CN113792771A

CN113792771A - TCON chip for processing graphic card

Info

Publication number: CN113792771A
Application number: CN202111016336.3A
Authority: CN
Inventors: 刘智君; 卫敏; 段云鹏
Original assignee: Qingdao Xinxin Microelectronics Technology Co Ltd
Current assignee: Qingdao Xinxin Microelectronics Technology Co Ltd
Priority date: 2021-08-31
Filing date: 2021-08-31
Publication date: 2021-12-14
Anticipated expiration: 2041-08-31
Also published as: CN113792771B

Abstract

The application relates to providing a TCON chip for processing a graphic card, which is used for solving the problem that the dependence and the requirement of software detection of the graphic card on a CPU are higher, so that some low-end TOCN chips cannot detect the graphic card. According to the image data detection method and device, the self-adaptive binarization module is used for binarizing the image data to obtain image data to be detected, the feature recognition module is used for matching the obtained image data to be detected with preset graphic card features, finally, the graphic card corresponding to the preset graphic card features is detected, finally, the TCON chip is used for detecting and correcting the graphic card through a pure hardware structure, dependence and requirements on a CPU are reduced, and the TOCN chip can be adapted to the low end. In addition, the method and the device also adopt a detection mode of multiplexing a large number of hardware modules and multiplexing time sharing, reduce the area of the hardware modules, and simultaneously set the software channel module for the TCON chip with the CPU to operate, thereby greatly reducing the updating and upgrading period of the chip and reducing the chip cost.

Description

TCON chip for processing graphic card

Technical Field

The application relates to the technical field of graphic card detection, in particular to a TCON chip for processing a graphic card.

Background

In the prior art, in the process of displaying a picture, due to characteristics of a TCON chip (Timing Control chip) and liquid crystal itself, such as limitation of charging and discharging, when displaying some specific graphic cards, crosstalk between pixels may occur. On the one hand, the abnormal display of the picture is caused, and on the other hand, if the state is kept for a long time, the service life of the liquid crystal display is also influenced.

Software detection of these special cards requires a relatively high bandwidth for the bus and a certain requirement for the budget Processing capability of the CPU, which is not allowed for some low-end TOCN chips, so a way to reduce the dependence and requirement on the CPU (Central Processing Unit) and adapt to the detection of the cards by the low-end TOCN chips is needed.

Disclosure of Invention

The application aims to provide a TCON chip for processing a graphic card, which is used for solving the problems of high dependence and high requirement of software detection of the graphic card on a CPU.

In a first aspect, the present application provides a TCON chip for processing a graphic card, the chip including an adaptive binarization module and a feature identification module, wherein:

the self-adaptive binarization module is used for carrying out binarization on the image data to obtain image data to be detected;

the feature recognition module is used for matching the image data to be detected with a preset image card feature, and if the preset image card feature is matched, determining that the image card corresponding to the preset image card feature is detected.

In one possible embodiment, the feature recognition module comprises at least one feature detection module and/or at least one feature statistics module, wherein:

the characteristic detection module is used for detecting the pixel rows of the data to be detected by adopting the chart card row vectors in the preset chart card characteristics to obtain the row vector detection result of each pixel row, and performing characteristic detection on the row vector detection result of each pixel row by adopting the row vector arrangement characteristics in the preset chart card characteristics to obtain the chart card detection result in the data to be detected;

the characteristic counting module is used for detecting the pixel rows of the data to be detected by adopting the chart card row vectors in the preset chart card characteristics to obtain the row vector detection result of each pixel row.

In a possible implementation manner, the detecting of the pixel rows of the data to be detected by using the card row vector in the preset card features is performed to obtain the row vector detection result of each pixel row, and the feature detection module and the feature statistics module are specifically configured to:

for each pixel row, sequentially matching a plurality of binary data of the pixel row with the graphics card row vector;

if n card row vectors are continuously matched, recording the starting position and the ending position of the card row vectors and the continuous number of the card row vectors, wherein n is larger than or equal to a first threshold value;

and if the pixel row is matched with a plurality of graphic card row vectors, selecting the identifier of the graphic card row vector with the largest continuous quantity as the row vector detection result of the pixel row.

