CN104581167A - Distributed image compression transmission method for wireless sensor network - Google Patents
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Abstract
The invention discloses a distributed image compression transmission method for a wireless sensor network. Based on an improved three-layer network structure, the method is characterized by comprising the following steps: automatically segmenting images by using an acquisition node on a first layer of the network based on the characteristic of images to be transmitted; sending various blocked images and compression parameters to a plurality of coding nodes of a second layer of the network; performing 9-7 lifting wavelet transform on the blocked images by various coding nodes according to the compression parameters and performing coding compression by using a layered wavelet tree set segmentation algorithm; sending coding files to a first transmission node of a third layer of the network; performing multiple hop transmission on the coding files to a target node by the transmission nodes of the third layer of the network; decoding reconstructed images of the received coding files by the target node. By combining the image blocking technology, the lifting wavelet transformation algorithm and the layered wavelet tree set segmentation algorithm, the distributed compression transmission on the images is realized, so that the network data transmission quantity is effectively reduced, the energy consumption of sensor nodes is balanced, and the network life cycle is prolonged.
Description
Technical field
The present invention relates to the image compressing transmission technology of radio sensing network, particularly relate to the distributed image compression transmitting method for radio sensing network.
Background technology
Data compression technique in radio sensing network compresses the data of transducer collection or information, need in sensor network to transmit a large amount of data, data to be transmitted is carried out compressing transmitting again, effectively can reduce the communication energy consumption in network, so data compression is extremely important.But compression can produce calculating energy consumption, thus in the radio sensing network of finite energy data compression scheme design need communication energy consumption, calculate energy consumption and other need consider Different factor between trade off.
Image compression is data compression technique application on the digital image.Because radio sensing network sensor node energy is extremely restricted, multimedia signal processing is the significant bottleneck problem in radio sensing network scientific research always.The image compression object be applied in radio sensing network reduces the redundant information in view data, thus by form storage more efficiently and transmission data, reduce the communication energy consumption in network.
Distributed image compression is that multiple node exchanges cooperation mutually, jointly completes the compression calculation task of target image.Because amount of image information is huge, compressed image needs larger data buffer storage space and calculates energy consumption.And in radio sensing network, node needs to bear the tasks such as data acquisition, information calculating and transmission of messages, if the pressure gathering in network, calculate, transmit is concentrated on a single node, then not only increase storage and the processing pressure of node, more can reduce the life-span of this node.The distributed image application be compressed in radio sensing network is on multiple sensor nodes that can communicate mutually in the complicated image of individual node compression distribution of computation tasks to wireless sensor network, multiple node is by cooperating with each other, exchanging, common calculating, storage and transmission data, cooperated complicated image compression tasks.By distributed image compression energy, the compression calculation task of single node is evenly distributed to multinode to bear, efficient balance network energy consumption, is conducive to the life cycle increasing whole network.
Summary of the invention
For the problems referred to above, the distributed image compression transmitting method that the present invention proposes, object is the energy consumption of balanced radio sensing network processing image information, extends network lifecycle.
For achieving the above object, the invention provides based on a kind of distributed image compression transmitting method for radio sensing network, comprise the steps::
Step (1) according to the feature of image to be transmitted, to image automatic uniform piecemeal, is sent to the compression parameters required and each block image multiple coding nodes of the network second layer at the acquisition node of network first tier respectively;
Step (2) receives the block image of network first tier acquisition node transmission in each coding nodes of the network second layer, separately wavelet transformation is carried out to the block image received, adopt the image compression algorithm based on wavelet transformation to carry out compressed encoding to the matrix of wavelet coefficients data after conversion according to the compression parameters received again, obtain coded file; Wavelet Transformation Algorithm wherein adopts and promotes 9-7 wavelet transformation, and image compression algorithm adopts layering wavelet tree set-partition SPIHT (Set Partitioning In Hierarchical Trees) algorithm;
Step (3) is sent to the first transmission node of network third layer at the coded file that each coding nodes handle of the network second layer obtains in step (2);
Step (4) at the transmission node multi-hop transmission coded file of network third layer to destination node;
Step (5) is decoded and inverse wavelet transform to all coded files received at the destination node of network third layer, integrates, reconstructed image to data.
