding, the filtering strength is highest if the boundary is exactly the boundary of a macroblock (BS one 4), otherwise BS one 3. for neighboring blocks using inter coding, if the residual coefficients are not all 0, BS-2, if the residual coefficients are all 0, further determination is made, if the reference frames or motion vectors of the neighboring blocks are different, BS-1, otherwise, BS-0 does not perform filtering.
In the aspect of transformation, h.264 uses DCT-like transformation based on 4 × 4 pixel blocks, but uses spatial transformation based on integers, and has no problem of errors due to trade-off and inverse transformation. Compared with floating point operation, integer DCT transform causes some extra errors, but because quantization after DCT transform also has quantization error, the quantization error caused by integer DCT transform has little influence. In addition, integer DCT transform also has the advantages of reducing the operation amount and complexity and being beneficial to the fixed-point DSP transplantation.
Parallel processing, since the decoding of HEVC is much more complex than AVC, some techniques have allowed parallel decoding to be implemented. The most important are Tiles and wavefronts (Tiles and Wavefront). The image is divided into a rectangular grid of tree coding units (Tiles). Currently, a chip architecture gradually develops from single-core performance to a multi-core parallel direction, so in order to adapt to chip implementation with a very high parallelization degree, h.265 introduces many optimization ideas of parallel operation.
H.265 uses integer DCT transform based on 4x4 sub-blocks, so that the transform operation can be completed with integer addition, shift, and xor operations. According to the difference of residual data, H.265 adopts 3 transforms, for the luminance DC coefficient of the macro block of 16x 16 intra prediction mode, the hadamard transform is adopted, for the chrominance DC coefficient, the 2x 2 hadamard transform is adopted, and for the other 4x4 blocks of residual data, the integer DCT transform is adopted.
The motion estimation parallel algorithm is carefully optimized to run in a Kernel at the DEVICE end, so that the times of reading and writing the DEVICE memory are effectively reduced, and the parallel execution efficiency is improved.
entropy coding, the last step of the video coding process is entropy coding, and two different entropy coding methods are adopted in h.265: universal Variable Length Coding (UVLC) and text-based adaptive binary arithmetic coding (CABAC).
More specifically, determining the target wireless communication network with the maximum real-time signal strength from the respective real-time signal strengths further comprises:
If the real-time signal strength is the same, the priority of the wireless communication network is from high to low: a radio station communication network, a 5G communication network, and a 4G communication network.
the above is a detailed description of an embodiment of the method for switching the communication network of the inspection unmanned aerial vehicle provided by the application, and the following is a detailed description of an embodiment of the system for switching the communication network of the inspection unmanned aerial vehicle provided by the application.
referring to fig. 2, an embodiment of the present application provides an inspection unmanned aerial vehicle communication network switching system, including:
The signal acquisition unit 201 is configured to acquire real-time signal strengths of the wireless communication networks received by the inspection unmanned aerial vehicle;
A target communication network determining unit 202, configured to determine, from the respective real-time signal strengths, a target wireless communication network with the largest real-time signal strength;
And the image data processing unit 203 is used for matching a preset image compression algorithm and a preset communication module corresponding to the target wireless communication network according to the communication protocol type of the target wireless communication network, and compressing and transmitting the image data acquired by the inspection unmanned aerial vehicle through the preset image compression algorithm and the preset communication module.
More specifically, the image data processing unit 203 is specifically configured to:
If the communication protocol type of the target wireless communication network is the wireless radio station communication network, matching a preset mpeg2 image compression algorithm with a preset wireless radio station communication module, compressing image data acquired by the inspection unmanned aerial vehicle through the mpeg2 image compression algorithm, and transmitting the image data through the wireless radio station communication module.
More specifically, the image data processing unit 203 is specifically configured to:
if the communication protocol type of the target wireless communication network is the 4G communication network, matching a preset JPEG image compression algorithm with a preset 4G communication module, compressing image data acquired by the inspection unmanned aerial vehicle through the JPEG image compression algorithm, and transmitting the image data through the 4G communication module.