In a possible implementation, the feature detection module and the feature statistics module are further configured to:

each pixel row is respectively used as a target pixel row, and the starting position of the graphic card row vector of the target pixel row and the adjacent pixel row is compared aiming at the target pixel row;

if the initial positions of the card row vectors of the adjacent pixel rows are the same, and the initial position of the card row vector of the target pixel row is different from the initial position of the card row vector of the adjacent pixel row, correcting the initial position of the card row vector of the target pixel row to be the initial position of the card row vector of the adjacent pixel row; wherein the starting position of the graphics card row vector of the target pixel row is the starting position of the graphics card row vector with the largest continuous quantity.

In a possible implementation manner, the performing the feature detection on the row vector detection result of each pixel row by using the row vector arrangement feature in the preset map card feature to obtain the map card detection result in the data to be detected, where the feature detection module is specifically configured to:

matching the row vector detection results of the pixel rows with the row vector arrangement characteristics in sequence;

if m row vector arrangement characteristics are continuously matched, recording the starting position and the ending position of the row vector arrangement characteristics and the continuous number of the row vector arrangement characteristics, wherein m is greater than or equal to a second threshold value;

and if the pixel rows are matched with various row vector arrangement characteristics, selecting the identification of the row vector arrangement characteristic with the largest continuous quantity as the detection result of the image card.

In a possible implementation manner, if a plurality of feature search modules in the feature recognition module are used to detect the preset image card features, each feature search module is responsible for detecting a designated image area of the image data to be detected, where each feature search module in the plurality of feature search modules is the feature detection module or the feature statistics module.

In one possible implementation, when the feature identification module has a plurality of feature search modules, the chip further includes a single frame detection arbitration module:

the single-frame detection arbitration module is used for obtaining a decision result according to the detection results of the modules; the decision result is used for indicating whether a graphic card needing to be corrected exists or not and a graphic card identifier needing to be corrected, wherein the graphic card identifier is used for correcting the graphic card, and each feature searching module is the feature detecting module or the feature counting module.

In one possible implementation, the single frame detection arbitration module includes a plurality of channel selection modules and a single frame decision module:

the channel selection module is used for selecting two detection results of the corresponding feature search module from the plurality of feature search modules based on a preset gating rule; performing logic and operation on the two detection results to obtain a selection result of the selection module;

the single-frame decision module is configured to perform a logical or operation on the selection results of the plurality of channel selection modules to obtain the decision result.

In a possible implementation manner, when the type of the card to be detected exceeds a preset threshold, the chip further includes a multi-frame setting configuration module and a multi-frame detection arbitration module:

the multi-frame setting configuration module is used for setting the type of the graphic card detected by the characteristic identification module;

the multi-frame detection arbitration module is also used for acquiring the decision result of the single-frame decision module in a multi-frame detection mode; and if the decision result indicates that the image card needs to be corrected, determining that the type of the image card detected by the feature recognition module is unchanged, and if the decision result indicates that the image card does not exist, replacing the type of the image card which needs to be detected and is set by the multi-frame setting configuration module.

In one possible implementation, the chip further includes a signal correction module:

and the signal correction module is used for matching a corresponding correction mode according to the detected image card identifier and performing correction operation on the image data by adopting the correction mode.

In one possible implementation, the chip further comprises a software channel module:

and the software channel module is used for sending the detected graphic card identifier to the CPU for correction.

The technical scheme provided by the embodiment of the application at least has the following beneficial effects:

in the embodiment of the application, the self-adaptive binarization module is used for binarizing the image data to obtain image data to be detected, the obtained image data to be detected is sent to the feature recognition module, the feature recognition module is used for matching the obtained image data to be detected with the preset image card features, and finally, the image card corresponding to the preset image card features is detected. The method and the device have the advantages that a large number of hardware modules are multiplexed on hardware, and the purpose of reducing the chip cost is achieved by reducing the area of the modules. In addition, this application can also adopt the multiplexing detection mode of timesharing on hardware, can detect more picture cards, satisfies customer's demand, sets up software channel module simultaneously, can supply to have CPU's TCON chip to operate, greatly reduced the cycle that the chip updated, reduced the chip cost.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic diagram of a graphic card according to an embodiment of the present disclosure;

