CN108830909A - Promote the image preprocessing system and method for period texture image compression ratio - Google Patents
Promote the image preprocessing system and method for period texture image compression ratio Download PDFInfo
- Publication number
- CN108830909A CN108830909A CN201810509572.0A CN201810509572A CN108830909A CN 108830909 A CN108830909 A CN 108830909A CN 201810509572 A CN201810509572 A CN 201810509572A CN 108830909 A CN108830909 A CN 108830909A
- Authority
- CN
- China
- Prior art keywords
- image
- texture
- module
- compression
- sampled images
- 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.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T9/00—Image coding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30121—CRT, LCD or plasma display
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Multimedia (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Compression Of Band Width Or Redundancy In Fax (AREA)
- Compression Or Coding Systems Of Tv Signals (AREA)
Abstract
The image preprocessing system of period texture image compression ratio is promoted designed by the present invention, pixel in defects of display panel detection image containing period texture in each Texture-period with same phase is formed a width sampled images, and several the corresponding sampled images of pixel for extracting all outs of phase in each Texture-period in the defects of display panel detection image by its image sampling module；Several above-mentioned sampled images are carried out grid arrangement, the reconstructed image of composition and the above-mentioned same resolution ratio of defects of display panel detection image according to the phase position of every width sampled images itself by grid arrangement module；Image compression module carries out lossy compression to reconstructed image or lossless compression obtains bit stream data.Present invention image compression rate with higher, can facilitate storing and transmitting for texture image.
Description
Technical field
The present invention relates to technical field of image processing, pre- in particular to a kind of image for promoting period texture image compression ratio
Processing system and method.
Technical background
TFT (Thin Film Transistor, the thin film transistor (TFT)) array of liquid crystal display panel carries out automatic optics inspection mistake
It needs to shoot the lossless digital picture that 10~30 resolution ratio are 6576*4384 or 10000*7096 in journey, is examined in above-mentioned optics
Survey process generally uses area array cameras to carry out the shooting of lossless digital picture, then utilizes JPEG-LS (Joint
Photographic Experts Group-lossless) lossless compression the lossless digital picture of shooting compress it is laggard
Row transimission and storage, however since the high resolution of area array cameras is in detected liquid crystal display panel, which results in area array cameras
The image of shooting contains strong period texture, and in the JPEG-LS loss-free compression process of image, these textures will be greatly reduced compression
Than to increase the requirement of memory capacity and transmission bandwidth.
Summary of the invention
Present invention aim to provide it is a kind of promoted period texture image compression ratio image preprocessing system and side
Method, system and method image compression rate with higher, can facilitate storing and transmitting for texture image.
In order to achieve this, a kind of image preprocessing system of promotion period texture image compression ratio designed by the present invention
System, it is characterised in that：It includes image sampling module, grid arrangement module, image compression module, wherein described image sampling
The picture that module is used to have same phase in the defects of display panel detection image containing period texture in each Texture-period
Element one width sampled images of composition, and extract in the defects of display panel detection image all outs of phase in each Texture-period
Several corresponding sampled images of pixel；
The grid arrangement module is used for several above-mentioned sampled images according to the phase position of every width sampled images itself
Carry out grid arrangement, the reconstructed image of composition and the above-mentioned same resolution ratio of defects of display panel detection image；
Described image compression module is used to carry out lossy compression to reconstructed image or lossless compression obtains bit stream data.
A kind of corresponding decompression system of above-mentioned image preprocessing system, it includes data acquisition module, decompression module, resolution
Rate computing module, image block module and image restoring module；The data acquisition module for obtain the bit stream data with
And image texture parameter and image resolution ratio parameter in reconstructed image；
The decompression module is for damaging decompression to bit stream data progress is corresponding with above-mentioned lossy compression or lossless compression
Or lossless decompression obtains pre- decompressed image；
The parametric texture and image resolution ratio parameter that resolution ratio computing module is used in the reconstructed image that basis obtains utilize
Parameter initialization method calculates the sampled images resolution ratio of each phase；
Image block module is used to using the sampled images resolution ratio of each phase be the resolution ratio of image block to pre- decompression figure
Several sampled images are obtained as carrying out piecemeal；
Image restoring module is used for by the pixel value of several sampled images, according to the phase of every width sampled images in display surface
The phase sequence of period texture in board defect detection image is arranged successively to obtain final decompressed image.
A kind of image pre-processing method promoting period texture image compression ratio, it includes the following steps：
Step 1：Image sampling module is by each Texture-period in the defects of display panel detection image containing period texture
The interior pixel with same phase forms a width sampled images, and extracts each texture in the defects of display panel detection image
Several corresponding sampled images of the pixel of all outs of phase in period；
Step 2：Grid arrangement module by several above-mentioned sampled images according to every width sampled images itself phase position into
Row grid arrangement, the reconstructed image of composition and the above-mentioned same resolution ratio of defects of display panel detection image；
Step 3：Described image compression module carries out lossy compression to reconstructed image or lossless compression obtains bit stream data.
A kind of decompressing method of above-mentioned image pre-processing method, it includes the following steps：
Step 01：Data acquisition module obtains image texture parameter and image in the bit stream data and reconstructed image
Resolution parameter；
Step 02：The decompression module is corresponding with above-mentioned lossy compression or lossless compression to bit stream data progress to damage solution
Pressure or lossless decompression obtain pre- decompressed image；
Resolution ratio computing module according in obtained reconstructed image parametric texture and image resolution ratio parameter utilize parameter
Initial method calculates the sampled images resolution ratio of each phase；
Step 03：Image block module is the resolution ratio of image block to pre- decompression using the sampled images resolution ratio of each phase
Image carries out piecemeal and obtains several sampled images；
Step 04：Image restoring module is by the pixel value of several sampled images, according to the phase of every width sampled images aobvious
The phase sequence for showing period texture in panel defect detection image is arranged successively to obtain final decompressed image.
The systems and methods that the present invention designs are by the way of two dimensional image homography coordinate transform, this transformation side
Formula is reversible, and texture decomposition is carried out by way of two dimensional image homography coordinate transform can be significantly reduced strong period texture
High frequency response in image spectrum, that is, keep image smoothened, to be easier to be compressed, this allows the present invention not
In the case where modifying compression algorithm, the compression ratio of period texture image is promoted, to reduce the display panel containing period texture
The memory capacity and transmission bandwidth that defects detection image is occupied when storing and transmitting.
In addition, the decompression system and decompressing method opposite with image pre-processing method has also been devised in the present invention, it uses two
Dimension image homography coordinate inversion is accurately decompressed above-mentioned compressed file, obtains accurately containing period texture
Defects of display panel detection image.
Detailed description of the invention
Fig. 1 is image preprocessing system structure diagram of the invention；
Fig. 2 is decompression system structural schematic diagram of the invention；
Wherein, 1-image sampling module, 2-grid arrangement modules, 3-image compression modules, the acquisition output of 4-data
Module, 5-data acquisition modules, 6-decompression modules, 7-resolution ratio computing modules, 8-image block modules, 9-images are also
Former module.
Specific embodiment
Below in conjunction with the drawings and specific embodiments, the present invention is described in further detail：
The present invention be suitable for liquid crystal display panel tft array carry out the shooting of automatic optics inspection process Texture-period image into
Row compress backup link, the image preprocessing system for the promotion period texture image compression ratio that the present invention designs, it includes image
Decimation blocks 1, grid arrangement module 2, image compression module 3 and data acquire output module 4, wherein image sampling module 1
Data output end connects the data input pin of grid arrangement module 2, and the data output end of grid arrangement module 2 connects compression of images
The data input pin of module 3, the data input pin of the data output end connection data acquisition output module 4 of image compression module 3,
The data output end of data acquisition output module 4 is for connecting memory or data transmission network；
Described image decimation blocks 1 were used for texture week each in the defects of display panel detection image containing period texture
Pixel in phase with same phase forms a width sampled images, and extracts each line in the defects of display panel detection image
Manage several corresponding sampled images of pixel of all outs of phase in the period；
The grid arrangement module 2 is used for several above-mentioned sampled images according to the phase position of every width sampled images itself
Grid arrangement is carried out, the reconstructed image of a width and the above-mentioned same resolution ratio of defects of display panel detection image is formed；
Described image compression module 3 is used to carry out lossy compression to reconstructed image or lossless compression obtains bit stream data.
Data acquisition output module 4 be used for by the bit stream data and reconstructed image image texture parameter and
Image resolution ratio parameter is acquired output.
The period texture contained in above-mentioned defects of display panel detection image, the horizontal cycle and vertical cycle of texture exist
Between 3~20 pixels, and texture has biggish contrast, i.e. most bright pixel value and most dark pixel value differs by more than 30.
In above-mentioned technical proposal, lossy compression includes that jpeg damages image compression and jpeg2000 damages image coding
Compression.
In above-mentioned technical proposal, the lossless compression includes the image lossless based on static and dynamic Huffman encoding algorithm
Compression, the Lossless Image Compression Algorithm (JPEG-LS lossless compression) based on Arithmetic Coding algorithm are compiled based on LZW (string list compression algorithm)
The Lossless Image Compression Algorithm of code and its innovatory algorithm, the image lossless pressure based on run-length encoding and improvement adaptive run-length encryption algorithm
Contracting, the Lossless Image Compression Algorithm based on Fano Shannon encryption algorithm and the coding compression of jpeg2000 lossless image.
A kind of corresponding decompression system of above system, it includes that data acquisition module 5, decompression module 6, resolution ratio calculate mould
Block 7, image block module 8 and image restoring module 9, the data of the data output end connection decompression module 6 of data acquisition module 5
Input terminal, the data input pin of the data output end connection resolution ratio computing module 7 of decompression module 6, resolution ratio computing module 7
Data output end connects the data input pin of image block module 8, and the data output end of image block module 8 connects image restoring
The data input pin of module 9；
The data acquisition module 5 is used to obtain image texture parameter and the figure in the bit stream data and reconstructed image
As resolution parameter；
The decompression module 6 is for damaging decompression to bit stream data progress is corresponding with above-mentioned lossy compression or lossless compression
Or lossless decompression obtains pre- decompressed image；
The parametric texture and image resolution ratio parameter that resolution ratio computing module 7 is used in the reconstructed image that basis obtains utilize
Parameter initialization method calculates the sampled images resolution ratio of each phase；
Image block module 8 is used to using the sampled images resolution ratio of each phase be the resolution ratio of image block to pre- decompression figure
Several sampled images are obtained as carrying out piecemeal；
Image restoring module 9 is used to show the pixel value of several sampled images according to the phase of every width sampled images
The phase sequence of period texture in panel defect detection image is arranged successively to obtain final decompressed image.
In above-mentioned technical proposal, it is described damage decompression include jpeg damage image coding decompression and jpeg2000 damage image
Coding decompression.
The lossless decompression includes compiling based on static and dynamic Huffman encoding algorithm image lossless decompression, based on arithmetic
The image lossless decompression of code algorithm, is based on run-length encoding and improvement at the image lossless decompression based on LZW coding and its innovatory algorithm
The image lossless decompression of adaptive run-length encryption algorithm, the image lossless decompression based on Fano Shannon encryption algorithm and jpeg2000
Lossless image coding decompression.
A kind of image pre-processing method promoting period texture image compression ratio, which is characterized in that it includes the following steps：
Step 1：Image sampling module 1 is by each Texture-period in the defects of display panel detection image containing period texture
The interior pixel with same phase forms a width sampled images, and extracts each texture in the defects of display panel detection image
Several corresponding sampled images of the pixel of all outs of phase in period；
Step 2：Grid arrangement module 2 by several above-mentioned sampled images according to every width sampled images itself phase position into
Row grid arrangement, the reconstructed image of composition and the above-mentioned same resolution ratio of defects of display panel detection image；
Step 3：Described image compression module 3 carries out lossy compression to reconstructed image or lossless compression obtains bit stream data.
Data acquire output module 4 for the image texture parameter and image resolution in the bit stream data and reconstructed image
Rate parameter is acquired output.
In the step 1 of above-mentioned technical proposal, the defects of display panel detection image containing period texture is expressed as I (x, y)
Wherein, defects detection image level coordinate x=0,1,2 ... ..., W-1；Defects detection image vertical coordinate y=0,1,2 ... ...,
H-1；W is the horizontal resolution of image, and H is the vertical resolution of image；
Several corresponding sampling of the pixel of all outs of phase in each Texture-period in defects of display panel detection image
Image is expressed as DI, j(x1, y1)=I (x1*N+i, y1*M+j) wherein, sampled images horizontal coordinate x1=0,1,2 ... ..., A-
1；Sampled images vertical coordinate y1=0,1,2 ... ..., B-1, wherein N is the texture level period, and M is texture vertical cycle, water
Square to texture number A=W/N, vertical direction texture number B=H/M, horizontal phase i=0,1,2 ... ..., N-1；Vertical phase
Position j=0,1,2 ..., M-1；Unit is pixel；
In step 2, reconstructed image F is by above-mentioned matrix DI, jIt constitutes
A kind of decompressing method of the above method, it includes the following steps：
Step 01：Data acquisition module 5 obtains image texture parameter and figure in the bit stream data and reconstructed image
As resolution parameter；
Step 02：The decompression module 6 carries out damage corresponding with above-mentioned lossy compression or lossless compression to bit stream data
Decompression or lossless decompression obtain pre- decompressed image；
Resolution ratio computing module 7 according in obtained reconstructed image parametric texture and image resolution ratio parameter utilize parameter
Initial method calculate each phase sampled images resolution ratio (set horizontal resolution as W, vertical resolution H, texture level
Period is N, vertical cycle M, then horizontal direction texture number A=W/N, vertical direction texture number B=H/M, above-mentioned line
Reason number of cycles is equivalent to the sampled images resolution ratio of each phase)；
Step 03：Image block module 8 is the resolution ratio of image block to pre- solution using the sampled images resolution ratio of each phase
Pressure image carries out piecemeal and obtains several sampled images；
Step 04：Image restoring module 9 is by the pixel value of several sampled images, according to the phase of every width sampled images aobvious
The phase sequence for showing period texture in panel defect detection image is arranged successively to obtain final decompressed image.
In above-mentioned steps 02, pre- decompressed image I1 (x2, y2) wherein, pre- decompressed image horizontal coordinate x2=0,1,
2 ... ..., W1-1；Pre- decompressed image vertical coordinate y2=0,1,2 ... ..., H1-1, W1 are pre- decompressed image horizontal resolution, H1
For pre- decompressed image vertical resolution；
Several sampled images EI, j(x3, y3)=I1 (i1*A1+x3, j1*B1+y3), wherein sampled images horizontal coordinate x3
=0,1,2 ... ..., A1-1；Sampled images vertical coordinate y3=0,1,2 ... ..., B1-1；Sampled images horizontal phase i1=0,
1,2,……,N1-1；Sampled images vertical phase j1=0,1,2 ..., M1-1；Width sampled images horizontal direction texture number
A1=W1/N1, width sampled images width sampled images vertical direction texture number B1=H1/M1.
Decompressed image F1 is by several sampled images EI, j(x3, y3) is arranged alternately to obtain according to its phase：
The content that this specification is not described in detail belongs to the prior art well known to professional and technical personnel in the field.
Claims (10)
1. a kind of image preprocessing system for promoting period texture image compression ratio, it is characterised in that：It includes image sampling mould
Block (1), grid arrangement module (2), image compression module (3), wherein described image decimation blocks (1) will be for that will contain the period
Pixel in the defects of display panel detection image of texture in each Texture-period with same phase forms a width sampled images,
And several corresponding pumpings of pixel for extracting all outs of phase in each Texture-period in the defects of display panel detection image
Sampled images；
The grid arrangement module (2) be used for by several above-mentioned sampled images according to every width sampled images itself phase position into
Row grid arrangement, the reconstructed image of composition and the above-mentioned same resolution ratio of defects of display panel detection image；
Described image compression module (3) is used to carry out lossy compression to reconstructed image or lossless compression obtains bit stream data.
2. the image preprocessing system according to claim 1 for promoting period texture image compression ratio, it is characterised in that：It
It further include data acquisition output module (4), data acquisition output module (4) is used for the bit stream data and reconstruct image
Image texture parameter and image resolution ratio parameter as in are acquired output.
3. the image preprocessing system according to claim 1 for promoting period texture image compression ratio, it is characterised in that：Institute
Stating lossy compression includes that jpeg damages image compression and jpeg2000 damages image compression.
4. the image preprocessing system according to claim 1 for promoting period texture image compression ratio, it is characterised in that：Institute
Stating lossless compression includes based on static and dynamic Huffman encoding algorithm Lossless Image Compression Algorithm, based on the figure of Arithmetic Coding algorithm
As lossless compression, the Lossless Image Compression Algorithm based on LZW coding and its innovatory algorithm, based on run-length encoding and improvement adaptive run-length
The Lossless Image Compression Algorithm of encryption algorithm, the Lossless Image Compression Algorithm based on Fano Shannon encryption algorithm and jpeg2000 lossless image are compiled
Code compression.
5. a kind of corresponding decompression system of system described in claim 2, which is characterized in that it includes data acquisition module (5), solution
Die block (6), resolution ratio computing module (7), image block module (8) and image restoring module (9)；The data acquisition module
(5) for obtaining image texture parameter and image resolution ratio parameter in the bit stream data and reconstructed image；
The decompression module (6) be used for bit stream data carry out it is corresponding with above-mentioned lossy compression or lossless compression damage decompress or
Lossless decompression obtains pre- decompressed image；
The parametric texture and image resolution ratio parameter that resolution ratio computing module (7) is used in the reconstructed image that basis obtains utilize ginseng
Number initial method calculates the sampled images resolution ratio of each phase；
Image block module (8) is used to using the sampled images resolution ratio of each phase be the resolution ratio of image block to pre- decompressed image
It carries out piecemeal and obtains several sampled images；
Image restoring module (9) is used for by the pixel value of several sampled images, according to the phase of every width sampled images in display surface
The phase sequence of period texture in board defect detection image is arranged successively to obtain final decompressed image.
6. decompression system according to claim 5, it is characterised in that：The decompression that damages includes that jpeg damages image coding
Decompression and jpeg2000 damage image coding decompression.
7. decompression system according to claim 5, it is characterised in that：The lossless decompression includes being based on static and dynamic suddenly
The image lossless decompression of the graceful encryption algorithm of husband, the image lossless decompression based on Arithmetic Coding algorithm are encoded and its are improved based on LZW
The image lossless decompression of algorithm, is based on taking the image lossless decompression based on run-length encoding and improvement adaptive run-length encryption algorithm
The image lossless decompression and jpeg2000 lossless image coding decompression of promise Shannon encryption algorithm.
8. a kind of image pre-processing method for promoting period texture image compression ratio, which is characterized in that it includes the following steps：
Step 1：Image sampling module (1) will be in the defects of display panel detection image containing period texture in each Texture-period
Pixel with same phase forms a width sampled images, and extracts each texture week in the defects of display panel detection image
Several corresponding sampled images of the pixel of all outs of phase in phase；
Step 2：Grid arrangement module (2) carries out several above-mentioned sampled images according to the phase position of every width sampled images itself
Grid arrangement, the reconstructed image of composition and the above-mentioned same resolution ratio of defects of display panel detection image；
Step 3：Described image compression module (3) carries out lossy compression to reconstructed image or lossless compression obtains bit stream data.
9. image pre-processing method according to claim 8, it is characterised in that；It further includes step 4：Data acquisition output
Module (4) by the bit stream data and reconstructed image image texture parameter and image resolution ratio parameter be acquired it is defeated
Out.
10. a kind of decompressing method of claim 8 the method, which is characterized in that it includes the following steps：
Step 01：Data acquisition module (5) obtains the bit stream data and image texture parameter and image in reconstructed image
Resolution parameter；
Step 02：The decompression module (6) is corresponding with above-mentioned lossy compression or lossless compression to bit stream data progress to damage solution
Pressure or lossless decompression obtain pre- decompressed image；
Resolution ratio computing module (7) according in obtained reconstructed image parametric texture and image resolution ratio parameter using at the beginning of parameter
Beginning method calculates the sampled images resolution ratio of each phase；
Step 03：Image block module (8) is the resolution ratio of image block to pre- decompression using the sampled images resolution ratio of each phase
Image carries out piecemeal and obtains several sampled images；
Step 04：Image restoring module (9) is showing the pixel value of several sampled images according to the phase of every width sampled images
The phase sequence of period texture in panel defect detection image is arranged successively to obtain final decompressed image.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810509572.0A CN108830909B (en) | 2018-05-24 | 2018-05-24 | Image preprocessing system and method for improving compression ratio of periodic texture image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810509572.0A CN108830909B (en) | 2018-05-24 | 2018-05-24 | Image preprocessing system and method for improving compression ratio of periodic texture image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108830909A true CN108830909A (en) | 2018-11-16 |
| CN108830909B CN108830909B (en) | 2022-08-19 |
Family
ID=64145332
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810509572.0A Active CN108830909B (en) | 2018-05-24 | 2018-05-24 | Image preprocessing system and method for improving compression ratio of periodic texture image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108830909B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111325707A (en) * | 2018-12-13 | 2020-06-23 | 深圳中科飞测科技有限公司 | Image processing method and system, and detection method and system |
| CN115633157A (en) * | 2022-12-23 | 2023-01-20 | 合肥惠强新能源材料科技有限公司 | Remote monitoring method and system for lithium battery diaphragm processing |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1592363A (en) * | 2003-08-06 | 2005-03-09 | 索尼株式会社 | Image processing apparatus, image processing system, imaging apparatus and image processing method |
| CN101447084A (en) * | 2008-12-19 | 2009-06-03 | 北京师范大学 | Compressing encode/decode method for three-dimensional model texture based on Quardtree |
| WO2012101582A1 (en) * | 2011-01-24 | 2012-08-02 | Invision Biometrics Ltd. | Method and system for acquisition, representation, compression, and transmission of three-dimensional data |
| CN104063873A (en) * | 2014-07-08 | 2014-09-24 | 华东交通大学 | Shaft sleeve part surface defect on-line detection method based on compressed sensing |
| CN104869425A (en) * | 2015-05-13 | 2015-08-26 | 信阳师范学院 | Compression and decompression method based on texture image similarity |
| CN107515227A (en) * | 2017-07-27 | 2017-12-26 | 武汉精测电子技术股份有限公司 | Based on the lossless method for compressing image of defect and defects of display panel detecting system |
| CN107993204A (en) * | 2017-11-28 | 2018-05-04 | 重庆大学 | A kind of MRI image reconstructing method based on image block enhancing rarefaction representation |
-
2018
- 2018-05-24 CN CN201810509572.0A patent/CN108830909B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1592363A (en) * | 2003-08-06 | 2005-03-09 | 索尼株式会社 | Image processing apparatus, image processing system, imaging apparatus and image processing method |
| CN101447084A (en) * | 2008-12-19 | 2009-06-03 | 北京师范大学 | Compressing encode/decode method for three-dimensional model texture based on Quardtree |
| WO2012101582A1 (en) * | 2011-01-24 | 2012-08-02 | Invision Biometrics Ltd. | Method and system for acquisition, representation, compression, and transmission of three-dimensional data |
| CN104063873A (en) * | 2014-07-08 | 2014-09-24 | 华东交通大学 | Shaft sleeve part surface defect on-line detection method based on compressed sensing |
| CN104869425A (en) * | 2015-05-13 | 2015-08-26 | 信阳师范学院 | Compression and decompression method based on texture image similarity |
| CN107515227A (en) * | 2017-07-27 | 2017-12-26 | 武汉精测电子技术股份有限公司 | Based on the lossless method for compressing image of defect and defects of display panel detecting system |
| CN107993204A (en) * | 2017-11-28 | 2018-05-04 | 重庆大学 | A kind of MRI image reconstructing method based on image block enhancing rarefaction representation |
Non-Patent Citations (2)
| Title |
|---|
| 周佳男: ""基于相位场模型的纹理图像分割方法研究"", 《中国优秀硕士学位论文集 信息科技辑》 * |
| 朴燕等: ""利用匹配区域的纹理特征改善重构三维图像的视觉质量"", 《光学学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111325707A (en) * | 2018-12-13 | 2020-06-23 | 深圳中科飞测科技有限公司 | Image processing method and system, and detection method and system |
| CN115633157A (en) * | 2022-12-23 | 2023-01-20 | 合肥惠强新能源材料科技有限公司 | Remote monitoring method and system for lithium battery diaphragm processing |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108830909B (en) | 2022-08-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110348487B (en) | Hyperspectral image compression method and device based on deep learning | |
| CN106960416B (en) | A kind of video satellite that content complexity is adaptive compression image super-resolution method | |
| Wiseman | The still image lossy compression standard-JPEG | |
| CN109889846B (en) | Method and device for compressing and decompressing Demura table data and coding and decoding system | |
| US10440360B2 (en) | Video processing system | |
| CN101494788B (en) | Method and apparatus for compressing and decompressing video image | |
| Zhao et al. | Image compressive-sensing recovery using structured laplacian sparsity in DCT domain and multi-hypothesis prediction | |
| US20150312503A1 (en) | Imaging system, imaging apparatus, and imaging method | |
| CN108830909A (en) | Promote the image preprocessing system and method for period texture image compression ratio | |
| CN103957426A (en) | RGB565 true color image lossy compression and decompression method | |
| CN110047038B (en) | Single-image super-resolution reconstruction method based on hierarchical progressive network | |
| Bacchus et al. | Quasi lossless satellite image compression | |
| CN104683818A (en) | Image compression method based on biorthogonal invariant set multi-wavelets | |
| Turcza | Entropy encoder for low-power low-resources high-quality CFA image compression | |
| Lukin et al. | Automatic lossy compression of noisy images by spiht or jpeg2000 in optimal operation point neighborhood | |
| Takeyama et al. | Joint mixed-noise removal and compressed sensing reconstruction of hyperspectral images via convex optimization | |
| CN101720043B (en) | Imaging spectrum image compression method based on multi-mode prediction | |
| US9819969B2 (en) | Generalization of methods and systems for image compression while encoding at least one extra bit | |
| Yan et al. | Compressive sampling for array cameras | |
| Awad et al. | Improving Reconstructed Image Quality via Hybrid Compression Techniques. | |
| CN111107360B (en) | Spectrum-space dimension combined hyperspectral image lossless compression method and system | |
| CN113141506A (en) | Deep learning-based image compression neural network model, and method and device thereof | |
| Li et al. | Non-blind post-processing algorithm for remote sensing image compression | |
| Sendamarai et al. | FPGA implementation of combined compression & denoising scheme for remote sensing images | |
| CN106296754B (en) | Show data compression method and display data processing system |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |