CN101110050A - Picture processing chip for self-adapting automatic dead point detection and method thereof - Google Patents
Picture processing chip for self-adapting automatic dead point detection and method thereof Download PDFInfo
- Publication number
- CN101110050A CN101110050A CNA2007101202176A CN200710120217A CN101110050A CN 101110050 A CN101110050 A CN 101110050A CN A2007101202176 A CNA2007101202176 A CN A2007101202176A CN 200710120217 A CN200710120217 A CN 200710120217A CN 101110050 A CN101110050 A CN 101110050A
- Authority
- CN
- China
- Prior art keywords
- bad point
- memory
- data
- confidence level
- statistics number
- 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
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000001514 detection method Methods 0.000 title claims description 29
- 230000015654 memory Effects 0.000 claims description 138
- 238000012217 deletion Methods 0.000 claims description 20
- 230000037430 deletion Effects 0.000 claims description 20
- 230000005611 electricity Effects 0.000 claims description 16
- 238000012360 testing method Methods 0.000 claims description 16
- 230000010076 replication Effects 0.000 claims description 4
- FGUUSXIOTUKUDN-IBGZPJMESA-N C1(=CC=CC=C1)N1C2=C(NC([C@H](C1)NC=1OC(=NN=1)C1=CC=CC=C1)=O)C=CC=C2 Chemical compound C1(=CC=CC=C1)N1C2=C(NC([C@H](C1)NC=1OC(=NN=1)C1=CC=CC=C1)=O)C=CC=C2 FGUUSXIOTUKUDN-IBGZPJMESA-N 0.000 claims description 2
- 238000003860 storage Methods 0.000 abstract description 8
- 230000008439 repair process Effects 0.000 description 4
- 230000006978 adaptation Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000014759 maintenance of location Effects 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000007792 addition Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000012913 prioritisation Methods 0.000 description 1
Images
Landscapes
- Techniques For Improving Reliability Of Storages (AREA)
Abstract
The utility model discloses a self-adapting image processing chip to automatically examine dead pixel and the method thereof. The image processing chip comprises a dead pixel examination module, a control module and the storage. The method thereof comprises: a. power on the image processing chip; b. examine the dead pixel at each frame; c. compare the examination results with the dead pixel data in the No.1 storage; d. As per the comparison results, add, modify and delete the related dead pixel data in the No. 1 storage. If the examination continues, return to step b. The utility model can realize automatic dead pixel examination without any software and can add or delete the dead pixel data in the process of examination, thus ensuring timely update of the dead pixel data and realizing the self-adapting and automatic dead pixel examination. The optimal scheme of the utility model can also automatically remove the dead pixel.
Description
Technical field
The present invention relates to picture processing chip, relate in particular to a kind of picture processing chip and method of self-adapting automatic dead point detection.
Background technology
Therefore in today of Multi-media information prosperity, the purposes of picture processing chip is more and more extensive, but in present picture processing chip, can't avoid the existence of bad point, all needs to detect position---the bad point coordinate of bad point when dispatching from the factory and when using.
Present picture processing chip must rely on special software to go bad a little and detect, and when Streaming Media one is opened, software just otherwise stop detecting so can take a large amount of resources, influences overall performance.In addition, bad point data is can not be in testing process adaptive carries out additions and deletions, and it is untimely to cause bad point data to upgrade.
Summary of the invention
For above deficiency, the technical problem to be solved in the present invention provides a kind of picture processing chip and method of self-adapting automatic dead point detection, changes in real time the bad point data according to testing result, and relies on hardware to realize whole detection.
In order to solve the problems of the technologies described above, the invention provides a kind of picture processing chip of self-adapting automatic dead point detection, comprise that bad point detects module; It is characterized in that, also comprise control module and storer;
Described bad point detection module detects a bad point to each frame after being used for opening stream, and testing result is reported control module;
Described storer comprises first memory, is used for bad point data of dynamic memory;
Described control module is used for reading the bad point data of first memory, and compares with described testing result, increases, revises and delete corresponding bad point data according to the result who compares in first memory.
The bad point data of further, storing in the described first memory comprise: bad point coordinate and bad point attribute; Described bad point attribute comprises confidence level and the statistics number of automatic detected bad point.