FIG. 2 is a schematic diagram of another graphics card provided in an embodiment of the present application;

fig. 3 is a schematic structural diagram of a TCON chip for processing a graphic card according to an embodiment of the present disclosure;

fig. 4 is a schematic structural diagram of an internal module of a TCON chip for processing a graphic card according to an embodiment of the present disclosure;

FIG. 5 is a schematic diagram illustrating a selection of multiple modules to detect different regions according to an embodiment of the present disclosure;

fig. 6 is a schematic flowchart of an arbitration module for single frame detection according to an embodiment of the present application;

fig. 7 is a schematic flowchart of determining a decision result according to priority according to an embodiment of the present application;

fig. 8 is a schematic flowchart of another single frame detection arbitration module according to an embodiment of the present application;

fig. 9 is a schematic structural diagram of a signal correction module according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. The embodiments described are some, but not all embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Also, in the description of the embodiments of the present application, "/" indicates or means, for example, a/B may indicate a or B; "and/or" in the text is only an association relationship describing an associated object, and means that three relationships may exist, for example, a and/or B may mean: three cases of a alone, a and B both, and B alone exist, and in addition, "a plurality" means two or more than two in the description of the embodiments of the present application.

The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as implying or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", and "a" may explicitly or implicitly include one or more features, and in the description of embodiments of the present application, "a plurality" means two or more unless otherwise indicated.

Hereinafter, some terms in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

Drawing card: the image test card is composed of a color block and a black-and-white block, wherein the color block and the black-and-white block are combined with three pixels of R (red), G (green) and B (blue), and is mainly used for testing and correcting a display picture of a liquid crystal display.

In the prior art, in the process of displaying a picture, due to the characteristics of the TCON chip and the liquid crystal itself, such as the limitation of charging and discharging, when displaying some specific graphic cards, crosstalk between pixels may occur. On the one hand, the abnormal display of the picture is caused, and on the other hand, if the state is kept for a long time, the service life of the liquid crystal display is also influenced.

Software detection of these special cards requires a relatively high bandwidth for the bus and a certain requirement for the budget processing capability of the CPU, which is not allowed for some low-end TOCN chips, and therefore a way to detect cards that reduces the dependence on and requirements for the CPU and can accommodate low-end TOCN chips is needed.

In view of this, embodiments of the present application provide a TCON chip for processing a graphics card, where the chip implements detection of the graphics card based on a hardware manner.

In order to facilitate the detection of the graphic card by adopting a hardware mode, the characteristics of different types of graphic cards are analyzed in the embodiment of the application to obtain the characteristic expression of the graphic card, which facilitates the hardware detection.

As shown in fig. 1 and fig. 2, are schematic views of different graphics cards. Each three boxes side by side in the lateral direction in the schematic diagrams of the cards shown in fig. 1 and 2 represent a pixel. The three boxes in solid black indicate that the pixel value is low and will be binarized to 0 when binarized. Based on this, as shown in fig. 1, the graph card provided in the embodiment of the present application only includes one kind of feature vector, where the feature vector of the graph card 101 is 1010, 0101, the feature vector of the graph card 102 is 1010, 0101, the feature vector of the graph card 103 is 1010, and the feature vector of the graph card 104 is 1100, and 1100.

As shown in fig. 2, for the graph card provided in the embodiment of the present application and including multiple feature vectors, the feature vectors corresponding to the graph card are 1010, 0101, and 0101, 0101.

As shown in fig. 3, the chip includes an adaptive binarization module 301 and a feature identification module 302, wherein:

the adaptive binarization module 301 is configured to binarize the image data to obtain image data to be detected.

The adaptive binarization module 301 is configured to binarize according to a binarization threshold, where when data of any pixel is higher than the binarization threshold, the binarization of the pixel is 1, and when the data of any pixel is less than or equal to the binarization threshold, the binarization of the pixel is 0, so as to obtain a binarization image composed of 0 and 1. The self-adaptive binarization module can self-adaptively adjust the binarization threshold value according to the image content. The binarization threshold value can be adaptively determined according to the overall brightness condition of a frame image. For example, when the overall luminance is high, the binarization threshold is high, and when the overall luminance is low, the binarization threshold is low. Certainly, in implementation, the binarization threshold value can be determined according to actual requirements, and the binarization threshold value is suitable for the embodiment of the application.

The feature recognition module 302 is configured to match the image data to be detected with a preset map card feature, and if the preset map card feature is matched, determine that a map card corresponding to the preset map card feature is detected.

Therefore, in the embodiment of the application, the adaptive binarization module 301 is used for binarizing the image data to obtain image data to be detected, the obtained image data to be detected is sent to the feature recognition module 302, the obtained image data to be detected is matched with the preset image card features by the feature recognition module 302, and finally the image card corresponding to the preset image card features is detected.

In some possible embodiments, in order to reduce the hardware cost of the feature recognition module 302 for detecting the graphics card, the adaptive binarization module 301 is responsible for binarizing the image data input at the front end by using an adaptive binarization threshold algorithm, so as to obtain image data to be detected, and sending the image data to the feature recognition module 302. For example, the adaptive binarization module 301 may binarize the image data with 3 × 10 bits to obtain the data to be detected with 3 × 1 bits. Where 3 means R, G, B three pixel channels, and 10 means that one pixel channel has a value of 10 bits.

In an embodiment, the adaptive binarization module 301 may binarize R, G, B image data of three pixel channels, where if at least one of the data to be detected after the binarization of the image data of R, G, B three pixel channels of the same pixel point is 1, the data to be detected is matched as 1 of the feature vector, and if all of the data to be detected after the binarization of the image data of R, G, B three pixel channels of the same pixel point is 0, the data to be detected is matched as 0 of the feature vector.

In another embodiment, the adaptive binarization module 301 may binarize the image data according to the brightness to obtain a brightness binarization image, where a pixel in the brightness binarization image corresponds to a value, so as to be conveniently matched with the feature vector. For example, the binarization value of the pixel is 1, that is, the pixel is matched with 1 in the feature vector, and the binarization value of the pixel is 0, that is, the pixel is matched with 0 in the feature vector.

Therefore, the data amount required to be processed can be reduced by performing binarization processing on the image data through the self-adaptive binarization module, so that the hardware cost for detecting the graphic card by the feature identification module 302 is reduced, and meanwhile, the graphic cards with different gray scale levels can be detected.

In some embodiments, as shown in fig. 4, a schematic structural diagram of an internal module of a TCON chip provided in an embodiment of the present application is shown, and as shown in fig. 4, the feature identification module 302 in the embodiment of the present application includes at least one feature detection module and/or at least one feature statistics module.

The characteristic detection module is used for detecting the pixel rows of the data to be detected by adopting the line vectors of the preset image card characteristics to obtain the line vector detection result of each pixel row, and detecting the characteristics of the line vector detection result of each pixel row by adopting the line vector arrangement characteristics of the preset image card characteristics to obtain the image card detection result of the data to be detected;

the characteristic statistic module is a simplified structure of the characteristic detection module and is used for executing partial functions of the characteristic detection module. The characteristic statistic module is used for detecting the pixel rows of the data to be detected by adopting the chart card row vectors in the preset chart card characteristics to obtain the row vector detection result of each pixel row.

For example, the detection of the row vector of each pixel row may be specifically implemented as: for each pixel row, sequentially matching a plurality of binarized data of the pixel row acquired from the adaptive binarization module 301 with the graphics card row vector in the preset graphics card features; if the number of times of continuously matching n card row vectors is reached, recording the starting position and the ending position of the card row vectors and the continuous number of the card row vectors, wherein n is greater than or equal to a first threshold value; and if the pixel row is matched with a plurality of graphic card row vectors, selecting the identifier of the graphic card row vector with the largest continuous quantity as the row vector detection result of the pixel row.

For example, the binarization data of the pixel row acquired in the adaptive binarization module 301 are 0101, 1010, 0101, and the card row vector in the preset card feature is 0101, the first and second data are considered to be matched to the card row vector twice in succession, the third data are considered to be not matched to the card row vector, n is 2, if n is greater than or equal to a first threshold, a consecutive number of the card row vector is recorded 2, the fourth, fifth, and sixth data are considered to be matched to the card row vector three times in succession, n is 3, if n is greater than or equal to the first threshold, a consecutive number of the card row vector is recorded 3, and at this time, 3 is greater than 2, an identifier of a later recorded card row vector is selected as the row vector detection result of the pixel row.