More specifically, the image data processing unit 203 is specifically configured to:
If the communication protocol type of the target wireless communication network is the 5G communication network, matching a preset H.265 image compression algorithm and a preset 5G communication module, compressing image data acquired by the inspection unmanned aerial vehicle through the H.265 image compression algorithm, and transmitting the image data through the 5G communication module.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. a communication network switching method for an inspection unmanned aerial vehicle is characterized by comprising the following steps:
Acquiring the real-time signal intensity of each wireless communication network received by the inspection unmanned aerial vehicle;
determining a target wireless communication network with the maximum real-time signal strength from the real-time signal strengths;
And matching a preset image compression algorithm and a preset communication module corresponding to the target wireless communication network according to the communication protocol type of the target wireless communication network, and compressing and transmitting image data acquired by the inspection unmanned aerial vehicle through the preset image compression algorithm and the preset communication module.
2. The method of claim 1, wherein the wireless communication network comprises: a 4G communication network, a 5G communication network, and a radio station communication network.
3. the method according to claim 2, wherein the matching of the preset image compression algorithm and the preset communication module corresponding to the target wireless communication network according to the communication protocol type of the target wireless communication network, and the compressing and transmitting of the image data acquired by the unmanned aerial vehicle inspection tour through the preset image compression algorithm and the preset communication module specifically comprise:
if the communication protocol type of the target wireless communication network is a wireless radio station communication network, matching a preset mpeg2 image compression algorithm with a preset wireless radio station communication module, compressing image data acquired by the inspection unmanned aerial vehicle through the mpeg2 image compression algorithm, and transmitting the image data through the wireless radio station communication module.
4. The method according to claim 2, wherein the matching of the preset image compression algorithm and the preset communication module corresponding to the target wireless communication network according to the communication protocol type of the target wireless communication network, and the compressing and transmitting of the image data acquired by the unmanned aerial vehicle inspection tour through the preset image compression algorithm and the preset communication module specifically comprise:
And if the communication protocol type of the target wireless communication network is a 4G communication network, matching a preset JPEG image compression algorithm with a preset 4G communication module, compressing the image data acquired by the inspection unmanned aerial vehicle through the JPEG image compression algorithm, and transmitting the image data through the 4G communication module.
5. The method according to claim 2, wherein the matching of the preset image compression algorithm and the preset communication module corresponding to the target wireless communication network according to the communication protocol type of the target wireless communication network, and the compressing and transmitting of the image data acquired by the unmanned aerial vehicle inspection tour through the preset image compression algorithm and the preset communication module specifically comprise:
If the communication protocol type of the target wireless communication network is a 5G communication network, matching a preset H.265 image compression algorithm with a preset 5G communication module, compressing image data acquired by the inspection unmanned aerial vehicle through the H.265 image compression algorithm, and transmitting the image data through the 5G communication module.
6. the method of claim 2, wherein said determining the target wireless communication network with the highest real-time signal strength from among the real-time signal strengths further comprises:
If the real-time signal strength is the same, the priority of the wireless communication network is from high to low: a radio station communication network, a 5G communication network, and a 4G communication network.
7. The utility model provides an unmanned aerial vehicle communication network switched systems patrols and examines, its characterized in that includes:
The signal acquisition unit is used for acquiring the real-time signal intensity of each wireless communication network received by the inspection unmanned aerial vehicle;
The target communication network determining unit is used for determining a target wireless communication network with the maximum real-time signal strength from the real-time signal strengths;
and the image data processing unit is used for matching a preset image compression algorithm and a preset communication module corresponding to the target wireless communication network according to the communication protocol type of the target wireless communication network, and compressing and transmitting the image data acquired by the inspection unmanned aerial vehicle through the preset image compression algorithm and the preset communication module.
8. The system according to claim 7, wherein the image data processing unit is specifically configured to:
If the communication protocol type of the target wireless communication network is a wireless radio station communication network, matching a preset mpeg2 image compression algorithm with a preset wireless radio station communication module, compressing image data acquired by the inspection unmanned aerial vehicle through the mpeg2 image compression algorithm, and transmitting the image data through the wireless radio station communication module.
9. the system according to claim 7, wherein the image data processing unit is specifically configured to:
And if the communication protocol type of the target wireless communication network is a 4G communication network, matching a preset JPEG image compression algorithm with a preset 4G communication module, compressing the image data acquired by the inspection unmanned aerial vehicle through the JPEG image compression algorithm, and transmitting the image data through the 4G communication module.
