WO2021169502A1

WO2021169502A1 - A pixel image as well as its processing method and application system

Info

Publication number: WO2021169502A1
Application number: PCT/CN2020/136533
Authority: WO
Inventors: Cheng FENG
Original assignee: Changsha Pixel Code Technology Co. Ltd
Priority date: 2020-02-27
Filing date: 2020-12-15
Publication date: 2021-09-02

Abstract

It is a pixel image as well as its processing method and application system, which is to decompose graph pixels into five basic elements, namely, black points, white points, geometric points, half-angle points and full-angle points, and then process the data and generate the pixel image. The method includes the steps of: 1. classifying graphs; 2. dividing a graph into different zones; 3. computing the sizes and pixel densities of graph zones to sort zone pixels and logical relationship of data; 4. obtaining basic pixel images and pixel application data values through comprehensive computing; 5. processing external data with basic pixel elements for editing, and importing the graphs of each zone, then merging and encrypting them to form a main storage area; at the same time, processing colors, sound or 3D graphs as required; 6. scanning and analyzing rows and columns after filling the main storage area, then carrying out location and error correction automatically; 7. generating the image boundary by computing to distinguish the main storage recognition area; 8. erasing incorrect pixel code and internal and external messy code of graphs; 9. outputting a complete pixel graph.

Description

A pixel image as well as its processing method and application system

Field of the invention

The present invention relates to the field of computer information processing technology, particularly to a pixel image as well as its processing method and application system.

Description of the prior arts

At present, in the commonly-used computer recognition and anti-counterfeit technologies, such as graph recognition, barcode recognition and QR code anti-counterfeit labels, the barcode is a set consisting of the black bars (bar for short) and white bars (blank for short) arranged in a certain order. It can represent specific characters, figures or symbols. The one-dimensional barcode is composed of a series of characters, that is, 0 and 1, in different widths on the horizontal direction. These binary characters are further combined according to a certain rule for data storage and reading. The merits of one-dimensional barcode are high input speed, high accuracy, low cost and high reliability. However, the one-dimensional barcode also has the following deficits: small data capacity, which generally accommodates about 30 characters, containing only letters and figures. It cannot represent Chinese characters and image information. In addition, it is over large (leading to low space utilization) and is hard to recognize if it is damaged.

On the basis of one-dimensional barcode, the two-dimensional barcode technology was produced. It can store Chinese characters, figures and pictures, so it is far more widely used than the one-dimensional barcode, and undergoes continuous development. At present, the two-dimensional barcode is divided into 2D stacked code and 2D matrix code. In shape, the 2D stacked code is formed by piling up multiple pieces of short one-dimensional barcode; the 2D matrix code is composed in matrix mode. On the matrix-related element positions, “point” indicates the binary “1” , and “blank” indicates the binary “0” . The barcode is grouped by arranging the “Point” and “blank” . The typical matrix two-dimensional barcode includes: Code One, Maxi Code, QR Code, Data Matrix, etc. The most popular one is the Quick Response Code (QR Code, as shown in Figure 1) invented by a Japanese Masahiro Hara. It generally consists of location graph, format information, version information, data, and error correction information. With the advantages of large information capacity, high reliability, supporting a variety of texts including Chinese characters and images, and good security and anti-counterfeit performance, it is most widely used in the mobile payment field.

In general, the two-dimensional barcode can only be generated in a limited rectangular diagram, so it cannot be well combined with patterns in other shapes. In this process, it virtually needs a large-enough plane rectangle pattern to ensure that it can be used. In other words, the first method is to use the two-dimensional barcode to overwrite a complete and large-enough plane on a graph to make the two-dimensional barcode be a part of the graph; The other method is to enlarge the two-dimensional barcode to a bigger rectangle to include the whole pattern, making it a part of the two-dimensional barcode. Actually, these combination methods still use the routine technology of two-dimensional barcode both in form and in algorithm, without a substantive breakthrough. By taking Tencent’s round two-dimensional barcode (as Figure 2) as an example, it is still easy to see the location identifiers that compose the three points of the rectangle though the barcode is circular.

To perfectly combine patterns and identification code, it is necessary to develop a method for processing pixel image and a related application system. Through pixel processing, the identification data can be combined with patterns, such as the application to trademarks and logos. In this way, trademarks can become recognition code, and details of related companies and products can be obtained through scanning trademarks.

Currently, The computer-based image processing mainly includes image storage, transmission, encryption, optimization, enhancement and restoration. It is oriented to both displayable and non-displayable objects, namely, actual images or virtual mapping images. Simply speaking, the computer graphics is mainly to study spot, line, plane and body as well as the method of displaying visual information. At present, the computer image processing techniques are mostly based on image digitalization, for example, the digital camera and digital scanner can be used to sample and digitize an image to form a big multi-dimensional array, which includes a variety of digitalized numbers corresponding to the color value, gray level value, and coordinate value of the image. For the images existing in a computer, you can complete image digitalization through the techniques of image editing, enhancement, restoration and segmentation. After being digitalized, the image is further processed based on a scale (such as the binary, octal or hexadecimal) to generate new data or digital signals, and is parsed and encrypted to finally form image signals, which are computed, transmitted and saved by using the core processor of the computer. To put it in a simple way, the computer image processing technique is to demonstrate an image using a variety of digits and symbols. It is virtually to process a variety of data information but not to directly perform an operation on the image. The computer-based image processing is generally to process the text files saved in disks. In practice, it is carried out on the underlying binary text files. Disk parse and storage are physically performed in the binary mode. Before the image enters the computer processor, it is computed only through the binary mode. In other words, in the traditional computer image processing methods, data is parsed and edited in a single scale, that is, only one scale (binary, octal or hexadecimal) is used in the whole image computing process, and the parse, analysis, explanation and storage for image content carried out in the underlying data processing core (CPU) of computers all adopt the binary mode.

Although the above mentioned processing mode can dispose and operate data in a relatively standard manner, it cannot use data more reasonably, efficiently and safely. Due to the fixed number of bits and scale, this processing mode may result in much long and useless information, and invalid and messy code, thereby increasing the storage load and slowing the computing rate.

In conclusion, as the existing image processing technique is to parse and edit data in a single scale (the binary, octal or hexadecimal) , it can process the unit data of images only based on a single during operation. Therefore, its safety, storage usage and parse rate are not high. In this context, it is necessary to design a new mixed-scale image information processing method, which can process the unity elements of images with a variety of combinations and arrangements, parse and edit data with a better method by utilizing scale differences, and more safely and efficiently finish the parse and storage operations.

Summary of the invention

Part I

One of the objects of the present invention is to provide a pixel image as well as its processing method and application system. The primary technical roadmap is to develop a new technology of recognizing pixel images to defend against forgery through image pixelate and whole-graph extension and deformation. The pixel image obtained thereof supports all functions of the two-dimensional code reading and recognition method and other graph reading and recognition methods. It is more dirty resistant, and has better error correction capacity, encryption security and scalability.

The technical solution of the present invention is to provide a pixel image as well as its processing method and application system

I. Pixel image

The present invention provides a sort of pixel images, which is characterized in pixelated data editing for images, and is a kind of pixel-based identified images that can be translated into various geometric graphs and vertical matrix graphs via an advanced logic calculation method. Such pixel images support multi-shape extension and vertical identification that the barcode and two-dimensional code do not have. They are composed of five basic elements, including the black point, white point, geometric point, half-angle point and full-angle point, wherein the black and white points are used to represent data filling information (for example, the black point represents “1” and the white point represents “0” ) ; the rest are all called auxiliary points. The combinations of the basic elements are used to represent vector directions, pixel densities, start and stop, location, fault tolerance, logical relationship, etc.

The functions of the black and white points above are similar to those of the black and white bars in the present two-dimensional technology and will not be repeated herein. In the present invention, the symbols of pixel image auxiliary points indicate different orders and meanings, that is, the symbols of different auxiliary points and the quantity of symbols are used to indicate different spatial and numeric variables, which match the orders of space, location, direction and data logic. Figure 3 is just a detailed drawing of a pixel image instance based on the present invention, which includes the five kinds of basic elements: black point, white point, geometric point, half-angle point and full-angle point, wherein the half-angle point indicates a hollow triangle, the full-angle point indicates a solid triangle, solid triangle with vertical lines or solid triangle with diamonds; the geometric point indicates a square, trapezoid, rhombus, black bar or heart shape. The meanings of all the auxiliary points are defined as follows:

1. “Triangles ” represented by the full-angle point:

indicate direction information, for example:

Two triangles pointing to the same direction represent the direction of data generation and identification;

Three triangles pointing to the same direction represent the image and scanning direction;

Four triangles pointing to the same direction represent the error correction direction.

2. “Triangles with vertical lines” or “Triangles with diamonds” represented by the full-angle point:

indicate the information about reading state, for example:

One “triangle with vertical lines” represents reading starts;

Two “triangles with vertical lines” represent reading resumes after a pause;

One “triangle with diamonds” represents reading stops;

Two “triangles with diamonds” represent reading is finished. (The graph reading orders work similarly as recorder buttons. )

3. “Hollow triangles ” represented by the half-angle point:

indicate pixel density information, for example:

The value of density ranges from 1 to 10, namely ten levels. When a hollow triangle is added, the unit density is enhanced to the next higher level.

4. “Trapezoids” or “rhombuses” represented by the geometric point:

indicate reading modes: for example:

One or two repeated combinations of “trapezoids” or “rhombuses” represent you can start to read the data in the main storage area;

Three repeated combinations of “trapezoids” or “rhombuses” represent you can read the data of the main storage area after identifying the reading direction;

Four repeated combinations of “trapezoids” or “rhombuses” represent you can read the data of main storage area after locating the related graph.

5. “Heart-shaped symbol” represented by the geometric point:

indicate fault tolerance information, for example:

One solid “heart-shaped symbol” represents the data and graph computing in an intact main storage area, which can be performed only once;

Two repeated solid “heart-shaped symbols” represent the data and graph computing in more than two main storage areas, which can be performed for two times;

Three repeated solid “heart-shaped symbols” represent the data and graph computing compatible with the main storage area, which can be performed for many times;

One hollow “heart-shaped symbol” represents the data and key computing in an intact main storage area, which becomes compatible after decryption.

II. An image data processing method

includes the following steps:

S1. Discern and classify the geometric shape of the target pattern;

The geometric shape isclassified into triangle, rectangle, circle, polygon, trapezoid, rhombus, annulus, box, building, figure, face, animals, Chinese letters, English letters, Arabic numerals, eye, hand, fingerprint, ware, flower, cloud, map, note, etc;

S2. Divide the target pattern into different zones according to the classification result ofS1, and generate the ranking orders and interval values of rows and columns. Zones can be divided from top to bottom or from left to right, making the sizes and quantity of the zones adapt to the requirements of graph changes. During the ranking, the zones can be numbered with Q1, Q2 and the like, so as to divide the target pattern into a specific number of zones;

S3. Sort the pixel of each zone through the treatment of S2 and carry out data logic operation. Compute the value of each location according to the location size, which is measured by location area, that is, the location value is equal to the location area, and the location number matches a unique location value. At the same time, group the external data by zone to form multiple data modules, which can be numbered with A1, A2, A3 and the like. The number of each data module matches a unique zone number. For example, zone Q1 corresponds to module A1, and zone Q2 to module A2, and so on;

S4. Compute the basic pixel graph and pixel application data value based on the result of S3. The pixel application data value mainly includes zone pixel density and externally-parsed pixel format information, wherein the zone pixel density is determined by the ratio of the zone value and the pixel value contained in the corresponding data module;

Formula: Pixel density M = Pixel value A/Zone value Q,

Example: Zone value Q2=100 Pixel value A2 =100 Pixel density M=1;

that is, Q100/A100=M1.

While the pixel application data value is being processed, the information about internal fuzzy location and error correction data relationship is generated;

S5. Edit external data with the basic elements of the pixel image of the present invention according to the basic pixel graph and pixel application data value obtained through S4, that is, parse external data into the data in pixel format, allocate the pixel data to each data module according to data volume, and finally import the pixel data into the matching location graph (that is, the pixel filling process) to form the main storage area. Merge all filled location images to form an intact pixel image;

When external data is edited to the data of pixel format, encryption is preferred. In the process of importing pixel data into various location graphs to form characters, pictures or images, encryption can also be used;

S6. When filling pixels in the main storage area and after the operation, you can scan the rows and columns of pixel graphs for analysis to design the function of automatic location and error correction;

S7. Compute the boundary of the graph processed through S6, which is used to distinguish the main storage area;

S8. After comprehensively processing the graph obtained through S7, clean up the incorrect pixel code and the messy code of graphs;

S9. After the series of operations including geometric graph classification, zone division, density computing, logic operation, fuzzy location, filling-in, automatic location and error correction, boundary formation, messy code clearance, you can finally obtain and output an intact pixel pattern;

The pixel pattern generated with the above-mentioned method is a two-dimensional graph containing characters, figures and texts, or a three-dimensional image combining colors and sound.

In the method above, the data logic operation includes not only the steps of analyzing, optimizing, recognizing, and reading pixels and link data, scanning OR code and pictures and encryption, but also the process of edit, encoding, programming, group encoding, parse, decryption for the pixel and link data.

In the method above, the data logic operations involved are divided into row merge logic and column merge logic. Horizontally, the method supports 1 to 999 rows of combination operation; vertically, the method supports 1 to 999 columns of combination operation. It can also perform 1 to 999 data bits of combination operation, and 2-to 999-scale of combination operation. The operation modes include vector operation, variable operation, function operation, linear operation, multi-scale operation, cross-scale operation, secret key operation, and fuzzy operation.

In the method above, before using the basic elements of pixel images to edit external data, translate the characters, letters and Chinese characters into pixel constants and then import them to the data module.

In the method above, the following error correction and fault tolerance mechanism can be used:

1. Error correction and fault tolerance in reading:

Error correction and fault tolerance are realized by combining the positions of the black bar symbol and half-angle “hollow triangle” . When the black bar symbol appears before the “hollow triangle” , the data being scanned and read is mainly the data on the prior location; when the black bar symbol appears after the “hollow triangle” , the data being scanned and read is mainly the data on the later location.

2. Error correction and fault tolerance during import:

Carry out error correction when importing location pixel data. If the imported data is not consistent with the pixel density value, the system will automatically return and repeatedly parse data. Then the system will generate new pixel data and import it again.

3. Error correction and fault tolerance during generation:

In consideration of the factors like the actually-used error correction and tolerance mechanism and boundary loss or stain, you can adjust the density difference to below 0.9, that is, on each 100 zones, you can allocate 90 pieces of pixel data at the most to ensure an effective sample rate to be obtained during read and recognition.

In the method above, you can use the following location mechanism:

1. Location by combining the geometric-point “trapezoids” or “rhombuses” :

Through one or two repeated combinations of “trapezoids” or “rhombuses” , the system can directly read the data in the main storage area;

Through three repeated combinations of “trapezoids” or “rhombuses” , the system can read the data of the main storage area again after identifying the direction.

2. Read and location mechanism

The system completes the operations of reading, identification and scanning by using continuous symbols, such as the combinations of continuous "trapezoids" or "rhombuses" . Whenever it reads the continuous symbol, it can automatically associate to the data in the main storage area for reading.

3. Location rule setting

When location is performed during the generation of pixel images, the “triangle” represented by the full-angle symbol will be used usually to represent the direction. For example:

If the main storage area is on the left of the “triangle” represented by the vertical full-angle point, the “trapezoid” or “rhombus” is set on the right of the full-angle point;

If the main storage area is on the right of the “triangle” represented by the vertical full-angle point, the “trapezoid” or “rhombus” is set on the left of the full-angle point;

If the main storage area is above the “triangle” represented by the vertical full-angle point, the “trapezoid” or “rhombus” is set below the full-angle point.

III. A pixel image application system

includes a software system used to generate the pixel image and a scanning device to scan the pixel image and explains its contents, wherein the scanning device can be an intellectual terminal or a computer peripheral device.

The software system contains the functions of encryption, code reading, recognition, scanning, code and drawing scanning, decryption, parse, decoding and so on.

Compared with the prior arts, the technical solution of the present invention has the following advantages:

The pixel image supports boundary extension and dynamic recognition, which is a breakthrough to the composition form and operate mode of barcode and two-dimensional code. The pixel image can technically pixelate geometric graphs, text graphs, human faces and dynamic figures. The process of recognizing and reading pixel images is completed at an ultra high speed, so it takes a shorter period of time. The equipment using CCD 2D barcode for reading can only read 50 pixel image symbols containing 150 characters per second, three barcode symbols containing the same data information per second, and 10 to 30 symbols of Data Matrix graphs. The logical operation of pixel image greatly enhances the operation and recognition speed. Due to good reading and writing capabilities, it is widely used in the fields of IT system and software system, and comprehensively improves the capacity of anti-counterfeit recognition. The merits of pixel image are concluded as follows:

1. Compared with the existing two-dimensional code, the pixel image of the present invention features more basic elements, which can be used to indicate a wider range of information. The common two-dimensional code is generally composed of two basic elements, namely, the black point and white point; while the pixel image of the present invention can be composed of five basic elements, that is, the black point, white point, geometric point, half-angle point and full-angle point. The present invention supports the combinations of the five basic digital elements, which can be performed through not only the binary encoding mode, but also the multi-scale encoding or cross-scale encoding mode. Therefore, it can generate a more variety of combinations and arrangements, obtain more precise numeric results, and represent a wider range of information.

2. The existing two-dimensional code usually consists of the storage recognition area, location area, and error correction area, so as to generate complete application data graphs. The pixel image of the present invention can generate complete application data graphs by only using the storage recognition area (corresponding to the main storage area) , without the need of the location area of two-dimensional code, enabling pixel images to be generated in any kinds and shapes of patterns and layouts, with larger flexibility and changeability. The storage recognition area of pixel image can directly complete location and error correction, with higher error correction rate and error-tolerant rate.

3. The pixel image can be used to generate any full-graph pixel pictures, with the repeat probability to be 1/1,000,000. Due to the higher security, it can fully meet the security requirements of the inter-bank payment system.

4. The pixel image can be used to fill both two-dimensional and three-dimensional figures.

Part II

In the “image data processing method” in Part I, data logic operation is required. For example in S5, external data need to be edited so as to generate images or graphic data in corresponding format. To achieve that, the single scale has always been applied to conduct operation in the existing techniques, whose safety, storage usage and parse rate are not high. To address this problem, the present invention is to provide a pluralistic mixed-scale image data processing method, featuring in the optimized parse function, multiple encryption function and class-based compression and storage function that are not available in the single-scale parse and processing method. It can process data in the multi-combination and cross-scale mode and use the scale difference to obtain a more efficient method for encryption, compression, parse and edit so as to improve the usage and operation efficiency of data and process the image information and related file information more safely and systematacially. Besides, it can improve data security, greatly cut down data occupancy, save data space, reduce the load on computer hardware and software as well as on system operation, and offer a new operation method and computing model, enabling the computer to process information more efficiently and rapidly in the links of security assurance, encryption, storage, compression, operation, display, and transmission.

The technical solution of the present invention is to provide a pluralistic mixed-scale image data processing method, which includes the steps of:

(1) Divide the object according to the graph size, type and depth of color, and distribution relation of the object. The said object includes one or more types of data in the scope of Arabic numerals, English letters, punctuations, symbols, graphs and characters; and the said characters include other types of charactersassociated with compilation and translation, such as Arabic numerals and English letters. The whole object is divided into several sub-objects.

(2) Judge the application parameters, including the type, size, density, color difference, shape, content and applicable scenario of the object. The values of application parameters can embody the level and type of the object. The level is expressed by a digit in the range of 0 through 99; the type is expressed by an English letter in the range of A through Z.

(3) Decompose the object into the basic unity elements that can be processed in digital mode. The said basic unity elements classified in shape include figures, letters, characters, lumps, spots, bars, balls, animals and plants and other irregular geometries. Further even, they are composed of different color lumps, which are black, white, gray and colorful.

(4) Compute the basic unity elements obtained through step 3 based on different scales and different numbers of bits to generate computing parameters for the object, wherein the scale ranges from binary to the 1024 scale; and the number of bits ranges from 2 to 9999.

(5) Set the function, logic and algorithm for image processing and carry out an operation by using the computing parameters obtained through step 4. Process the target data based on different scales and generate the partial image.

The formula is that: M=R (X) r (1) ,

wherein, “M” represents the resulting value, which is formed by arranging the black, white and gray color lumps of the basic unity elements;

“X” represents the target numeric value, which can be composed by figures (0 to 9999) and English letters (totaling to 10, 026different values) , and will be allocated according to different objects and different computer control orders;

“r” represents a multi-scale numeric value, which is in one of the scales from binary to the 1024 scale (totaling to 1, 023different values) , and are allocated according to different objects and different computer control orders;

“R” represents the number of bits, which is one of the values from 2 to9999 (totaling to 9,998 different values) , and are allocated according to different objects and different computer control orders.

In the computing above, the scale can be transformed from high to low or from low to high, and the generation mode can be set to symmetry or asymmetry. The computing methods include fuzzy algorithm, abstract algorithm, transformation algorithm, optimization algorithm, logical algorithm, and intelligent algorithm.

(6) Merge the partial images obtained by respectively processing each sub-object to generate an intact image, which is composed of the black, white and gray color lumps of the basic unity elements. The black, white and gray color lumps indicate different data information. For example, the white color lump indicates “0” ; the gray one indicates “1” ; and the black one indicates “2” . The black, white and gray color lumps can be arranged based on different scales and different numbers of bits to indicate many kinds of data results.

(7) After the intact image is generated, perform self-examination, review and error correction for it.

(8) Output the intact image.

Further even, the said method is not only suitable for processing general image data, but also for processing the data of stereo images, mapping images, dynamic images, waveband and frequency band, and other electronic data.

The invention is to provide an application system of image data processing method based on pluralistic mixed-scale, which includes the software and hardware parts; the said image is obtained through the method above, that is, the image is obtained by processing data based on different scales and different numbers of bits, which can be explained as formula (1) ,

wherein “M” represents the resulting value obtained through the formula, which is the image formed by arranging the black, white and gray color lumps of the basic unity elements;

“X” represents the target numericvalue, which can be composed by figures (0 to 9999) and English letters (totaling to 10,026different values) , and can be allocated according to different objects and different computer control orders;

“r” represents a multi-scale numeric value, which is one of the scales from binary to the 1024 scale (totaling to 1,023 different values) , and can be allocated according to different objects and different computer control orders;

“R” represents the number of bits, which is one of the values from 2 to9999 (totaling to 9,998 different values) , and can be allocated according to different objects and different computer control orders.

The resulting value “M” consists of the black, white and gray color lumps, which indicate different data information. For example, the white color lump indicates “0” ; the gray one indicates “1” ; and the black one indicates “2” . The black, white and gray color lumps can be arranged based on different scales and different numbers of bits to indicate many kinds of data results.

The target numeric value “X” is expressed with the black, white and gray color lumps, wherein the gray color lump can express 256 different gray values with the increase of color depth, and the black and white ones can express two different value, so “X” can be expressed by a total of 258 kinds of color lumps. For example, if four different numeric values are needed, you can use a black, gray, light gray and white color lumps to express them, mapping to the figures 4, 3, 2 and 1. If five different numeric values are needed, you can use a black, deep gray, medium gray, light gray and white color lumps to express them, mapping to the figures 5, 4, 3, 2 and 1. They can be used to distinguish from the variables of color, coordinate, and position of the original image.

“R” represents the number of bits, which can be set to a specific value according to the function and variable requirements. If it is set to “8” , the target numeric value is a 8-bit figure; if set to “10” , the target numeric value is a 10-bit figure.

“r” represents a scale, which can be set to a specific value according to the function and variable requirements. If it is set to “2” , the target numeric value will be parsed in the binary mode. In this mode, if the target numeric value is set to “4” , it will be parsed into “0100” . If “r” is set to “8” , the target numeric value will be parsed in the octal mode. In this mode, if the target numeric value is set to “4” , it will be parsed into “0004” .

Further even, the software of the application system is partially used for parse, optimization, recognition, reading, scanning, code scanning, image scanning, encryption, locking and compression, as well as editing, encoding, programming, decoding, translation, decryption, unlocking and decompression for the image and its linking data.

Further even, the hardware of the application system includes the operation system, storage system, display system and communication system, which can be intelligent terminals or computer peripherals.

The beneficial effects are as follows: the pluralistic mixed-scale operation method and its image and application system can be used to edit data based on different scales and different numbers of bits according to the different types and characteristics of the image unity information, so as to reasonably allocate data resources and optimize data operation, thereby using the data more safely and efficiently in the links of storage, transmission, encryption, compression, decryption and parse. The mixed-scale operation method can separate invalid information from operation, which greatly reduces invalid code and messy code, saves storage space and increases the computing rate. Besides, the mixed-scale data processing method is more beneficial to data encryption and transmission, thereby greatly improving the security. The unit data after being parsed and optimized based on different scales can save the running space, and can be used to execute more tasks in connection with the hardware data, thereby improving the operation efficiency.

Instructions with pictures

Figure 1 is the standard schematic diagram of the existing Quick Response Code (QR Code) ;

Figure 2 is the schematic diagram of the improved round Quick Response Code;

Figure 3 is an enlarged details drawing of the pixel image instance of the present invention;

Figure 4 is the flow of the pixel image processing method of the present invention;

Figure 5 is the overall schematic diagram of the pixel image processing method of the present invention;

Figure 6 is the target graph of the Embodiment1 of the present invention;

Figure 7 shows the classes and zones of the target graph generated in the Embodiment 1 of the present invention, that is, the schematic diagram of S1;

Figure 8 shows the zone-based encoding performed in the Embodiment1 of the present invention, that is, the schematic diagram of S2 through S4;

Figure 9 shows the process of filling the target graph and generating the pixel image in the Embodiment 1 of the present invention, that is, the schematic diagram of S5 through S8;

Figure 10 shows the intact pixel image finally generated in the Embodiment 1 of the present invention;

Figure 11 is the enlarged details drawing of the Embodiment 1 of the present invention;

Figure 12 shows the flow of the image processing method of the Embodiment 2 of the present invention;

Figure 13 is the schematic diagram of object division in Embodiment 2 of the present invention;

Figure 14 is the schematic diagram of separating unity elements in Embodiment 2 of the present invention;

Figure 15 is the schematic diagram of generating computing parameters in Embodiment 2 of the present invention;

Figure 16 is the schematic diagram of processing the target data to obtain three group images in Embodiment 2 of the present invention;

Figure 17 is the schematic diagram of merging the three group images in Embodiment 2 of the present invention;

Figure 18 is the flow of the whole processing method in Embodiment 2 of the present invention;

Figure 19 is the flow of the whole processing method in Embodiment 3 of the present invention;

In the figures above, “1” represents the black point; “2” represents the white point; “3” represents the full-angle point; “4” represents the half-angle point; “5” represents the geometric point; “6” represents the main storage area.

Practice of the present invention

The following part will combine the figures of the instructions and an instance to explain the present invention in detail. The technologies involved but not detailed in the instance are all the prior arts.

Embodiment 1

As shown in Figure 4-11, this instance shows the process of generating a pixel image in the geometric shape of dove (as shown in Figure 6) , which includes the following steps:

S1. Discern and classify the geometric shape of the target pattern, which is identified in the shape of dove;

S2. As shown in Figure 7, divide the target pattern into different zones and generate the sequence numbers of rows and columns and the interval values. In this instance, through data analysis, the target pattern is divided into three zones from top to bottom, which are numbered as Q1, Q2 and Q3, as shown in Figure 8;

S3. Sort the pixel of each zone and perform logic operation according to the size, interval value and pixel density of each zone obtained through S2. This instance obtains three zone values (which are all assumed to be “100” to ease the operation) . At the same time, divide the external data into three data modules, which are marked as A1, A2 and A3 and respectively match the three zones;

S4. Combine the result of S3 to compute the basic pixel graph and the pixel density. In this instance, the pixel density of each zone is set to 1;

S5. According to the basic pixel graph and pixel application data values obtained through S4, edit the external data by using the basic elements of the pixel image of the present invention, that is, translate the external data into pixel data, allocate the pixel data to each data module according to the pixel density of each zone, and finally import the data to the corresponding zone graph (that is, the pixel filling process) to form a main storage area. In this instance, the data of modules A1, A2 and A3 are respectively imported in zones Q1, Q2 and Q3, as shown in Figure 9. Then merge all filled zone graphs according to the zone number to initially form an intact pixel graph;

S6. After filling pixel in the main storage area, scan the rows and columns of the pixel graph, and complete the entire design of the function of automatic location and error correction;

S7. Compute the boundary for the graph processed through S6 to generate the graph boundary, which is used to distinguish the main storage area;

S8. Erase the pixel error code and the messy code in the graph;

S9. Output an intact and correct pixel image, as shown in Figure 10.

In S5, the basic elements of the pixel image include the black point, white point, geometric point, half-angle point and full-angle point, wherein the black and white points are about data and computing mode, and the geometric point, half-angle point and full-angle point are all called auxiliary points. They can be combined to indicate the vector direction, pixel density, start and stop, location, fault tolerance and logical relationship.

The pixel image application system of the present invention includes the pixel image software system and the matching hardware (such as smart phones) . It works the same as the existing QR Code. It firstly produces a pixel image through the pixel data processing method said above, and then combines the pixel image with other products or services. Users can use a smart phone equipped with the pixel image software system to scan pixel patterns. The software will automatically restore and process the external data stored in the smart phone. The pixel image features all practical functions of the two-dimensional code, and enhances the recognition speed, capacity and fault-tolerant rate.

Embodiment 2

Figure 12 is the overall flow of the processing method of the present invention. Embodiment 2 of the present invention takes the matrix graph composed of letters and figures as an example to describe the method of the present invention;

Figure 13 shows how to divide the object. In Embodiment 2 of the present invention, the object is divided into three groups, namely, W1, W2 and W3, which respectively consists of “ABC” , “123” and “D45” ;

As shown in Figure 14, the object of this Embodiment of the present invention is only composed of letters and figures. Through an analysis, the three groups are all parsed in the binary mode and form three groups of binary code. The letters and figures are all basic unity elements, so in this Embodiment, the binary code can be used as basic unity elements;

As shown in Figure 15, the three groups can be processed in different scales as required to generate different computing parameters. For example, the basic unity elements of the three groups formed in Embodiment 2 are further translated into the ternary, binary and ternary computing parameters, which are digital parameters.

Figure 16 shows the process of setting the function, logic, algorithm used for image processing as required, performing the mixed-scale operation by using the computing parameters obtained through the last step, and computing with formula M=R (X) r to obtain the resulting images of the three groups respectively.

Figure 17 shows the process of merging the images generated in the last step to form an intact image, and automatically finishing review and error correction to finally output the intact image.

Embodiment 3

As shown in Figure 19, Embodiment 3 of the present invention is to process the colorful image. Compared with Embodiment 2, the main difference of Embodiment 3 is that the basic unity elements separated out from the object are composed of the black, white and gray color lumps. For this difference, the technicians have clearly explained the solution of Embodiment 3 by combining Figure 19 and the description of Embodiment 2, so it is not repeated herein.

Finally, it is necessary to explain that selecting and describing the instance in the instructions are to better explain the principle and practical applications of the present invention. The instance is only used to help describe the present invention. It does not include all details and is not the sole instance of the present invention. Obviously, it can be modified and changed a lot according to the contents of the instructions, enabling the technicians in related fields to better understand and utilize the present invention. The protective scope of the present invention patent is still subject to the Claims and its full scope and equivalents.

Claims

It is a method for processing image data so as to generate the pixel image consisting of the basic elements with basic points, which are classified into black points and white points and are used to store or compute data. It is characterized in that: the basic elements contain auxiliary points, which are divided into the half-angle point, full-angle point and geometric point; the auxiliary point represents different orders or information according to different combinations of their shapes and quantities; the said half-angle point represents a hollow triangle, which is used to indicate graph density; the full-angle point represents the solid triangle, solid triangle with vertical lines, and solid triangle plus diamonds, and can be combined in different modes to indicate data generation and identification direction, scanning direction, error correction direction and the status information of starting or stopping data reading; the geometric point represents the square, trapezoid, rhombus, black bar and heart shape. The combination of geometric points can represent fault tolerance information, compatibility information, and the main storage area information. The processing method is characterized in the following steps:

S1. Discern and classify the geometric shape of the target pattern;

S2. Divide the target pattern into different zones according to the result of S1, and generate ordering numbers and interval values of rows and columns. In this method, the zones are divided from top to bottom or from left to right, making the sizes and numbers of zones cater to the changes of graphs;

S3. Sort the pixel of the zones through the treatment of S2 and carry out data logic operation. Compute the numeric value of each zone according to the zone size, which is measured by zone area, that is, the zone value is equal to the zone area, and the zone number matches a unique zone value; at the same time, group the external data by zone to form multiple data modules. The number of each data module matches a unique zone number;

S4. Compute the basic pixel graph and pixel application data value based on the result of S3. The said pixel application data value mainly includes zone pixel density and externally-parsed pixel format information, wherein the zone pixel density is determined by the ratio of the zone value and the pixel value included in the corresponding data module;

S5. Edit external data with the basic elements according to the basic pixel graph and pixel application data value obtained through S4, that is, parse external data into the data in pixel format, allocate the pixel data to each data module, and finally import the pixel data into the matching zone graph to form the main storage area. Merge all filled zone graphs to initially form an intact pixel graph;

S6. After filling pixel in the main storage area, scan and analyze the rows and columns of the pixel graph, and complete the design of automatic location and error correctio;

S7. Compute the boundary of the pixel graph processed through S6, which is used to identify the main storage area;

S8. Erase incorrect pixel code and the messy code of graphs;

S9. Output the intact pixel image and pattern.
A method for processing image data as set forth in Claim 1 is characterized in that: the said S1 involves the following geometric shapes: triangle, rectangle, circle, polygon, trapezoid, rhombus, annulus, box, building form, figure, face, animal, Chinese letters, English letters, Arabic numerals, eye, hand, fingerprint, wave, flower, cloud, water drop, spiral, chain, line, map and note.
A method for processing image data as set forth in Claim 1 is characterized in that: the said data logic computing is divided into logic row merge and logic column merge. Horizontally, the method supports 1 to 999 rows of combination operation; vertically, it supports 1 to 999 columns of combination operation. It can also perform 1 to 999 data bits of combination operation, and 2 to 999scale of combination operation. The operation modes include vector operation, variable operation, function operation, linear operation, multi-scale operation, cross-scale operation, secret key operation, and fuzzy operation.
A method for processing image data as set forth in Claim 1 is characterized in that: the image generated by the said method includes two-and three-dimensional graphs; the said two-dimensional graph includes the combinations of characters, digits and text; and the said three-dimensional graph includes the combinations of colors, voice, solid figures and dynamic pictures.
A method for processing image data as set forth in Claim 1 is characterized in that: the said method can encrypt not only the content and characters of external data, but also the graph generated thereof.
A pixel image application system is characterized in that: comprise the application system that is used to generate pixel images through the method as claimed in Claim 1 and the scanning devices used to scan images and explain the image content. The said scanning devices include communication equipment, intellectual terminals or computer peripheral devices.
A pixel image application system as set forth in Claim 6 is characterized in that: additionally offer functions of encryption, reading code, identification, scanning, scanning code and images, decryption, parse, and decoding for pixel images.
A pluralistic mixed-scale image data processing method is characterized in that:

It includes the following steps:

(1) Divide the object, that is, divide the whole object into several groups according to the graph size, type and depth of color, and distribution relation of the object, which are named group objects;

(2) Translate each group object into the basic unity elements that can be processed in digital mode;

(3) Compute each of the group objects processed through step 2 based on different scales and different numbers of bits to generate computing parameters for the group object. The scale ranges from binary to the 1024 scale; and the number of bits ranges from 2 to 9999;

(4) Set the function, logic and algorithm, then carry out operation on each group object based on different scales by using the parameters obtained through step 3, and finally generate the partial image;

(5) Merge the partial images obtained by respectively processing each group object to generate an intact image;

(6) When the intact image is formed, perform self-examination, review and error correction;

(7) Output the intact image;

The said object is one or more types of data in the scope of Arabic numerals, English letters, punctuations, symbols, graphs and characters. The said basic unity element includes digits, letters and color lumps. In the said step 4, the formula M=R(X) r is used for operation, wherein “M” represents the resulting value, which is a partial image formed by arranging the black, white and gray color lumps of basic unity elements; “X” represents the target numeric value, which is composed of digits and English letters; “r” represents a multi-scale numeric value, in the range of2through1024; “R” represents the number of bits, in the range of 2 through 9999.