The identification corresponding to each kind of card row vector is different, and the identification corresponding to each kind of card row vector can be set according to the specific situation in the practical application. For example, if the card row vector is not detected, the detection result of the card row vector is identified as 0; if the detected image card row vector is 0101, the identifier of the image card row vector is 1; if the detected image card row vector is 1010, the identifier of the image card row vector is 2; if the detected graph card row vector is 0011, the identifier of the graph card row vector is 3; if the detected card row vector is 1100, the identifier of the card row vector is 4.

In some embodiments, if the card row vector is noisy, the noise will interfere with the detection and cause false detection, so the initial position of the card row vector where the noise occurs needs to be corrected. Wherein the starting position of the line vector of the graphics card is the starting position of the line vector of the graphics card with the largest number of continuous lines. The feature detection module and the feature statistics module are therefore further configured to: and respectively taking each pixel row as a target pixel row, comparing the initial positions of the card row vectors of the target pixel row and the adjacent pixel row aiming at the target pixel row, and correcting the initial position of the card row vector of the target pixel row as the initial position of the card row vector of the adjacent pixel row if the initial positions of the card row vectors of the adjacent pixel row are the same and the initial position of the card row vector of the target pixel row is different from the initial position of the card row vector of the adjacent pixel row.

One embodiment is shown in table 1:

TABLE 1

In table 1, the line start position of the second line vector is different from the line start positions of the first line vector and the third line vector, so the start position of the second line vector is corrected to be the line start positions of the first and third line vectors.

In some embodiments, in order to detect a card with a more complex structure, the feature detection module in the feature identification module 302 may further perform feature detection on the row vector detection result of each pixel row by using the row vector arrangement feature in the preset card feature, so as to obtain the card detection result in the data to be detected.

One possible implementation manner is that the feature detection module is adopted to sequentially perform matching operation on the row vector detection results of the pixel rows and the row vector arrangement features; if m row vector arrangement characteristics are continuously matched, recording the starting position and the ending position of the row vector arrangement characteristics and the continuous number of the row vector arrangement characteristics, wherein m is greater than or equal to a second threshold value; and if the pixel rows are matched with various row vector arrangement characteristics, selecting the identification of the row vector arrangement characteristic with the largest continuous quantity as the detection result of the image card.

The row vector arrangement characteristic is determined according to the identification of the graphics card row vector recorded when the row vector of each pixel row is detected. For example, if the detection result obtained by detecting the card row vector in each row is 0101, 1010, the recorded card row vector is identified as 1, 2, and the corresponding row vector arrangement feature is 1212.

The identification corresponding to each kind of line vector arrangement characteristic is different, and the identification corresponding to each kind of specific line vector arrangement characteristic of the graphic card can be set according to specific conditions in practical application. For example, if the row vector arrangement feature is not detected, the identifier of the row vector arrangement feature is 0; if the detected row vector arrangement characteristic is 1212, the identifier of the row vector arrangement characteristic is 1; if the detected card row vector is 1111, the identifier of the row vector arrangement feature is 2. Specifically, it can be set as shown in table 2:

TABLE 2

In the embodiment of the present application, each of the first threshold and the second threshold may be the same or different, and m and n may be the same or different, and are all applicable to the embodiment of the present application.

In some embodiments, for convenience of the following description, a feature search module is defined, which is the feature detection module or the feature statistics module. In implementation, in order to prevent false detection, the feature search module can be selected and used according to the image card to be detected. And if a plurality of feature searching modules in the feature identification module are adopted to detect the preset image card features, each feature searching module is responsible for detecting the appointed image area of the image data to be detected. For example, one frame of image is divided into a plurality of image areas, a first feature search module is responsible for one of the image areas, and another feature search module is responsible for another image area. In summary, the differences between the feature detection module and the feature statistics module are shown in table 3:

TABLE 3

As shown in table 3, it can be seen that, in the embodiment of the present application, the feature statistics module detects one card at a time and detects the row feature vector of the card, and the feature detection module can detect all cards at a time and detect both the row feature vector and the column feature vector of the card, so that it can be known that the complexity of the feature statistics module is lower and the area of the feature statistics module is smaller than the area of the feature detection module, for example, the area of the feature statistics module may be 1/3 of the feature detection module.