10. The system according to claim 7, wherein the image data processing unit is specifically configured to:
if the communication protocol type of the target wireless communication network is a 5G communication network, matching a preset H.265 image compression algorithm with a preset 5G communication module, compressing image data acquired by the inspection unmanned aerial vehicle through the H.265 image compression algorithm, and transmitting the image data through the 5G communication module.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111970519A (en) * | 2020-08-28 | 2020-11-20 | 中国人民解放军国防科技大学 | Airborne video return method |
| CN112119648A (en) * | 2019-08-29 | 2020-12-22 | 深圳市大疆创新科技有限公司 | Control method, remote server, control station, and storage medium |
| CN112436879A (en) * | 2020-09-29 | 2021-03-02 | 远传融创(杭州)科技有限公司 | Multipoint switching method applied to ultra-long-distance communication of unmanned aerial vehicle |
| CN113783603A (en) * | 2021-09-06 | 2021-12-10 | 北京京东乾石科技有限公司 | Unmanned aerial vehicle positioning method, device and system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2005015838A1 (en) * | 2003-08-12 | 2005-02-17 | Siemens Aktiengesellschaft | Method for controlling partial data transmissions to a multi-mode radio communications terminal by means of at least two different radio access methods |
| CN105052203A (en) * | 2014-02-10 | 2015-11-11 | 深圳市大疆创新科技有限公司 | Adaptive communication mode switching |
| CN107027044A (en) * | 2017-03-31 | 2017-08-08 | 大鹏高科（武汉）智能装备有限公司 | A kind of unmanned boat monitor video live broadcasting method and system |
| CN107079365A (en) * | 2017-01-17 | 2017-08-18 | 深圳市大疆创新科技有限公司 | Unmanned plane, remote control and its control method |
| CN107396426A (en) * | 2016-05-16 | 2017-11-24 | 天津航天中为数据系统科技有限公司 | Airborne Terminal and corresponding data transmission system and transmission method for unmanned plane |
| CN108900240A (en) * | 2018-08-08 | 2018-11-27 | 江阴航源航空科技有限公司 | A kind of unmanned plane internet remote control system |
| CN110248380A (en) * | 2019-06-18 | 2019-09-17 | 深圳慧源创新科技有限公司 | A kind of method of the automatic connection unmanned plane WiFi signal of user terminal |
-
2019
- 2019-10-28 CN CN201910764730.1A patent/CN110557638A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2005015838A1 (en) * | 2003-08-12 | 2005-02-17 | Siemens Aktiengesellschaft | Method for controlling partial data transmissions to a multi-mode radio communications terminal by means of at least two different radio access methods |
| CN105052203A (en) * | 2014-02-10 | 2015-11-11 | 深圳市大疆创新科技有限公司 | Adaptive communication mode switching |
| CN107396426A (en) * | 2016-05-16 | 2017-11-24 | 天津航天中为数据系统科技有限公司 | Airborne Terminal and corresponding data transmission system and transmission method for unmanned plane |
| CN107079365A (en) * | 2017-01-17 | 2017-08-18 | 深圳市大疆创新科技有限公司 | Unmanned plane, remote control and its control method |
| CN107027044A (en) * | 2017-03-31 | 2017-08-08 | 大鹏高科（武汉）智能装备有限公司 | A kind of unmanned boat monitor video live broadcasting method and system |
| CN108900240A (en) * | 2018-08-08 | 2018-11-27 | 江阴航源航空科技有限公司 | A kind of unmanned plane internet remote control system |
| CN110248380A (en) * | 2019-06-18 | 2019-09-17 | 深圳慧源创新科技有限公司 | A kind of method of the automatic connection unmanned plane WiFi signal of user terminal |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112119648A (en) * | 2019-08-29 | 2020-12-22 | 深圳市大疆创新科技有限公司 | Control method, remote server, control station, and storage medium |
| CN111970519A (en) * | 2020-08-28 | 2020-11-20 | 中国人民解放军国防科技大学 | Airborne video return method |
| CN111970519B (en) * | 2020-08-28 | 2021-06-15 | 中国人民解放军国防科技大学 | Airborne video return method |
| CN112436879A (en) * | 2020-09-29 | 2021-03-02 | 远传融创(杭州)科技有限公司 | Multipoint switching method applied to ultra-long-distance communication of unmanned aerial vehicle |
| CN113783603A (en) * | 2021-09-06 | 2021-12-10 | 北京京东乾石科技有限公司 | Unmanned aerial vehicle positioning method, device and system |
| CN113783603B (en) * | 2021-09-06 | 2023-09-29 | 北京京东乾石科技有限公司 | Unmanned aerial vehicle positioning method, device and system |
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