In technique scheme, the image automatic Partitioning method described in step (1) is realized by following steps:
(1.1) detect image to be transmitted and whether be coloured image and the size detecting image to be transmitted;
(1.2) if gray level image and image is less than 50KB, then not block image;
(1.3) if gray level image and image is greater than 50KB, then image is divided into 4 pieces;
(1.4) if coloured image and image is less than 150KB, then image is divided into 3 subgraphs by RGB triple channel;
(1.5) if coloured image and image is greater than 150KB, then image is divided into 3 subgraphs by RGB triple channel, each subgraph is divided into 4 pieces again, is divided into into 12 block images.
In technique scheme, step (2) is described to be realized by following steps the process that block image carries out compressed encoding:
(2.1) lifting 9-7 wavelet transformation is carried out to the block image received, generally carry out 5 layers of wavelet decomposition;
(2.2) layering wavelet tree set-partition spiht algorithm initial threshold is calculated,
Initial threshold T
0=2
n, wherein
Initialization significant coefficient table LSP (List of Significant Pixels) is empty set;
The inessential coefficient table LIP of initialization (List of Insignificant Pixels), LIP={c (i, j) ∈ H};
Initialization inessential subset table LIS (List of Insignificant Sets),
LIS={D (i, j) | c (i, j) ∈ H and there is non-zero descendants;
Wherein, c (i, j) represents the coefficient value of matrix of wavelet coefficients mid point (i, j), and i represents the line number at this place, and j represents the columns at this place; H represents the coordinate set of all tree roots; D (i, j) represents all descendants's coordinate sets of point (i, j);
(2.3) sort pass: under the threshold value of current restriction, carries out scanning encoding to the significant coefficient in matrix of wavelet coefficients, is divided into the inessential coefficient table LIP of scanning and the inessential subset table LIS two parts of scanning:
(2.3.1) inessential coefficient table LIP is scanned, detect each coefficient in LIP successively, if effectively export " 1 " and this coefficient symbols position (if coefficient of efficiency be on the occasion of, export " 1 ", for negative value then exports " 0 "), and this coefficient is moved on to significant coefficient table LSP, if invalid, export " 0 "; Wherein, if definition is greater than current threshold value T by the absolute value of scan fraction, then this coefficient is effective, otherwise then thinks that this coefficient is invalid;
(2.3.2) inessential coefficient table LIS is scanned;
If (2.3.2.1) this set is D (i, j), export D (i, j) validity, and if D (i, j) is effective, D (i, j) is divided into O (i, j) and L (i, j);
Wherein D (i, j) represents all descendants's coordinate sets of point (i, j), O (i, j) represents four of point (i, j) direct children's coordinate sets, L (i, j) represents descendants's coordinate set of all non-immediate children of point (i, j);
(2.3.2.1.1) detect the validity of four coefficients in O (i, j), effective coefficient moves on to significant coefficient table LSP, and exports " 1 " and coefficient symbols position, and invalid coefficient moves on to inessential coefficient table LIP, and exports " 0 ";
(2.3.2.1.2) L (i is detected, j) whether empty set, if not empty set, point (i, j) is added inessential subset table LIS, label L (i, j), step (2.3.2.2) is forwarded to, if empty set, point (i, j) is shifted out inessential subset table LIS;
If (2.3.2.2) this set is L (i, j), export L (i, j) validity, if L (i, j) is effective, meet (k by each, l) ∈ O (i, j) point adds inessential subset table LIS, is labeled as D (k, l), and point (i, j) is shifted out from inessential subset table LIS;
(2.4) micronization processes, in scanning significant coefficient table LSP, non-present scans the point obtained specifically, and export the n-th the highest effective value, the definition of n is as shown in (2.2).
(2.5) calculate new threshold value, threshold value calculates Ru shown in (2.2), returns (2.3), carries out next round scanning, until reach the requirement of compression parameters;
(2.6) this coding nodes of acquisition is exported to the compressed encoding file of block image by the compressed encoding of (2.3) to (2.5).
In technique scheme, the decoded reconstructed image described in step (5) is realized by following steps:
(5.1) the target node accepts coded file of network third layer, understand coded file, obtain the image block mode of this coded file, compressed encoding parameter, detect the coded file whether having received all block images of complete original image, if not yet receive the next coded file of complete then continuation wait-receiving mode;
(5.2) destination node is according to the compressed encoding parameter comprised in coded file, the coded file received in decode one by one (5.1), and obtain matrix of wavelet coefficients data, the coding/decoding method adopted is layering wavelet tree set-partition algorithm;
(5.3) destination node does inverse wavelet transform to the matrix of wavelet coefficients obtained in (5.2), obtains each block image data, and the wavelet transformation adopted is for promoting 9-7 wavelet transformation;
(5.4) destination node is according to the information of the image block mode comprised in coded file, the block image data obtained in splicing (5.3), reconstruct original image.
In technique scheme, step (1) to step (5) is all the Three Tiered Network Architectures based on improvement described below:
(1) network first tier is acquisition node, and acquisition node major responsibility is segmentation image and determine compression parameters, sends block image and the compression parameters multiple coding nodes to the network second layer;
(2) the network second layer comprises multiple coding nodes being in acquisition node periphery, coding nodes major responsibility is that the compression parameters of informing according to acquisition node carries out wavelet transformation and compressed encoding to the block image received, then the coded file obtained after compressed encoding is sent to the first transmission node of network third layer;
(3) network third layer comprises multiple transmission node and destination node, and first transmission node receives the coded file that network second layer coding nodes sends, and by other transmission node multi-hop transmission coded files to destination node.Destination node major responsibility is decoded and inverse wavelet transform to all coded files received, and integrates, reconstructed image to the data obtained.
Compared with prior art, tool of the present invention has the following advantages and beneficial effect:
(1) network data transmission amount is reduced: the present invention makes full use of the combination promoting 9-7 Wavelet Transformation Algorithm and layering wavelet tree set-partition spiht algorithm, when keeping original picture quality, image being compressed as far as possible, greatly reducing the volume of transmitted data of radio sensing network;
(2) equalizing network node energy consumption: the Three Tiered Network Architecture that the present invention proposes improvement, based on this network configuration, distributed compression transmission is carried out to image, compared to centralized compression transmission means, the compression that multiple coding nodes can be adopted to share individual node calculates energy consumption, at whole each node energy consumption of radio sensing network efficient balance, extend the overall life cycle of network;
(3) simplified network structure: at classics based in the distributed treatment algorithm of wavelet transformation, usually the structure of bunch only responsible one-level wavelet transformation of each level, and generally need the wavelet transformation to view data carries out 4 to 5 grades just can reach good compressed encoding effect, cause adopting the network configuration of classical distributed algorithm to need multiple bunches of groups and great deal of nodes to participate in, network layer is many, complex structure, easily there is mistake, and due to the complex network structures of multi-level multinode, there is the excessive problem of network service energy consumption in classic algorithm;
And in the network configuration of the present invention's proposition, greatly simplify network configuration, network first tier only has an acquisition node, the network second layer adopts multiple coding nodes of single stage network framework, network third layer only includes transmission node and destination node, the sensor node division of labor being in network at all levels is clear and definite, network configuration is simple, easy maintenance, and in this Three Tiered Network Architecture, all adopt one direction transfer of data between node and node, effectively can reduce whole wireless sensing network system data round-trip transmission energy consumption compared to the classical distributed algorithm of employing.
Accompanying drawing explanation
Fig. 1 is the Three Tiered Network Architecture figure for distributed image compression transmission improved;
Fig. 2 is the distributed image compression transport network architecture figure that embodiment is set up;
Fig. 3 is embodiment image distribution formula compression transmission general flow chart.
Embodiment
For making the object, technical solutions and advantages of the present invention clearly, below in conjunction with drawings and Examples, the present invention is described in further detail, but the scope of protection of present invention is not limited to the scope of execution mode statement.
The Three Tiered Network Architecture for distributed image compression transmission of the improvement that the present invention proposes as shown in Figure 1, the network configuration that embodiment adopts 18 nodes to set up as shown in Figure 2, the network configuration set up in known embodiment comprises 1 acquisition node of ground floor network, 12 coding nodes of second layer network, 4 transmission nodes of third layer network and 1 destination node.
The function that embodiment realizes is that the method that a width coloured image is transmitted by distributed compression is transferred to destination node.
Embodiment is that the distributed compression transmission of image devises a set of operational processes platform, can realize the functions such as the automatic Partitioning of image, distributed compression coding, decoding and reconstituting, wireless transmission and reception.Mainly comprise image block, compressed encoding, wireless transmission, decoding and reconstituting four module.Fig. 3 is embodiment image distribution formula compression transmission general flow chart, is described in detail below to how realizing image distribution formula compression transmission in embodiment.
Step (1) according to the feature of image to be transmitted, to image automatic uniform piecemeal, is sent to the compression parameters required and each block image multiple coding nodes of the network second layer at the acquisition node of network first tier respectively.
First image automatic Partitioning method in step (1) detects color bit depth and the image size of image to be transmitted, if gray level image and image is less than 50KB, then and not block image; If gray level image and image is greater than 50KB, then image is divided into 4 pieces; If coloured image and image is less than 150KB, then image is divided into 3 subgraphs by RGB triple channel; If coloured image and image is greater than 150KB, then image is divided into 3 subgraphs by RGB triple channel, each subgraph is divided into 4 pieces again, is divided into into 12 block images.
The image to be transmitted selected in an embodiment is resolution is 512*512, and the pixel bit degree of depth is 24, and size is the standard color test pattern picture of 768KB.According to the image automatic Partitioning method of this invention, this image is divided into automatically the gray level image of 12 pieces of formed objects in acquisition node, the resolution of every block subgraph is 256*256, and the pixel bit degree of depth is 8, and size is 65.0KB.
The acquisition node of network first tier selects the compression parameters required to be that (practical application can the quality of reconstructed image and the demand to communication energy consumption as requested in compression 32 times in an embodiment, select different compression multiples, compression multiple is lower, the quality of reconstructed image is better but communication energy consumption is larger), the compression parameters of each block image and requirement being sent to separately a coding nodes of the network second layer, is namely be sent to 12 coding nodes altogether.
Step (2) carries out wavelet transformation in each coding nodes of the network second layer to the block image received, adopt the image compression algorithm based on wavelet transformation to compress the matrix of wavelet coefficients data after conversion according to the compression parameters received again, obtain coded file; Wavelet Transformation Algorithm wherein adopts and promotes 9-7 wavelet transformation, and image compression algorithm adopts layering wavelet tree set-partition spiht algorithm.
Because 12 pieces of block images to be transferred to 12 coding nodes of the network second layer by step (1) in embodiment respectively, therefore step (2) is carried out in 12 coding nodes of the network second layer simultaneously, for one of them coding nodes in embodiment, step (2) is described to be realized by following steps the process that block image carries out compressed encoding:
(2.1) receive the compression parameters of block image and requirement, carry out lifting 9-7 wavelet transformation to the block image received, acquiescence carries out 5 layers of wavelet decomposition.
According to emulation testing, adopt in the image compression of radio sensing network and promote 9-7 wavelet transformation, ideal image reconstruction quality can be obtained under lower calculating energy consumption, and in compressed image, adopting 5 layers of lifting 9-7 wavelet transformation decomposed to be enough to reconstruct most of image preferably, the wavelet decomposition of 5 layers is suitable selection.
(2.2) adopt layering wavelet tree set-partition spiht algorithm according to the compression parameters received in step (2.1), the matrix of wavelet coefficients obtained is compressed, obtain compressed encoding file in step (2.1).
According to emulation testing, compress in radio sensing network to image, adopt layering wavelet tree set-partition algorithm to be suitable selection, spiht algorithm has outstanding advantage compared to other algorithms based on wavelet transformation.At Embedded Zerotree Wavelet Coding EZW (Embedded Zerotree Wavelet) algorithm, in the image compression algorithm that embedded block coding EBCOT (the Embedded Block Coding with Optimized Truncation) algorithm these three of layering wavelet tree set-partition spiht algorithm and optimal truncation is classical, spiht algorithm has better compression efficiency compared to EZW algorithm, spiht algorithm has lower calculating energy consumption and less memory requirements compared to EBCOT algorithm, therefore layering wavelet tree set-partition spiht algorithm is best suited for being applied to the algorithm compressed image at radio sensing network in these three algorithms.
In an embodiment, compression parameters as requested, each coding nodes needs the block image of 65.0KB to compress 32 times, and each coded file size after compressed encoding is 2.02KB.
Each coding nodes of step (3) is transferred to the coded file obtained in step (2) the first transmission node of network third layer.
In an embodiment, network second layer net has 12 coding nodes, and each coding nodes sends the first transmission node of a coded file to network third layer, and therefore this transmission node need receive 12 coded files altogether.
The transmission node multi-hop transmission coded file of step (4) network third layer is to destination node.
In an embodiment, network third layer has 4 transmission nodes and 1 destination node, and each transmission node needs to receive and sends 12 coded files to next node.
The destination node of step (5) network third layer is decoded and inverse wavelet transform to all coded files received, and integrates, reconstructed image to data.
In an embodiment, destination node needs reception 12 coded files altogether, and step (5) described decoded reconstructed image is realized by following steps:
(5.1) the target node accepts coded file of network third layer, understand coded file, obtain the image block mode of this coded file, compressed encoding parameter, detect the coded file whether having received all block images of complete original image, if not yet receive the next coded file of complete then continuation wait-receiving mode;
(5.2) destination node is according to the compressed encoding parameter comprised in coded file, the coded file received in decode one by one (5.1), and obtain matrix of wavelet coefficients data, the coding/decoding method adopted is layering wavelet tree set-partition algorithm;
(5.3) destination node does inverse wavelet transform to the matrix of wavelet coefficients obtained in (5.2), obtains each block image data, and the wavelet transformation adopted is for promoting 9-7 wavelet transformation;
(5.4) destination node is according to the information of the image block mode comprised in coded file, the block image data obtained in splicing (5.3), reconstruct original image.
Network data transmission component analysis: in an embodiment, original image size is 768KB, adopt the distributed compression transmission method that this invention proposes, the acquisition node of network first tier needs the coding nodes of block image to the network second layer of transmission 12 65.0KB, the all coding nodes of the network second layer need the first transmission node of coded file to network third layer of transmission 12 2.02KB altogether, network third layer has 4 transmission nodes, each transmission node needs the coded file of transmission 12 2.02KB to next node, therefore adopt the distributed compression transmission method that the present invention proposes, need altogether to send 901.2KB data.If adopt the mode directly sending original image, suppose between acquisition node and destination node, have 4 transmission nodes, then acquisition node and each transmission node all need the data sending 768KB, need the data sending 3840KB altogether.Therefore adopt the distributed compression transmission algorithm that this invention proposes, the volume of transmitted data of more than 70% can be saved.
Radio sensing network energy consumption analysis: in an embodiment, the energy consumption of each node is as follows:
The energy consumption of acquisition node is E
s=12 × M × E
tX(d);
The energy consumption of coding nodes is E
c=M × (E
rX+ E
tX(d)/r+E
comp);
The energy consumption of transmission node is E
t=12 × M × (E
tX(d)+E
rX)/r;
The energy consumption of destination node is E
d=12 × M × (E
rX+ E
ncomp)/r
Network total energy consumption is E
1=E
s+ 12 × E
c+ 4 × E
t+ E
d;
Wherein M is the size of every block block image, and unit is bit; E
tXd () is for being sent to the energy consumption of distance d by 1bit data; E
rXfor receiving the energy consumption of 1bit data; E
compfor 1bit data being done the energy consumption needed for wavelet transformation and compressed encoding; E
ncompfor 1bit data being done the energy consumption needed for inverse wavelet transform and decoding; R is compression ratio.
Suppose that each euclidean distance between node pair is 5m, that is to say that data transmission range is 5m, then in embodiment, acquisition node energy consumption is 0.33J, coding nodes energy consumption is 0.15J, transmission node energy consumption is 0.02J, and destination node energy consumption is 0.06J, and embodiment network total energy consumption is 2.27J.
And if original image is directly transmitted in employing, acquisition node and each transmission node all need to send 768KB data, destination node and each transmission node all need to receive 768KB data, then network total energy consumption is 3.22J, under the environment set of embodiment, adopt distributed compression transmission means, network total energy consumption can save about 30%.
And if adopt centralized compression transmission mode, carry out direct compressed encoding at acquisition node to image, then acquisition node energy consumption is 1.49J, and transmission node energy consumption is 0.02J, and destination node energy consumption is 0.06J, and network total energy consumption is 1.63J.
From upper data, transmitting image in radio sensing network, compressed image transmit again for minimizing communication energy consumption have significant effect, and adopt the network total energy consumption of distributed compression transmission means can a little more than employing centralized compression transmission means.This is because the thought of the distributed compression transmission method of the present invention's proposition is that the calculating energy consumption apportioning of compressed image is carried out to periphery coding nodes, and therefore distributed compression transmission method has had more than centralized compression transmitting method and once sent the energy consumption with reception block image in the main-process stream of compression transmission picture.
From upper data, in embodiment, acquisition node energy consumption is 0.33J, a little more than coding nodes energy consumption 0.15J, transmission node energy consumption 0.02J, destination node energy consumption 0.06J, in distributed compression transmission method, acquisition node energy consumption is the highest in each node energy consumption.And in centralized compression transmitting method, acquisition node energy consumption is 1.49J, transmission node energy consumption is 0.02J, destination node energy consumption 0.06J, and acquisition node energy consumption is still the highest in each node energy consumption.Therefore, in these two kinds of methods, the life cycle of radio sensing network is all subject to the restriction of acquisition node life cycle.
Can find out, in distributed compression transmission method, the distribution of each node energy consumption is comparatively balanced, the compression of having shared image due to coding nodes calculates energy consumption, the acquisition node energy consumption of distributed compression transmission method, far below the energy consumption of acquisition node in centralized compression transmitting method, only has 22.15% of the latter's energy consumption.Therefore, although adopt the network total energy consumption of distributed compression transmission method a little more than the total energy consumption adopting centralized compression transmitting method, but the energy distribution of each node in network due to distributed compression transmission method efficient balance, the distributed compression transmission method adopting the present invention to propose, can make network lifecycle compared to the network lifecycle adopting centralized compression transmitting method and promote about 3 to 4 times.
Claims (4)
1., for a distributed image compression transmitting method for radio sensing network, it is characterized in that the method is based on a kind of Three Tiered Network Architecture, comprise the steps:
Step (1) according to the feature of image to be transmitted, to image automatic uniform piecemeal, and is sent to the compression parameters required and each block image multiple coding nodes of the network second layer at the acquisition node of network first tier respectively;
Step (2) receives the block image of network first tier acquisition node transmission in each coding nodes of the network second layer, separately wavelet transformation is carried out to the block image received, adopt the image compression algorithm based on wavelet transformation to carry out compressed encoding to the matrix of wavelet coefficients data after conversion according to the compression parameters received again, obtain coded file;
Step (3) is sent to the first transmission node of network third layer at the coded file that each coding nodes handle of the network second layer obtains in step (2);
Step (4) at the transmission node multi-hop transmission coded file of network third layer to destination node;
Step (5) is decoded and inverse wavelet transform to all coded files received at the destination node of network third layer, integrates, reconstructed image to data.
2. a kind of distributed image compression transmitting method for radio sensing network according to claim 1, is characterized in that the automatic Partitioning image method that have employed a kind of improvement in step (1), comprises the steps:
(1.1) detect image to be transmitted and whether be coloured image and the size detecting image to be transmitted;
(1.2) if gray level image and image is less than 50KB, then not block image;
(1.3) if gray level image and image is greater than 50KB, then image is divided into 4 pieces;
(1.4) if coloured image and image is less than 150KB, then image is divided into 3 subgraphs by RGB triple channel;
(1.5) if coloured image and image is greater than 150KB, then image is divided into 3 subgraphs by RGB triple channel, each subgraph is divided into 4 pieces again, is divided into into 12 block images.
3. a kind of distributed image compression transmitting method for radio sensing network according to claim 1, is characterized in that the method is in conjunction with lifting wavelet transform and realize effective compressed encoding of image and decoding based on the image compression algorithm of wavelet transformation; Wherein, what the inverse wavelet transform in the wavelet transformation in step (2) and step (5) adopted is promote 9-7 wavelet transformation, and what the decoding in the compressed encoding in step (2) and step (5) adopted is layering wavelet tree set-partition spiht algorithm.
4. a kind of distributed image compression transmitting method for radio sensing network according to claim 1, is characterized in that the process of destination node decoded reconstructed image in step (5) comprises the steps:
(5.1) the target node accepts coded file of network third layer, understand coded file, obtain the image block mode of this coded file, compressed encoding parameter, detect the coded file whether having received all block images of complete original image, if not yet receive the next coded file of complete then continuation wait-receiving mode;
(5.2) destination node is according to the compressed encoding parameter comprised in coded file, the coded file received in decode one by one (5.1), and obtain matrix of wavelet coefficients data, the coding/decoding method adopted is layering wavelet tree set-partition algorithm;
(5.3) destination node does inverse wavelet transform to the matrix of wavelet coefficients obtained in (5.2), obtains each block image data, and the wavelet transformation adopted is for promoting 9-7 wavelet transformation;
(5.4) destination node is according to the information of the image block mode comprised in coded file, the block image data obtained in splicing (5.3), reconstruct original image.
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| CN109766319A (en) * | 2018-12-27 | 2019-05-17 | 网易（杭州）网络有限公司 | Compression duty processing method, device, storage medium and electronic equipment |
| CN109766319B (en) * | 2018-12-27 | 2021-05-11 | 网易（杭州）网络有限公司 | Compression task processing method and device, storage medium and electronic equipment |
| CN112616054A (en) * | 2020-12-11 | 2021-04-06 | 北京林业大学 | Self-adaptive compression transmission and recovery method and device for wild animal monitoring image |
| CN112616040A (en) * | 2020-12-11 | 2021-04-06 | 北京林业大学 | Wild animal image transmission method and system based on distributed architecture |
| CN112616054B (en) * | 2020-12-11 | 2023-03-03 | 北京林业大学 | Self-adaptive compression transmission and recovery method and device for wild animal monitoring image |
| CN116033380A (en) * | 2023-03-28 | 2023-04-28 | 华南理工大学 | Data collection method of wireless sensor network under non-communication condition |
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