In practical application, the feature detection module, the feature statistical module or the multi-module hybrid module can be selected and used according to specific situations. For example: for the graphic card only containing single feature vector, generally only using the feature detection module to detect, for the graphic card containing a plurality of feature vectors, a plurality of feature detection modules and/or a plurality of feature statistical modules can be used to complete detection in different areas according to the features of different areas of the screen. As shown in fig. 5, the feature detection module 0 in fig. 4 is used to detect the feature vector 0101 in the region 1, the feature detection module 1 detects the feature vector 1010 in the region 2, and the feature statistics module 0 detects the feature vector 1100 in the region 3.

Therefore, the feature recognition module matches the acquired image data to be detected with the preset image card features, the detection of the data to be detected corresponding to the image card to be detected is completed, the detection and correction of the TCON chip to the image card are completed by adopting a pure hardware structure, and the purpose of reducing the chip cost is achieved. Meanwhile, a large number of hardware module multiplexing modes are adopted, such as a characteristic detection module and a characteristic statistical module, and the purpose of reducing the chip cost is achieved by reducing the area of the modules.

In some embodiments, if the feature recognition module 102 uses a plurality of feature search modules, the chip further comprises a single frame detection arbitration module:

the single-frame detection arbitration module is used for obtaining a decision result according to the detection results of the plurality of feature search modules; the decision result is used for indicating whether a graphic card needing to be corrected exists or not and a graphic card identifier needing to be corrected, wherein the graphic card identifier is used for correcting the graphic card, and each feature searching module is the feature detecting module or the feature counting module.

In one embodiment, as shown in fig. 6, a plurality of feature search modules, such as the feature detection module 0, the feature detection module 1, the feature statistics module 0, and the feature statistics module 1 in fig. 4, are used for detecting the graphics card, and a single frame detection arbitration module is required. The method comprises the steps that a feature detection module 0, a feature detection module 1, a feature statistics module 0 and a feature statistics module 1 send feature identifiers 1, 0 and 0 obtained through detection to a channel selection module of a single-frame detection arbitration module, the channel selection module selects two feature identifiers of corresponding feature search modules from a plurality of feature search modules according to a preset gating rule, logic and operation are conducted on the two feature identifiers, and a selection result passing through the selection module is obtained.

For example, channels 1 and 2 are selected for the first time to obtain feature identifiers 1 and 0, and then a selection result of the selection module is 0 after logical and operation is performed; selecting

channels

1 and 3 for the second time to obtain feature identifiers 1 and 0, and performing logic AND operation to obtain a selection result 0 of the selection module; selecting channels 0 and 0 for the third time to obtain

feature identifiers

1 and 1, and performing logic AND operation to obtain a selection result of 1 passing through the selection module; and selecting channels 2 and 3 for the third time to obtain the feature identifiers 0 and 0, and performing logic AND operation to obtain that the selection result passing through the selection module is 0.

And then using a single-frame decision module to perform logic or operation on the selection results of the plurality of channel selection modules to obtain the decision result. For example, the result of the four selections is logically or-ed to obtain a final detection result of 1.

In some embodiments, if the feature vector of the graphic card is detected by the selection results of a plurality of the channel selection modules, a final decision result needs to be determined according to a preset priority.

Assuming that the selection results of the plurality of channel selection modules are respectively detection result 1, detection result 2, detection result 3, and detection result 4, the final decision result is determined according to a certain priority, and the specific steps are as shown in fig. 7:

in step 701, it is determined whether the detection result 1 is 0, if not, it indicates that the feature vector of the card is detected as the first result, in step 702, it is determined that the decision result is 1, and if it is 0, it indicates that the feature vector of the card is not detected as the first result, and step 703 is executed.

In step 703, it is determined whether the detection result 2 is 0, if not, it indicates that the feature vector of the card is detected as the second result, in step 704, it is determined that the decision result is the detection result 2, and if it is 0, it indicates that the feature vector of the card is not detected as the second result, and step 705 is executed.

In step 705, it is determined whether the detection result 3 is 0, if not, it indicates that the third result detects the feature vector of the card, in step 706, it is determined that the decision result is detection result 3, and if it is 0, it indicates that the third result does not detect the feature vector of the card, and step 707 is executed.

In step 707, it is determined whether the detection result 4 is 0, and if not, it indicates that the feature vector of the card is detected as the fourth result, in step 708, it is determined that the decision result is detection result 4, and if it is 0, it indicates that the feature vector of the card is not detected as the fourth result, and in step 709, it is determined that the decision result is 0.

Wherein the detection result with higher priority comprises the detection result with lower priority, and the correction of the detection result with high priority is equivalent to the correction of the detection result with low priority. Therefore, the result with high priority is selected as the final decision result in the detection results of the detected image card feature vectors.

In one possible implementation, as shown in fig. 8, a plurality of feature search modules, such as the feature detection module 0, the feature detection module 1, the feature statistics module 0, and the feature statistics module 1 in fig. 4, are used to detect the graphics card, and a single frame detection arbitration module is used. The feature detection module 0, the feature detection module 1, the feature statistics module 0 and the feature statistics module 1 send the detected

feature identifiers

1, 3 and 0 to the channel selection module of the single-frame detection arbitration module.

The channel selection module selects the feature identifiers of two channels of the corresponding feature search module (namely, the detection results output by the feature detection module or the feature statistics module) from the plurality of feature search modules according to a preset gating rule, and performs logic and operation on the two feature identifiers to obtain the selection result passing through the selection module. For example, channels 2 and 1 are selected for the first time to obtain

feature identifiers

3 and 1, but since both channels 2 and 1 detect feature vectors, according to a preset priority, a selection result of the passing selection module is obtained as 3 after logical and operation is performed, and feature identifiers 1 and 0 are obtained after

channels

1 and 3 are selected for the second time, and a selection result of the passing selection module is obtained as 0 after logical and operation is performed; selecting channels 0 and 0 for the third time to obtain

feature identifiers

1 and 1, and performing logic AND operation to obtain a selection result of 1 passing through the selection module; and selecting channels 2 and 3 for the third time to obtain feature identifiers 3 and 0, and performing logic AND operation to obtain that the selection result passing through the selection module is 0.

And then using a single-frame decision module to perform logic or operation on the selection results of the plurality of channel selection modules to obtain the decision result. For example, the results of the four selections are logically or-operated, and the selection results of two channel selection modules are the feature vectors of the detected graph card, so that the final detection result is 1 according to the preset priority, namely the priority of the first selection is higher than the priority of the third selection.

Therefore, the single-frame detection arbitration module can collect the results of the multi-path detection modules, arbitrate the detection results with higher complexity and higher priority into the final detected image card according to a certain selection rule and correct the final detected image card.

In some embodiments, when the number of the graphic cards to be detected exceeds a preset threshold, the existing hardware resources cannot meet the detection of the graphic cards, and at this time, a multi-frame detection mode needs to be started. Setting the type of the graphic card detected by the characteristic identification module through a multi-frame setting configuration module; and acquiring a decision result of the single-frame decision module in a multi-frame detection mode through a multi-frame detection arbitration module. And if the decision result indicates that the image card needs to be corrected, determining that the type of the image card detected by the feature recognition module is unchanged, and if the decision result indicates that the image card does not exist, replacing the type of the image card which needs to be detected and is set by the multi-frame setting configuration module.

In an embodiment, it may be specifically implemented that, assuming that a first frame detection card a set and a second frame detection card B set are used, if the card type detected by the feature identification module set in the current frame is a and the current frame decision result is 0, if the card a is not detected, the next frame of the card type to be detected set by the multi-frame setting configuration module is replaced with B; if the type of the image card detected by the feature identification module set in the current frame is A and the decision result of the current frame is 1, the type of the image card to be detected set by the multi-frame setting configuration module in the next frame is still A if the image card A is detected.

In another embodiment, it may be specifically implemented that, assuming that the cards detected in the first frame are 1, 2, and 3, if the card 1 is detected but the cards 2 and 3 are not detected, the card 1 is detected next but the cards 2 and 3 are not required to be detected, and the detection is replaced with the detection of the cards 1, 4, and 5. By analogy, when any kind of card is not detected, other kinds of cards can be detected instead.

In each embodiment, the parameter setting of the hardware module can be changed after selecting how many frames pass.

The multi-frame setting configuration module and the multi-frame detection arbitration module are a supplement under the condition of insufficient hardware resources of the chip, have insignificant difference with the correction effect of the single-frame detection arbitration module in vision, and can meet the requirements of customers on the image quality of the displayed image.

Therefore, the multi-frame detection mode is adopted in the method, the time-sharing multiplexing detection mode is adopted in hardware, more graphic cards can be detected, and the requirements of customers are met.

In some embodiments, when the TCON chip detects a graphic card, it needs to perform correction and compensation according to characteristics of the graphic card, for example, adjust the polarity of control signals such as POL, H2POL, and H2DOT, to reduce crosstalk between pixel points, so as to improve the display effect, as shown in fig. 4, the chip provided in this embodiment of the application further includes a signal correction module, configured to match a corresponding correction manner according to the detected graphic card identifier, and perform a correction operation on the image data by using the correction manner. Wherein, POL, H2DOT are the control signal that TCON chip control liquid crystal display.

For example, as shown in fig. 9, the TCON chip may further include a result setting module 901 and a correction module 902. The result setting module 901 sets in advance 16 correction methods for matching 50 kinds of cards, the result setting module 901 performs matching based on the detected card identification, and the correction module 902 performs correction of the control signal based on the correction method obtained by matching.

In some embodiments, the chip described in this embodiment of the present application further includes a software channel module, and the CPU may monitor the detected icon card identifier through the software channel module, and perform software correction on control signals such as POL, H2POL, and H2 DOT.

Therefore, more controllability is reserved for the TCON chip containing the CPU, the updating period of the chip is greatly reduced, and the chip cost is reduced.

Based on the foregoing description, in the embodiment of the present application, an adaptive binarization module is used to binarize image data to obtain image data to be detected, the obtained image data to be detected is sent to a feature identification module, the obtained image data to be detected is matched with a preset card feature by the feature identification module, and a card corresponding to the preset card feature is finally detected. The method and the device have the advantages that the multiplexing mode of hardware modules such as the feature detection module and the feature statistics module is adopted in hardware, and the purpose of reducing the chip cost is achieved by reducing the area of the modules. In addition, this application has still adopted multiframe detection module on hardware, through the detection mode of timesharing multiplex, detects more picture cards, satisfies customer's demand, sets up software channel module simultaneously, can supply the TCON chip that contains CPU to operate, greatly reduced the cycle that the chip is updated and is renewed, reduced the chip cost.

The embodiments provided in the present application are only a few examples of the general concept of the present application, and do not limit the scope of the present application. Any other embodiments extended according to the scheme of the present application without inventive efforts will be within the scope of protection of the present application for a person skilled in the art.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

Claims

1. A time sequence control (TCON) chip for processing a graphic card, which is characterized by comprising an adaptive binarization module and a feature identification module, wherein:

2. The chip according to claim 1, wherein the feature identification module comprises at least one feature detection module and/or at least one feature statistics module, wherein:

3. The chip according to claim 2, wherein the performing of the pixel row detection on the data to be detected by using the card row vector in the preset card features obtains a row vector detection result of each pixel row, and the feature detection module and the feature statistics module are specifically configured to:

4. The chip of claim 3, wherein the feature detection module and the feature statistics module are further configured to:

5. The chip according to claim 3, wherein the performing of the feature detection on the row vector detection result of each pixel row by using the row vector arrangement features in the preset graphics card features obtains the graphics card detection result in the data to be detected, and the feature detection module is specifically configured to:

6. The chip according to claim 2, wherein if a plurality of feature search modules in the feature identification module are used to detect the preset graphics card features, each feature search module is responsible for detecting a designated image area of the image data to be detected, wherein each feature search module is the feature detection module or the feature statistics module.

7. The chip of claim 2, wherein when the feature recognition module has a plurality of feature search modules, the chip further comprises a single frame detection arbitration module:

8. The chip of claim 7, wherein the single frame detection arbitration module comprises a plurality of channel selection modules and a single frame decision module:

9. The chip according to claim 7, wherein when the type of the card to be detected exceeds a preset threshold, the chip further comprises a multi-frame setting configuration module and a multi-frame detection arbitration module:

10. The chip of claim 2, further comprising a signal correction module: