CN106558085A - The compression method of geographic information image - Google Patents
The compression method of geographic information image Download PDFInfo
- Publication number
- CN106558085A CN106558085A CN201611154931.2A CN201611154931A CN106558085A CN 106558085 A CN106558085 A CN 106558085A CN 201611154931 A CN201611154931 A CN 201611154931A CN 106558085 A CN106558085 A CN 106558085A
- Authority
- CN
- China
- Prior art keywords
- frequency data
- image
- geographic information
- information image
- piecemeal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T9/00—Image coding
- G06T9/007—Transform coding, e.g. discrete cosine transform
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Discrete Mathematics (AREA)
- Multimedia (AREA)
- Remote Sensing (AREA)
- Compression Of Band Width Or Redundancy In Fax (AREA)
- Compression Or Coding Systems Of Tv Signals (AREA)
Abstract
Present invention is disclosed a kind of compression method of geographic information image, including：Image semantic classification step, obtains geographic information image, and piecemeal is carried out to geographic information image according to the landform rule in geographic information image, and landform rule includes the rugged degree of landform；Image transformation step, for each piecemeal, conversion process is carried out to image using two-dimensional wavelet transformation, wherein two-dimensional wavelet transformation using biorthogonal wavelet and two-dimensional wavelet transformation be broken down into twice one-dimensional wavelet transform being processed, one-dimensional wavelet transform is respectively low-frequency data conversion and high-frequency data conversion twice, and low-frequency data conversion and high-frequency data conversion are parallel processings；Image editing method, quantifies to the image through two-dimensional wavelet transformation, then is encoded, and the coding is using ECBOT algorithms.The present invention's reduces for the amount of calculation of image procossing, and arithmetic speed is higher, but picture quality is not subject to a significant impact.
Description
Technical field
The present invention relates to geographic information processing technology, more particularly to a kind of compression method of geographic information image.
Background technology
The application of GIS-Geographic Information System (GIS) is rapidly progressed.The basis of GIS-Geographic Information System is high-resolution
The electronic chart of degree, high-resolution and pin-point accuracy.Electronic chart can show dimensional topography, Military Simulation, urban planning,
There is high value in the fields such as digital technology application.
Fine definition, high-resolution electronic chart mean the huge geographic information image of data volume.Using these
During image, computer is needed to possess very big memory size and memory space, and very strong operational capability.Although computer is hard
Part technology has obtained high speed development, but when these geographic information images wish to be used widely on network, the network bandwidth
The using effect of these geographic information images is limited or can be affected with the performance of terminal unit.
Then, need to be improved in the method for image procossing, reduce operand as far as possible so that these geography information
Image still can be run glibly in limited bandwidth and in the also limited applied environment of terminal unit operational capability.
The content of the invention
It is contemplated that proposing a kind of compression method of the geographic information image with relatively low operand, the method is used
Wavelet transformation is being compressed to image.
An embodiment of the invention, proposes a kind of compression method of geographic information image, including：
Image semantic classification step, obtains geographic information image, according to the landform rule in geographic information image to geographical letter
Breath image carries out piecemeal, and landform rule includes the rugged degree of landform；
Image transformation step, for each piecemeal, carries out conversion process to image using two-dimensional wavelet transformation, wherein two
Dimension wavelet transformation using biorthogonal wavelet and two-dimensional wavelet transformation be broken down into twice one-dimensional wavelet transform being processed, twice
One-dimensional wavelet transform is respectively low-frequency data conversion and high-frequency data conversion, and low-frequency data conversion and high-frequency data conversion are parallel
Process；
Image editing method, quantifies to the image through two-dimensional wavelet transformation, then is encoded, and the coding is adopted
ECBOT algorithms.
In one embodiment, biorthogonal 2-d wavelet is biorthogonal 9/7 small echo (CDF97) wave filter.
In one embodiment, low-frequency data conversion is included the data and biorthogonal of the geographic information image of a piecemeal
2-d wavelet carries out convolution algorithm, extracts even item as low-frequency data.High-frequency data conversion is included the geography of a piecemeal
The data of frame carry out convolution algorithm with biorthogonal 2-d wavelet, extract odd term as high-frequency data.
In one embodiment, low-frequency data conversion and high-frequency data conversion is respectively mapped to low-frequency data stream control figure and height
Frequency data stream control figure, carries out concurrency merging on low-frequency data stream control figure and high-frequency data stream control figure, realizes that low-frequency data becomes
Change the parallel processing with high-frequency data conversion.
In one embodiment, also include being smoothed the border of piecemeal in image editing method, it is described smooth
Process includes：On the border of piecemeal, with border as axle, the data of axle both sides preset range are mutually mapped, after mapping
Data calculate dispersed elevation value, the data in the boundaries on either side preset range of piecemeal are adjusted according to dispersed elevation value.
The compression method of the geographic information image of the present invention causes to reduce for the amount of calculation of image procossing, and arithmetic speed is more
Height, but picture quality is not subject to a significant impact, is conducive to fine definition, high-resolution electronic chart in network and various
Popularization and application in terminal.
Description of the drawings
The flow chart that Fig. 1 discloses the compression method of the geographic information image of an embodiment of the invention.
Specific embodiment
With reference to shown in Fig. 1, the present invention proposes a kind of compression method of geographic information image, including the steps：
Image semantic classification step, obtains geographic information image, according to the landform rule in geographic information image to geographical letter
Breath image carries out piecemeal.In one embodiment, described landform rule includes the rugged degree of landform, and rugged degree can be using such as
Under mode defining：In the region selected, height above sea level is in the range of ± the 1096 of the sea level on the average in the region
Area accounts for the ratio of the region gross area.When the ratio is more than 7096, the rugged degree in the region is flat.When the ratio exists
30%～70%.It is that the rugged degree in the region is medium.When the ratio is below 3096, the rugged degree in the region is height
It is rugged.In one embodiment, the factor that landform rule also needs to consideration is the division of administrative region.Generally, rugged degree
Can be integrated into considering with administrative region, such as：Being first according to rugged degree carries out initial piecemeal, then according to row in initial piecemeal
Administrative division domain carries out piecemeal again.Or, being first according to administrative region carries out initial piecemeal, then according to rugged in initial piecemeal
Degree carries out piecemeal again.
Image transformation step, for each piecemeal, carries out conversion process to image using two-dimensional wavelet transformation, wherein two
Dimension wavelet transformation using biorthogonal wavelet and two-dimensional wavelet transformation be broken down into twice one-dimensional wavelet transform being processed, twice
One-dimensional wavelet transform is respectively low-frequency data conversion and high-frequency data conversion, and low-frequency data conversion and high-frequency data conversion are parallel
Process.
In one embodiment, 9/7 small echo of biorthogonal (CDF97) wave filter used in step 104.By two-dimensional wavelet transformation
Be decomposed into low frequency and high frequency twice one-dimensional wavelet transform being processed so that operand is reduced, while arithmetic speed is carried
Rise.At step 104, low-frequency data conversion is included the data of the geographic information image of a piecemeal and biorthogonal 2-d wavelet
Convolution algorithm being carried out, even item being extracted as low-frequency data, high-frequency data conversion is included the geographic information image of a piecemeal
Data and biorthogonal 2-d wavelet carry out convolution algorithm, extract odd term as high-frequency data.
One specific algorithm example is as follows：
LPn=-55hk (2n)
BPn=-22gk (2n)
Wherein, 2n for piecemeal image in some pixel value, hk and gk be downsampling factor, LPn and BPn difference table
Show high fdrequency components and low frequency component.Basic computational methods are the values of the continuous several points on sampled images with a line, respectively with
Sued for peace after particular factor quadrature again, odd term and even item are then extracted respectively as high-frequency data and low-frequency data.
SI=SI-1+ α (DI-1-11+DI)
DI=DI-1+ β (SI-1+10)
Wherein Si represents the value of i-th odd point of pixel in a line after convolution algorithm, during di is represented equally
The value of i-th even number point of the pixel after convolution algorithm.α and β is fixed coefficient, and the coefficient is the requirement according to concrete application
Select.
The computing formula of summary, the computing formula of high-frequency data conversion can be evolved into following form：
In one embodiment, in order that the speed for calculating preferably is lifted, low-frequency data is converted and high frequency
Also it is processed in parallel according to conversion.The operating process of parallel processing is as follows：Low-frequency data is converted and high-frequency data conversion maps respectively
To low-frequency data stream control figure and high-frequency data stream control figure.Concurrency is carried out on low-frequency data stream control figure and high-frequency data stream control figure
Merge, realize the parallel processing of low-frequency data conversion and high-frequency data conversion.It is on data-flow-control figure, all in calculating process
Mutually do not have the data of dependence be extracted carry out and meanwhile computing, realize data parallel.Similarly, institute is either with or without phase
Mutually the subalgorithm or subprocess of dependence is also extracted while run, and realizes that algorithm is parallel.Synthetic data it is parallel and
Algorithm is parallel, realizes the parallel processing of low-frequency data conversion and high-frequency data conversion, due to not having between parallel data and algorithm
There is relation of interdependence, so will not impact to operation result.
106. image editing methods, quantify to the image through two-dimensional wavelet transformation, then are encoded, the coding
Using ECBOT algorithms.In one embodiment, for the discontinuous problem of the transition for improving piecemeal boundary.In step 106
Increased the process that the border to piecemeal is smoothed.Smoothing processing includes：On the border of piecemeal, with border as axle,
The data of axle both sides preset range are mutually mapped, dispersed elevation value is calculated to the data after mapping, according to dispersed elevation value pair
Data in the boundaries on either side preset range of piecemeal are adjusted.
The specific algorithm of smoothing processing is as follows：
With border as axle, determine the preset range of boundaries on either side, can such as set pixel quantity m, that is, take and be distributed in side
Pixel of the sum of boundary both sides for m points.After setting preset range, the dispersed elevation of the data of the pixel of every a line is calculated respectively
Value A.
Then to the row in the pixel adjacent per a pair be smoothed process, if the data of two consecutive points it
Between absolute difference be more than a threshold delta, then it is assumed that be rough between two consecutive points., whereas if two phases
Absolute difference between the data of adjoint point is not more than the threshold delta, then it is assumed that be smooth between two consecutive points.For flat
Two sliding consecutive points, are no longer acted upon.
For rough consecutive points x (i) are with x (i+1) and dispersed elevation value A is sentenced sum of all pixels by x (i) ＜ x (i+1)
Adjusted step-lengths A/m of m.
Order
Above-mentioned process is performed repeatedly, until being all smooth between all of consecutive points in a line.
Next line is selected, repeats above-mentioned process again, the smoothing processing until all rows are all done.
The compression method of the geographic information image of the present invention causes to reduce for the amount of calculation of image procossing, and arithmetic speed is more
Height, but picture quality is not subject to a significant impact, is conducive to fine definition, high-resolution electronic chart in network and various
Popularization and application in terminal.
Claims (5)
1. a kind of compression method of geographic information image, it is characterised in that include：
Image semantic classification step, obtains geographic information image, according to the landform rule in geographic information image to geographical hum pattern
As carrying out piecemeal, the landform rule includes the rugged degree of landform；
Image transformation step, for each piecemeal, carries out conversion process to image using two-dimensional wavelet transformation, wherein described two
Dimension wavelet transformation using biorthogonal wavelet and two-dimensional wavelet transformation be broken down into twice one-dimensional wavelet transform being processed, twice
One-dimensional wavelet transform is respectively low-frequency data conversion and high-frequency data conversion, and low-frequency data conversion and high-frequency data conversion are parallel
Process；
Image editing method, quantifies to the image through two-dimensional wavelet transformation, then is encoded, and the coding is adopted
ECBOT algorithms.
2. the compression method of geographic information image as claimed in claim 1, it is characterised in that the biorthogonal 2-d wavelet is
Biorthogonal 9/7 small echo (CDF97) wave filter.
3. the compression method of geographic information image as claimed in claim 2, it is characterised in that
The low-frequency data conversion includes the data of the geographic information image of a piecemeal are rolled up with biorthogonal 2-d wavelet
Product computing, extracts even item as low-frequency data；
The high-frequency data conversion includes the data of the geographic information image of a piecemeal are rolled up with biorthogonal 2-d wavelet
Product computing, extracts odd term as high-frequency data.
4. the compression method of geographic information image as claimed in claim 3, it is characterised in that the low-frequency data conversion and high
Frequency is respectively mapped to low-frequency data stream control figure and high-frequency data stream control figure according to conversion, in low-frequency data stream control figure and high-frequency data
Concurrency merging is carried out on stream control figure, the parallel processing of low-frequency data conversion and high-frequency data conversion is realized.
5. the compression method of geographic information image as claimed in claim 1, it is characterised in that in described image coding step also
It is smoothed including the border to piecemeal, the smoothing processing includes：
On the border of piecemeal, with the border as axle, the data of axle both sides preset range are mutually mapped, to the number after mapping
According to dispersed elevation value is calculated, the data in the boundaries on either side preset range of piecemeal are adjusted according to dispersed elevation value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611154931.2A CN106558085A (en) | 2016-12-14 | 2016-12-14 | The compression method of geographic information image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611154931.2A CN106558085A (en) | 2016-12-14 | 2016-12-14 | The compression method of geographic information image |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106558085A true CN106558085A (en) | 2017-04-05 |
Family
ID=58445434
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611154931.2A Pending CN106558085A (en) | 2016-12-14 | 2016-12-14 | The compression method of geographic information image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106558085A (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103279963A (en) * | 2013-06-19 | 2013-09-04 | 上海众恒信息产业股份有限公司 | Geographic information image compression method |
-
2016
- 2016-12-14 CN CN201611154931.2A patent/CN106558085A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103279963A (en) * | 2013-06-19 | 2013-09-04 | 上海众恒信息产业股份有限公司 | Geographic information image compression method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Aymaz et al. | A novel image decomposition-based hybrid technique with super-resolution method for multi-focus image fusion | |
| Gilge et al. | Coding of arbitrarily shaped image segments based on a generalized orthogonal transform | |
| Sandić-Stanković et al. | DIBR-synthesized image quality assessment based on morphological multi-scale approach | |
| CN105469353B (en) | The embedding grammar and device and extracting method and device of watermarking images | |
| CN108932699A (en) | Three-dimensional matching reconciliation filtering image denoising method based on transform domain | |
| CN104537624B (en) | SAR image method for reducing speckle based on SSIM correction cluster rarefaction representations | |
| Kaur et al. | Improved homomorphic filtering using fractional derivatives for enhancement of low contrast and non-uniformly illuminated images | |
| Huang et al. | Atrous pyramid transformer with spectral convolution for image inpainting | |
| CN106558084A (en) | Compression method of geographic information image | |
| CN105469358B (en) | A kind of image processing method | |
| Liang et al. | The NAMlet transform: A novel image sparse representation method based on non-symmetry and anti-packing model | |
| Miya et al. | Wavelet techniques for medical images performance analysis and observations with EZW and underwater image processing | |
| Zhou et al. | Efficient Lightweight Image Denoising with Triple Attention Transformer | |
| CN105678718A (en) | Method and device for image denoising | |
| Laksmi et al. | Novel image enhancement technique using CLAHE and wavelet transforms | |
| Xinlei et al. | Super-resolution of remote sensing images via sparse structural manifold embedding | |
| CN106558085A (en) | The compression method of geographic information image | |
| Niu et al. | Edge-based perceptual image coding | |
| Yu et al. | Single image dehazing based on multi-scale segmentation and deep learning | |
| Liu et al. | An image zooming method based on the coupling threshold in the wavelet packet transform domain | |
| Lam et al. | Effects of JPEG compression on the accuracy of digital terrain models automatically derived from digital aerial images | |
| CN106560862A (en) | Compression method based on geographic information image and wavelet | |
| Radhika et al. | Digital image fusion using HVS in block based transforms | |
| CN104143188A (en) | Image quality evaluation method based on multi-scale edge expression | |
| CN109300086B (en) | Image blocking method based on definition |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20170405 |
|
| WD01 | Invention patent application deemed withdrawn after publication |