Further, described control module also is used for preserving confidence level maximum, statistics number maximum, statistical threshold and deletion threshold value;
Described control module increases, revises and deletes corresponding bad point data according to the result who compares and specifically is meant in first memory:
If the data of detected bad point have existed and be automatic detected bad point, as long as its confidence level and statistics number do not reach maximal value, then confidence level and the statistics number with this evil idea point in the first memory respectively adds 1; If confidence level and/or statistics number reach maximal value, then reach peaked and do not add 1;
If the data of detected bad point do not exist; Then add the data of this bad point in first memory, attribute is made as detected bad point automatically, and its confidence level and statistics number are 1;
If exist the bad point of bad point data not detect in the first memory; Then the confidence level with this bad point in the first memory subtracts 1, and statistics number adds 1;
If confidence level is less than the deletion threshold value greater than statistical threshold for the statistics number of a bad point, perhaps the ratio of confidence level and statistics number is then deleted the data of this bad point in the first memory less than the deletion threshold value.
Further, described picture processing chip also comprises the bad point cancellation module, and described memory also comprises second memory;
Described bad point cancellation module is used for carrying out the bad point reparation according to the bad point data of second memory;
Described control module also be used for to preserve the update cycle and when new update cycle of one of every arrival, the bad point data with in the first memory copy in the second memory.
Further, described storer also comprises the 3rd storer;
Described the 3rd memory is the not memory of obliterated data of lower electricity, the bad point data when being used for preserving front principal;
Described control module also is used for when picture processing chip powers on, with the bad point data Replica preserved in the 3rd memory in first memory and second memory; And under picture processing chip before the electricity, in bad point data Replica to the three memories in the first memory.
The present invention also provide a kind of in the described picture processing chip of claim 1 method of self-adapting automatic dead point detection, comprising:
A, picture processing chip power on;
When b, frame arrival, carry out bad point detection one time;
C, bad point data in testing result and the first memory are compared;
D, according to the comparison the result in first memory, increase, revise and delete corresponding bad point data; If continuation detects then returns step b.
Further, described bad point data comprise: bad point coordinate and bad point attribute; Described bad point attribute comprises confidence level and the statistics number of automatic detected bad point.
Further, described steps d specifically comprises:
If the data of detected bad point have existed and for dispatching from the factory bad point, have not then handled;
If the data of detected bad point have existed and for automatic detected bad point, as long as its confidence level and statistics number do not reach default maximal value, then in first memory its confidence level and statistics number are respectively added 1; If confidence level and/or statistics number reach maximal value, then reach peaked and do not add 1;
If the data of detected bad point do not exist, then in first memory, add the data of this bad point, confidence level and statistics number are 1;
If in first memory, exist the bad point of data not detect, then in first memory, the confidence level of this bad point is subtracted 1, statistics number adds 1;
If confidence level is less than the deletion threshold value greater than statistical threshold for the statistics number of a bad point, perhaps the ratio of confidence level and statistics number is then deleted these bad point data less than the deletion threshold value in first memory.
Further, described method also comprises the step e that walks abreast to d with step b: according to the bad point data in the second memory each bad point is repaired.
Further, if described step b arrives the next update cycle in arbitrary step in the e, then carry out step f: the bad point data in the first memory is copied to second memory; Return step b and step e then.
Further, if described step b in the f in arbitrary step picture processing chip to descend electricity, then with rear lower electricity in three memories of the content replication to the in the first memory.
Further, also comprise among the described step a: the bad point data in the 3rd memory are copied to respectively in first memory and the second memory.
Technical scheme of the present invention can just realize that without the intervention of software bad point detects automatically, and can survey the limit at frontier inspection the bad point data are increased, delete, and a data of giving a piece of bad advice can upgrade in time, have realized the self adaptation of bad point is detected automatically.Prioritization scheme of the present invention can also carry out bad point automatically to be eliminated.
Description of drawings
Fig. 1 is the concrete enforcement block diagram of the picture processing chip of self-adapting automatic dead point detection of the present invention;
The concrete true process flow diagram of the method for Fig. 2 self-adapting automatic dead point detection of the present invention.
Embodiment
Below in conjunction with drawings and Examples technical scheme of the present invention is described in detail.
The invention provides a kind of picture processing chip of self-adapting automatic dead point detection, as shown in Figure 1, comprise that storer, control module 4, bad point detect module 5.
Described memory comprises first memory 1 at least, and described first memory 1 is readable and writable memory, is used for dynamic memory bad point data, and described bad point data comprise bad point coordinate and bad point attribute; Described bad point coordinate can be the form of (X, Y) or (Y, X); Comprise bad point source at described bad point attribute, be used for indicating this bad point and be the automatic detected bad point bad point of still dispatching from the factory; The data of automatic detected bad point are called dynamic bad point data, and the bad point attribute of this part bad point data also comprises confidence level and the statistics number of this bad point.
Described bad point detection module 5 detects a bad point to each frame after being used for opening stream, and with testing result---and the coordinate of the bad point that namely detects reports control module 4.
Described control module 4 is used for judging according to the bad point data of the testing result of bad point detection module 5 and first memory 1 storage, increases, revise and delete corresponding bad point data in described first memory 1.Specifically, described control module 4 is used for preserving default confidence level maximum, statistics number maximum, statistical threshold and deletion threshold value; Be used for receiving the bad point coordinate that bad point detection module 5 reports, and from first memory 1, read the bad point coordinate in the bad point data of storing, then two parts of bad point coordinates are compared, handle accordingly according to comparison result, comprise following situation:
If the data of detected bad point have existed and for dispatching from the factory bad point; Then do not handle.
If the data of detected bad point have existed and be automatic detected bad point, as long as the confidence level and the statistics number of this evil idea point do not reach maximal value, then confidence level and the statistics number with this evil idea point in the first memory 1 respectively adds 1; If confidence level and/or statistics number reach maximal value, then reach peaked and do not add 1.
If the data of detected bad point do not exist; Then in 1 li data of adding this bad point of first memory, attribute is made as automatic detected bad point, and its confidence level and statistics number are 1; This is the one side content that can realize self-adapting detecting---can increase the bad point data according to actual conditions.
If exist the bad point of bad point data not detect in the storer; Then the confidence level with this evil idea point in the first memory 1 subtracts 1, and statistics number adds 1.
If confidence level is less than the deletion threshold value greater than statistical threshold for the statistics number of a bad point, perhaps the ratio of confidence level and statistics number is less than the deletion threshold value, then from 1 li data of deleting this bad point of first memory; Promptly after certain statistics number, when confidence level is still low, can think that this evil idea point has been repaired or be bad point by the flase drop survey before, so can from bad point data, delete that this is the content on the other hand that can realize self-adapting detecting---can delete bad point data according to actual conditions.Described statistical threshold and deletion threshold value are set as required in advance.
To carry out confidence level statistics be not in order to prevent because erroneous judgement and the bad point data deletion of will dispatching from the factory to the bad point of dispatching from the factory; Therefore the automatic bad point detection of described self adaptation also is for automatic detected bad point.Also can not distinguish dispatch from the factory bad point and automatic detected bad some during practical application, all handle, promptly in the bad some attribute of all bad point data, not comprise badly some source, and all comprise confidence level and statistics number according to the mode of automatic detected bad point.
Described picture processing chip also comprises bad point cancellation module 6.
Described storer also comprises second memory 2, and described second memory 2 is a readable and writable memory, and being used for provides bad point data to bad some cancellation module 6
Described bad some cancellation module 6 is used for going bad some reparation according to the bad point data of second memory 2, promptly finds corresponding bad point according to the coordinate in the bad point data, and repairs.
Described control module 4 also is used for providing clock signal and address signal to first memory 1 and second memory 2.Also be used for to preserve the update cycle---i.e. the statistics number (being frame number) at twice renewal bad point data institute interval, and when new update cycle of one of every arrival, the bad point data with in the first memory 1 copy in the second memory 2.
The purpose of upgrading the bad point data is to make the bad point cancellation module can go to repair emerging bad point, and can no longer lose time to repair the bad point of having repaired.
A kind of distortion of the present invention is, adopts two memory bank MEM1, MEM2, and one of them is earlier as first memory 1, and another is as second memory 2, and in testing process, each update cycle once exchanges then; This moment, described picture processing chip also comprised a selector; Described selector comprises two inputs, links to each other with MEM2 with MEM1 respectively; An output links to each other with the bad point cancellation module; A control end links to each other with control module; Be used for selecting one the tunnel to export according to the indication of control module 4 in the bad point data of two-way input.Described control module 4 also is used to indicate described selector switch to select which input data to export, and after duplicating when upgrading bad point data finish, and indicates described selector switch to reelect to select another road input data to export; Control module described in the testing process 4 is that the bad point data in the storer of unselected that road input of selector switch is read and write.When just having powered on such as chip, control module 4 indication selector switchs select the bad point data in the MEM1 to export, then in the testing process, the bad point data that control module 4 reads among the MEM2 is come and the testing result comparison, and the bad point data among the MEM2 is carried out respective handling according to comparison result, in this update cycle, MEM1 is as second memory 2, and MEM2 is as first memory 1; When arriving to the next update cycle, control module 4 indication selector switchs are reelected the bad point data of selecting in the MEM2 and are exported, at this moment, what control module 4 read and handled has been exactly the bad point data in the MEM1, so in this update cycle, MEM2 is as second memory 2, and MEM1 is as first memory 1; When next update cycle arrives again, indicate selector switch to select the bad point data in the MEM1 to export again ... by that analogy, be equivalent to the once purposes of two memory banks of each update cycle exchange.
Described storer can also comprise the 3rd storer 3.
Described the 3rd storer 3 is the storer of obliterated data not of electricity down, can but be not limited to EEPROM (EEPROM (Electrically Erasable Programmable Read Only Memo)), be used for preserving the bad point data of dispatching from the factory, and before dynamic bad point data during principal.
Described control module 4 also is used for when picture processing chip powers on, the bad point data of preserving in the 3rd storer 3 is copied in first memory 1 and the second memory 2, and under picture processing chip before the electricity, the bad point data or the dynamic bad point data of first memory 1 copied in the 3rd storer 3.
When practical application, first memory 1 and second memory 2 can be two physical storages, also can be two zones of same physical storage, but the latter's realization logic is complicated, because need avoid in control module 4 in storer during write data, a bad some cancellation module 6 is wanted read data.Certainly, three memories also can be the Same Physical memory, but owing to will take into account various requirement, so cost can be higher, and the storage chip area can be bigger.
The 3rd memory 3, first memory 1 and second memory 2 capacity separately all should enough be deposited dispatch from the factory bad point data and dynamic bad point data; The byte number that needs depends on the maximum picture size size of supporting of this picture processing chip, can calculate accordingly the byte number that the bad point data of preserving each bad point need, and multiply by then the maximum bad point number of expectation, just can obtain the needed capacity of memory; Obviously, exactly always the method for insurance will be counted as maximum bad point number, but the wasting of resources is more serious like this, so generally can be rule of thumb or test obtain a maximum bad point number the most feasible.
A kind of in above-mentioned picture processing chip the method for self-adapting automatic dead point detection, as shown in Figure 2, may further comprise the steps:
A, picture processing chip power on, and control module 4 will be descended electricity, and the storer of obliterated data---promptly the bad point data in the 3rd storer 3 does not copy to first memory 1, second memory 2 respectively.When picture processing chip powered on for the first time, the bad point data in described the 3rd memory 3 were the data of bad point of dispatching from the factory; The dynamic bad point data that also exist when dispatching from the factory the data of bad point and front principal afterwards.This step is to eliminate in order to go bad immediately after powering on a little, and badly the some detection also can have continuity, can certainly omit this step in the practical application.
Described bad point data comprise: bad point coordinate and bad point attribute; Described bad point coordinate can be the form of (X, Y) or (Y, X); Comprise bad point source at described bad point attribute, be used for indicating this bad point and be the automatic detected bad point bad point of still dispatching from the factory; The data of automatic detected bad point are called dynamic bad point data, and the bad point attribute of this part bad point data also comprises confidence level and the statistics number of this bad point.In order to prevent that data from overflowing, in control module 4, be preset with the maximal value of confidence level and statistics number.
If the situation that exists MEM1 mentioned above and MEM2 to rotate, then also to select this moment which earlier as first memory 1, which is earlier as second memory 2; If do not rotate, the step of this selection need not be arranged then.Do not eliminate if do not go bad a little, have only a memory bank so, then can should not select step yet as first memory 1.
B, open stream after, carry out bad point detection, can also walk abreast and carry out bad point and eliminate.
Bad point detection module 5 is carried out bad point detection one time to each frame, and testing result reported control module 4, whether the bad point data that the bad point coordinate that detection is obtained by control module 4 and first memory are 1 li are compared, judge whether this bad point has existed and be automatic detected bad point; By comparison result the bad point that respectively detects is carried out respective handling then.Specifically be divided into following several situation:
One, the data of detected bad point have existed and for dispatching from the factory bad point; This situation is not handled.
Two, the data of detected bad point have existed and have been detected bad point automatically, as long as its confidence level and statistics number do not reach default maximal value, then 1 li of first memory its confidence level and statistics number are respectively added 1; If confidence level and/or statistics number reach maximal value, then reach peaked and do not add 1.
Three, the data of detected bad point do not exist; This situation is in 1 li data of adding this bad point of first memory, and attribute is made as automatic detected bad point, and confidence level and statistics number are 1.
Four, the memory for bad point detection exists the bad point of data not detect; This situation subtracts 1 in 1 li confidence level with this bad point of first memory, and statistics number adds 1.
Can be in second to the 4th kind of situation, whether statistics number and Credibility judgement according to each bad point that automatically detects after processing should delete these bad point data, concrete determination methods is: if confidence level is less than the deletion threshold value greater than statistical threshold for the statistics number of a bad point, perhaps the ratio of confidence level and statistics number is then deleted these bad point data from 1 li of first memory less than the deletion threshold value.The scheme of more optimizing is only when the 4th kind of situation statistics number and confidence level to be judged, just only has confidence level with a bad point to subtract 1 and goes just to judge whether it should delete afterwards.Described statistical threshold and deletion threshold value are set in the described control module 4 as required in advance.
To carry out confidence level statistics be not in order to prevent because erroneous judgement and the bad point data deletion of will dispatching from the factory to the bad point of dispatching from the factory; Also can not distinguish the bad point of dispatching from the factory bad point and automatically detecting during practical application, all process according to the mode of automatic detected bad point, namely in the bad point attribute of all bad point data, not comprise the bad point source, and all comprise confidence level and statistics number; When detecting bad point, also only there be in addition above-mentioned second to the 4th kind of situation.
Bad point cancellation module 6 is repaired each bad point according to the bad point data of 2 li storages of second memory, is exactly specifically to find corresponding bad point and repair according to the bad point coordinate; The bad point of being repaired comprises bad point and the automatic detected bad point of dispatching from the factory.
If picture processing chip will descend electricity during step B carried out, then in content replication to the three memories 3 of control module 4 with 1 li of first memory, can copy all, also can only copy dynamic bad point data, then lower electricity.Obliterated data not behind 3 times electricity of the 3rd memory, the benefit of therefore doing like this are the time that can save bad point detection and elimination when next time powering on, and can protect the bad point information of dispatching from the factory.Also can directly descend during practical application.
If arrive the next update cycle during step B carries out, then carry out step C; Update cycle i.e. the statistics number (being frame number) at twice bad point Data Update institute interval, and is common in the interval of two frames, can be by the user according to presetting and be kept at control module 4 in the actual conditions.
C, bad point data are upgraded, and specific practice is:
As a distortion of the present invention, can also be with reference to method mentioned above in the purposes that copies two memory banks of rear exchange.
If picture processing chip will descend electricity during step C carried out, then in content replication to the three memories 3 of control module 4 with 1 li of first memory, can copy all, also can only copy dynamic bad point data, then lower electricity.Also can directly descend during practical application.
Do not eliminate if do not go bad a little, then can not have step C.
Certainly; the present invention also can have other various embodiments; under the situation that does not deviate from spirit of the present invention and essence thereof; those of ordinary skill in the art work as can make various corresponding changes and distortion according to the present invention, but these corresponding changes and distortion all should belong to the protection domain of the appended claim of the present invention.
Claims (12)
1. the picture processing chip of a self-adapting automatic dead point detection comprises the bad point detection module; It is characterized in that, also comprise control module and memory;
Described bad point detection module detects a bad point to each frame after being used for opening stream, and testing result is reported control module;
Described storer comprises first memory, is used for bad point data of dynamic memory;
Described control module is used for reading the bad point data of first memory, and compares with described testing result, increases, revises and delete corresponding bad point data according to the result who compares in first memory.
2. picture processing chip as claimed in claim 1, it is characterized in that: the bad point data of storing in the described first memory comprise: bad point coordinate and bad point attribute; Described bad point attribute comprises confidence level and the statistics number of automatic detected bad point.
3. picture processing chip as claimed in claim 2 is characterized in that, described control module also is used to preserve confidence level maximal value, statistics number maximal value, statistical threshold and deletion threshold value;
Described control module increases, revises and deletes corresponding bad point data according to the result who compares and specifically is meant in first memory:
If the data of detected bad point have existed and be automatic detected bad point, as long as its confidence level and statistics number do not reach maximal value, then confidence level and the statistics number with this evil idea point in the first memory respectively adds 1; If confidence level and/or statistics number reach maximal value, then reach peaked and do not add 1;
If the data of detected bad point do not exist; Then add the data of this bad point in first memory, attribute is made as detected bad point automatically, and its confidence level and statistics number are 1;
If exist the bad point of bad point data not detect in the first memory; Then the confidence level with this bad point in the first memory subtracts 1, and statistics number adds 1;
If confidence level is less than the deletion threshold value greater than statistical threshold for the statistics number of a bad point, perhaps the ratio of confidence level and statistics number is then deleted the data of this bad point in the first memory less than the deletion threshold value.
4. picture processing chip as claimed in claim 1 is characterized in that: also comprise bad some cancellation module, described storer also comprises second memory;
Described bad point cancellation module is used for carrying out the bad point reparation according to the bad point data of second memory;
Described control module also be used for to preserve the update cycle and when new update cycle of one of every arrival, the bad point data with in the first memory copy in the second memory.
5. picture processing chip as claimed in claim 4 is characterized in that: described storer also comprises the 3rd storer;
Described the 3rd memory is the not memory of obliterated data of lower electricity, the bad point data when being used for preserving front principal;
Described control module also is used for when picture processing chip powers on, with the bad point data Replica preserved in the 3rd memory in first memory and second memory; And under picture processing chip before the electricity, in bad point data Replica to the three memories in the first memory.
6. the method for a self-adapting automatic dead point detection in the described picture processing chip of claim 1 comprises:
A, picture processing chip power on;
When b, frame arrival, carry out bad point detection one time;
C, bad point data in testing result and the first memory are compared;
D, according to the comparison the result in first memory, increase, revise and delete corresponding bad point data; If continuation detects then returns step b.
7. method as claimed in claim 6 is characterized in that, described bad point data comprise: bad point coordinate and bad point attribute; Described bad point attribute comprises confidence level and the statistics number of automatic detected bad point.
8. method as claimed in claim 7 is characterized in that, described steps d specifically comprises:
If the data of detected bad point have existed and for dispatching from the factory bad point, have not then handled;
If the data of detected bad point have existed and for automatic detected bad point, as long as its confidence level and statistics number do not reach default maximal value, then in first memory its confidence level and statistics number are respectively added 1; If confidence level and/or statistics number reach maximal value, then reach peaked and do not add 1;
If the data of detected bad point do not exist, then in first memory, add the data of this bad point, confidence level and statistics number are 1;
If in first memory, exist the bad point of data not detect, then in first memory, the confidence level of this bad point is subtracted 1, statistics number adds 1;
If confidence level is less than the deletion threshold value greater than statistical threshold for the statistics number of a bad point, perhaps the ratio of confidence level and statistics number is then deleted these bad point data less than the deletion threshold value in first memory.
9. method as claimed in claim 6 is characterized in that, also comprises the step e that walks abreast to d with step b: each bad point is repaired according to the bad point data in the second memory.
10. method as claimed in claim 9 is characterized in that, if described step b arrives the next update cycle in arbitrary step in the e, then carries out step f: the bad point data in the first memory is copied to second memory; Return step b and step e then.
11. method as claimed in claim 10 is characterized in that, if described step b in the f in arbitrary step picture processing chip to descend electricity, then with rear lower electricity in three memories of the content replication to the in the first memory.
12. method as claimed in claim 11 is characterized in that, also comprises among the described step a: the bad point data in the 3rd storer is copied to respectively in first memory and the second memory.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN200710120217A CN100589083C (en) | 2007-08-13 | 2007-08-13 | Picture processing chip for self-adapting automatic dead point detection and method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN200710120217A CN100589083C (en) | 2007-08-13 | 2007-08-13 | Picture processing chip for self-adapting automatic dead point detection and method thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101110050A true CN101110050A (en) | 2008-01-23 |
| CN100589083C CN100589083C (en) | 2010-02-10 |
Family
ID=39042129
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN200710120217A Expired - Fee Related CN100589083C (en) | 2007-08-13 | 2007-08-13 | Picture processing chip for self-adapting automatic dead point detection and method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN100589083C (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101895786A (en) * | 2010-07-15 | 2010-11-24 | 杭州海康威视软件有限公司 | Detection method and device for image sensor |
| CN102045584A (en) * | 2010-12-23 | 2011-05-04 | 杭州海康威视软件有限公司 | Method for acquiring bad point detection image of image sensor and device thereof |
| CN103106927A (en) * | 2011-11-15 | 2013-05-15 | 恒景科技股份有限公司 | Dead pixel correction method and device |
| CN104333676A (en) * | 2014-11-11 | 2015-02-04 | 广东中星电子有限公司 | Defective pixel shielding method and system |
| CN105631443A (en) * | 2016-03-11 | 2016-06-01 | 广东欧珀移动通信有限公司 | Fingerprint template updating method and terminal device |
| CN107305695A (en) * | 2016-04-14 | 2017-10-31 | 上海富瀚微电子股份有限公司 | A kind of automatic bad point means for correcting of image and method |
-
2007
- 2007-08-13 CN CN200710120217A patent/CN100589083C/en not_active Expired - Fee Related
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101895786A (en) * | 2010-07-15 | 2010-11-24 | 杭州海康威视软件有限公司 | Detection method and device for image sensor |
| CN102045584A (en) * | 2010-12-23 | 2011-05-04 | 杭州海康威视软件有限公司 | Method for acquiring bad point detection image of image sensor and device thereof |
| CN103106927A (en) * | 2011-11-15 | 2013-05-15 | 恒景科技股份有限公司 | Dead pixel correction method and device |
| CN104333676A (en) * | 2014-11-11 | 2015-02-04 | 广东中星电子有限公司 | Defective pixel shielding method and system |
| CN104333676B (en) * | 2014-11-11 | 2019-06-21 | 广东中星微电子有限公司 | A kind of bad point covering method and system |
| CN105631443A (en) * | 2016-03-11 | 2016-06-01 | 广东欧珀移动通信有限公司 | Fingerprint template updating method and terminal device |
| CN105631443B (en) * | 2016-03-11 | 2019-03-22 | Oppo广东移动通信有限公司 | The update method and terminal device of fingerprint template |
| CN107305695A (en) * | 2016-04-14 | 2017-10-31 | 上海富瀚微电子股份有限公司 | A kind of automatic bad point means for correcting of image and method |
| CN107305695B (en) * | 2016-04-14 | 2021-03-09 | 上海富瀚微电子股份有限公司 | Automatic image dead pixel correction device and method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN100589083C (en) | 2010-02-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN100589083C (en) | Picture processing chip for self-adapting automatic dead point detection and method thereof | |
| CN105843749B (en) | A kind of NAND Flash fault-tolerance approach based on FPGA | |
| CN100538662C (en) | A kind of deterioration balance method of the storer based on local sampling | |
| CN104063186B (en) | A kind of data access method of electronic equipment | |
| CN107832013B (en) | A method of management solid-state hard disc mapping table | |
| CN109496300A (en) | A kind of storage medium rubbish recovering method, storage medium and program product | |
| CN103678150A (en) | Method and device for using solid state disk | |
| CN109753443A (en) | A kind of data processing method, device and electronic equipment | |
| CN106484330A (en) | A kind of hybrid magnetic disc individual-layer data optimization method and device | |
| CN104360957A (en) | Method for maintaining flash memory wear leveling | |
| US20110302367A1 (en) | Write Buffer for Improved DRAM Write Access Patterns | |
| CN106227621A (en) | The data back up method of logic-based volume management simplification volume and system | |
| CN112988673B (en) | Method and equipment for processing data overflow in decompression process | |
| CN107436845A (en) | A kind of Dram monitoring system and its monitoring method | |
| CN106445740A (en) | Control method and control system for NAND flash memory data in solid state disk | |
| CN109471594A (en) | A kind of MLC flash reading/writing method | |
| CN112347001B (en) | Verification method and device for flash memory garbage collection and electronic equipment | |
| CN106708960A (en) | Restoration method for file system of NAS device and NAS device | |
| CN110517718B (en) | Method and system for effectively screening new and bad blocks of particles | |
| US20130031311A1 (en) | Interface apparatus, calculation processing apparatus, interface generation apparatus, and circuit generation apparatus | |
| CN105389128B (en) | A kind of solid state hard disk date storage method and storage control | |
| CN1804822A (en) | Multi-interruption cache device and method | |
| CN109390010A (en) | Dynamic random access memory and its operating method | |
| CN107943610A (en) | A kind of method of lifting SSD comprehensive performances | |
| CN110262980A (en) | High-speed memory system based on finite lifetime storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20100210 Termination date: 20200813 |