CN111446968A - Vector space data multistage compression method - Google Patents
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Abstract
The disclosure belongs to the technical field of spatial information, and particularly relates to a method for multistage compression of vector spatial data. The spatial vector data compression method provided by the disclosure takes visualization of basic geographic spatial data as an application scene, sets the minimum resolution distance meeting the requirement of multiple scales as a precision requirement, realizes multilevel compression processing of vector spatial data, and further improves the data storage efficiency by combining grid filtering and binary offset storage.
Description
Technical Field
The disclosure belongs to the technical field of spatial information, and particularly relates to a method for multistage compression of vector spatial data.
Background
With the development of earth observation, navigation and positioning equipment and internet technology, the production capacity of various vector-type geographic spatial data is gradually enhanced, so that the data storage capacity is rapidly increased, and a new challenge is provided for the efficiency of spatial data storage, query, network transmission, visualization and other functions of a geographic information system and other application information systems.
As is well known, data compression can reduce data volume and improve the efficiency of each link of data processing, thereby better supporting spatial data application scenes including a mobile internet geographic information system.
The vector data model is one of the most basic geographic element entity expression models in the geographic information system. The method is characterized in that under a given geographic space coordinate system, geographic entities are abstractly expressed into geometric entities such as points, lines, surfaces and the like, the expression of the geographic entities is realized by recording the spatial coordinates of characteristic points and setting a set expression rule, and the method has the characteristics of complete description of entity targets, easy acquisition of spatial relationships and the like.
Disclosure of Invention
The invention aims to provide a vector space data multi-stage compression method. The method comprises the following specific processes:
bisection subdivision is carried out on the space expressed by the geographic coordinates step by step in the horizontal and vertical directions so as to obtain a multi-layer grid for dividing the geographic space;
determining the position of a coordinate point of data to be compressed in the grid, and continuously subdividing the grid so as to replace the coordinate value of the coordinate point of the data to be compressed by using the coordinate of the central point of the subdivided grid; the coordinate deviation between the center point of the subdivided grid and the coordinate point of the data to be compressed accords with a preset precision value of a multi-level scale;
setting a local coordinate reference system by taking a single data file to be compressed as a unit, and calculating the binary coordinate deviation of the coordinate point of the data to be compressed and the central point of the subdivided grid to obtain the binary offset;
storing geographic coordinates of the vector space data at the binary offset.
Further, when splitting is carried out in the horizontal and vertical directions, the left and right or up and down subspaces are split by adopting binary marks.
Further, the preset value is a minimum resolvable distance expressed under a multi-scale visual lossless condition.
Further, the step of determining the position of the coordinate point of the data to be compressed in the grid, and continuously subdividing the grid so as to replace the coordinate value of the coordinate point of the data to be compressed with the coordinate of the central point of the subdivided grid includes:
acquiring coordinate data of coordinate points of data to be compressed;
acquiring the minimum value and the maximum value of a grid latitude coverage range containing coordinate points of data to be compressed in the geographic space data;
judging whether the latitude coordinate of the coordinate point of the data to be compressed is larger than the middle value of the latitude coverage range;
if the latitude coordinate is larger than the middle value of the corresponding latitude coverage range, subdividing the grid to reduce the latitude coverage range of the grid to the middle value to the maximum value;
if the latitude coordinate is less than or equal to the middle value of the latitude coverage range, subdividing the grid to reduce the latitude coverage range of the grid to the minimum value to the middle value;
recursively dividing the grid into latitudes and likewise dividing the grid into longitudes so that the deviation between the central point of the grid and the coordinate point of the data to be compressed conforms to the preset value
Further, the maximum division error between the center point of the divided grid and the coordinate point of the data to be compressed is calculated by the following formula:
wherein Width is the Width of the grid, Height is the Height of the grid, and Scale is a Scale.
Further, the Width of the grid and the Height of the grid are calculated by the following formulas:
Width＝width/2n；
Height＝height/2n。
further, when the data to be compressed is a point vector element, the binary offset of the point vector element is calculated as follows:
the point vector elements are rearranged in sequence according to the binary code;
recording a first point as an original binary code, storing the latter point as the number of grids deviated from the former point, and converting the grid number into binary storage;
the length of the offset binary code is larger than 1 and smaller than that of the original binary code, and the length of the offset binary code is uniformly used as the maximum length of all the lengths of the offset binary code;
for records with insufficient offset bits, the front end of the binary sequence is padded with 0.
Further, when the data to be compressed is a line vector element or a plane vector element, the binary offset of the line vector element or the plane vector element is calculated as follows:
recording the node number and the initial point coordinate of the element object in each vector element, and performing offset calculation by taking the element object as a unit;
setting a cubic bit, calculating the offset direction of each coordinate point and the previous coordinate point, and storing eight offset directions which may be generated by each point by using two-bit binary coding;
calculating offset, recording the offset of the row and the column along the horizontal direction and the vertical direction respectively, converting the offset into binary codes and then alternately storing the binary codes as final offset;
the length of the offset binary code is greater than 1 and smaller than that of the original binary code, and the length of the uniform offset binary code is the maximum length of all the offset binary code lengths;
for records with insufficient offset bits, padding is performed at the front with 0.
The space vector data compression method provided by the disclosure takes visualization of basic geographic space data as an application scene, sets the minimum resolution distance which meets lossless screen display vision as a precision requirement, realizes vector space data compression processing, and further improves the data storage efficiency by combining grid filtering and binary offset storage. The compression ratio is also higher compared to the prior art.
Drawings
FIG. 1 shows a flow diagram of a vector space data compression algorithm according to the present invention;
FIG. 2 is a schematic geospatial subdivision;
FIG. 3 is a schematic diagram of a mesh filtering process;
FIG. 4 is a maximum partition error evaluation diagram under different partition times;
FIG. 5 is a diagram illustrating a binary offset storage format of a dot element;
FIG. 6 is a schematic diagram of a binary offset storage format for line and plane elements;
FIG. 7a original geographic information image;
FIG. 7b, FIG. 7c and FIG. 7d are the compression ratio results of 16, 18 and 20 bits respectively for the same number of splits;
FIG. 8 is a diagram illustrating compression results for different bit numbers.
Detailed Description
As can be seen from the above description, the vector data model is one of the most basic geographic element entity expression models in the geographic information system. The method realizes the expression of the geographic element entities by recording the spatial coordinates of the characteristic points and setting the set expression rules of the points, and has the characteristics of complete target description, easy acquisition of spatial relationship and the like.
Because the vector geographic space data uses high-precision coordinate numerical value expression set information, the vector geographic space data has higher structural precision than raster data and is suitable for expressing dynamic and multi-type complex geographic phenomena, and most of the expressions of the public geographic information and the positioning information of the Internet of things adopt a vector data model. For compression of vector data structures, many methods have been proposed. For example, patent document with application number 2010101806110 entitled "vector data college transmission method based on ordered point set pixel lossless compression" proposes a compression method based on ordered point set on the premise of pixel lossless; also, patent document No. 2014100243164 entitled "a method of compressing vector data" discloses a vector data compression method based on offset; further, the patent application No. 2015107723716 entitled "Scalable Vector Graphics compressing and rendering method and apparatus" proposes an optimized storage method for SVG (Scalable Vector Graphics) format. In the solutions disclosed in the above patent documents, corresponding solutions are proposed for the compression of the spatial vector data, but there still exist problems of limited compression ratio, complex storage format, and incapability of implementing multi-resolution compression.
In view of the above analysis, the inventor proposes a space vector data compression method through long-term creative work, which can effectively solve the technical problems proposed in the above analysis. According to the space vector data compression method, firstly, according to the coordinate position of the vector feature point, the geographic space is subjected to multiple times of staggered subdivision in the horizontal direction and the vertical direction, binary integer codes are sequentially constructed according to the position of the feature point coordinate after each subdivision, the number of subdivision is determined by set compression precision, the more the number of layers of subdivision is, the higher the expression precision is, the longer the corresponding code is, and further the purpose of multi-resolution vector data compression can be achieved. The result of the low-precision encoding of a given region is the prefix of the high-precision encoding of that region, so the longer the same length of encoded prefixes, the closer the distance in two grid spaces. The space vector data compression method provided by the disclosure takes visualization of basic geographic space data as an application scene, sets the minimum resolution distance which meets lossless screen display vision as a precision requirement, realizes vector space data compression processing, and further improves the data storage efficiency by combining grid filtering and binary offset storage. The compression ratio is also higher compared to the prior art.
The spatial data compression method proposed by the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. Advantages and features of the present invention will become apparent from the following description and from the claims. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is merely for the purpose of facilitating and distinctly claiming the embodiments of the present invention.
Spatial terms such as "below …", "below …", "below", "above", and the like are used in the detailed description of the disclosure for the purpose of easily describing the positional relationship of one component and another component shown in the drawings, but these are merely examples and are not intended to limit the present invention. In addition to the orientations shown in the figures, the spatial relationship terms are intended to encompass a variety of different orientations of the device in use or operation. The device may be otherwise oriented, such as rotated 90 degrees or at other orientations, and the spatially relative descriptors used herein interpreted accordingly.
The vector space data multistage compression method provided by the disclosure comprises the following steps:
step 1: according to the equipment display precision, the map scale and the display scale information, a plurality of Minimum Distinguishable Distances (MDDs) expressed by the vector space data under the condition of multi-scale visual lossless are determined, and the process mainly comprises the following implementation steps:
s11: for a display terminal with DPI (Dot Per lnch, DPI) display accuracy, there are DPI pixel points in a length of one inch. Under the international system of units, 1 meter represents 39.3701 Inches, which can be expressed in Inches per meter (Inches permamer, IPM). Then, the calculation formula of the international standard unit length (Merer Per Dots, MPD) that can be represented by a pixel point on the display terminal is as follows:
MPD＝1/(DPI×IPM)
(1)
this distance is used to represent the minimum resolvable distance that ensures visual non-loss during computer display.
S12: each map has map Scale (Scale) information, and the electronic map can be zoomed in and zoomed out according to Display requirements in the process of displaying in the computer, at the moment, the Display Scale (Dscale) of the map is changed, and the method for calculating the magnification (Ratio) in the process of displaying the map comprises the following steps:
Ratio＝Dscale/Scale
(2)
s13 from S11 we have already known the minimum distance that the computer pixel can resolve, and for the specific map displayed on the computer screen, the minimum resolvable distance of the actual ground distance expressed by the minimum resolvable distance conforms to the calculation formula:
MDD＝MPD×Dscale
(3)
＝MPD×Scale/Ratio
(4)
step 2: and (3) bisection subdivision is carried out on the space expressed by the geographic coordinates step by step along the horizontal direction and the vertical direction, the geographic space is divided into multi-level regular grids, and the subdivision schematic is shown in figure 2.
S21: and (3) performing staggered subdivision on the two-dimensional space along the horizontal direction and the vertical direction respectively, and marking the left and right or upper and lower subspaces after subdivision by adopting binary number '0' or '1'. After the two-dimensional space is divided along the horizontal direction, the left subspace is marked as '0', and the right subspace is marked as '1'; after the two-dimensional space is divided along the vertical direction, the lower subspace is marked as '0', and the upper subspace is marked as '1'. Based on Morton coding, binary identifiers "0" or "1" are alternately stored according to the sequence of left, right, then up and down, unique identifiers "00", "01", "10" and "11" of each grid are generated, and the geographic space is divided into regular four-grid grids.
S22: and for the four-grid generated in the S21, performing a subdivision process of S21 by taking each grid as a unit, wherein the grid generated once by subdivision is a layer, and new two-division identifications generated in the subdivision process are alternately stored and then connected with the two-division identification of the previous layer to form binary character strings corresponding to the grid positions one to one. The deeper the subdivision level is, the longer the binary string accumulation is, the smaller the range represented by the grid is, and the more accurate the position information expression is.
S23: after the geographic space is divided according to recursion, each area has a unique code corresponding to the geographic space, and has obvious layering characteristics in space, the prefixes of the codes of different levels in the same area are the same, and the higher the proximity degree of different areas is, the higher the prefix matching degree is.
And step 3: and determining the position of the coordinate point of the data to be compressed in the multi-level regular grid, and replacing the original double-precision type coordinate value with grid binary coding. And replacing the space vector data coordinate points falling in the grid with the central points of the regular grid generated by subdivision.
S31: for the coordinate point (117.67198438 ° E, 42.1855639 ° N) of the data to be compressed, the position determination step in the regular grid is specifically as follows:
s311: geospatial data latitude coverage is obtained at a minimum value of 40N to a maximum value of 44N.
S312: finding the middle value of the latitude coverage range, and judging whether the latitude coordinate is greater than the middle value.
S313: if the value is larger than the preset value, outputting a character '1', and reducing the coverage range to an intermediate value to a maximum value; otherwise, the character '0' is output, and the coverage is reduced to a minimum value to a middle value.
It is easily understood that the steps S312 and S313 are steps of dividing the grid latitude.
S314: and recursion steps S312 and S313, gradually approaching the latitude division to the accurate coordinate value, and splicing the output characters into a character string. When the number of splits is N-9, 42.1855639 ° N is converted into 100010111, which is a binary code.
S315: of course, the principle of longitude division is the same as latitude, and therefore, when the number of divisions n is 9, 117.67198438 ° E is converted into binary code 100111101
S316: binary codes of the latitude and longitude coordinates are alternately stored, binary codes 110000011101111011 of coordinate points (117.67198438 ° E, 42.1855639 ° N) are obtained, and positions of the coordinate points in the regular grid are also determined.
S32: after the geographic space is divided into regular grids, each coordinate point in the research area can fall into the unique grid in the classification, and the vector space data is simplified in a grid filtering mode: replacing all coordinate points of the same element object in the same grid by using the central point of the grid; for coordinate points of different element objects falling in the same grid, the grid center points are required to be replaced and stored respectively so as to ensure the integrity of the element objects. As shown in fig. 3, such a grid filtering method realizes compression of spatial vector data by reducing the number of coordinate points.
S33: the vector coordinate points are usually stored in a double-precision data type, and one coordinate value storage space occupies 16 bytes, 8 bits per byte and 96 bits of memory space. After vector coordinates are replaced by the grid central points through S32, the position information of the central points of the grids is stored by grid binary coding, the number of storage bits can be determined according to requirements, and the storage space is greatly saved.
And 4, step 4: and calculating errors generated by the regular subdivision, and determining subdivision levels and data storage precision on the premise that the maximum error generated by the subdivision is smaller than the minimum distinguishable distance, namely the requirement of visual non-damage is met.
S41: for a rectangular grid, the maximum distance from all points in the grid to the center point is half of the diagonal. Therefore, the central point of the grid is used to replace other points in the grid, and the maximum partition Error (ME) that can be generated is calculated by the following formula:
wherein Width is the Width of the grid, Height is the Height of the grid, and Scale is a Scale.
S42: the Width of the grid is Width, and is related to the Height of the grid and the subdivision times n:
Width＝width/2n
(6)
Height＝height/2n(7)
wherein width is the width of the drawing and height is the height of the drawing.
Substituting the expressions (6) and (7) into the final calculation formula (5) for obtaining the maximum division error:
as can be seen from equation (8), the larger the value of n, the smaller the maximum division error.
S43: and calculating the maximum division error under different n values, wherein the condition that ME is less than MDD is a necessary premise for visual lossless display of space vector data. The method comprises the following steps of displaying space vector data in a geographic information system, wherein the requirement of amplification display needs to be met, and a calculation formula of a magnification (Magnify) under the premise of no visual loss is as follows:
s44: on the premise of meeting the data display requirement, the minimum subdivision number n is determined, and further the data simplification degree of the space vector data, the number of bits of coordinate storage, the maximum division error under different n values and the map magnification are determined as shown in fig. 4.
And 5: setting a local coordinate reference system by taking a single data file to be compressed as a unit, carrying out binary offset calculation, and finally storing the geographic coordinates of the vector space data by using the binary offset. The data to be compressed can be divided into elements of appropriate point quantity, elements of line vector and elements of plane vector.
S51: for the point vector elements, the spatial distribution of the point vector elements has randomness, but each point is an independent object, the drawing sequence does not have any influence on the drawing result, and the specific steps are as follows.
S511: the point vector elements are rearranged in order according to the binary coding.
S512: and recording the first point as an original binary code, storing the subsequent points as the number of grids deviated from the previous point, and converting the grid number into binary storage.
S513: the length of the offset binary code will be greater than 1 and less than the length of the original binary code, and the offset binary code length will be unified to the maximum length of all the offset binary code lengths.
S514: for records with insufficient offset bits, the front end is filled with '0' to ensure the uniformity of offset bits
The specific storage format is shown in fig. 5.
S52: the coordinates of line elements or plane elements (boundaries) have relevance in terms of element objects, cannot be re-ordered and re-drawn like point elements, and need to be organized in terms of each object, and the specific steps are as follows:
s521: and recording the node number and the start point coordinates of the element object in each vector element, and performing offset calculation by taking the vector object as a unit.
S522: and setting a cubic bit, calculating the offset direction of each coordinate point and the previous coordinate point, and storing four offset directions which can be generated by each point by using two-bit binary coding.
S523: and calculating the offset, recording the offset of the row and the column along the horizontal direction and the vertical direction respectively, converting the offset into binary codes, and then alternately storing the binary codes as the final offset.
S524: the length of the offset binary code is larger than 1 and smaller than that of the original binary code, and the length of the uniform offset binary code is the maximum length of all the offset binary code lengths.
S525: for records with insufficient offset bits, the leading end is filled with "0", ensuring uniformity of the offset bits.
The specific storage format is shown in fig. 6.
In another aspect of the present disclosure, an apparatus is also provided, on which the steps of the vector space data multi-stage compression method disclosed in the present disclosure can be executed.
The vector space data multistage compression method provided by the present disclosure can be implemented by taking Java programming language as an implementation tool on a dell XPS8930 desktop computer configured with intel i7-9700K @3.6GHz, and in the embodiment, the vector space data multistage compression method is implemented by taking beijing and surrounding area 1: taking road vector data in a thematic map of administrative divisions of 100 ten thousand as an example, vector data compression calculation is carried out according to the implementation flow of the method shown in figure 1; then, an evaluation of the compression efficiency was made based on the different binary storage lengths.
Fig. 7 shows different Bit lengths generated under different subdivision times and the implemented vector space data compression ratio. FIG. 8 shows the compression effect pairs under different Bit lengths, and it can be seen from the graph that significant jaggies exist in the redrawn compression results when the Bit number reaches 16 and 18, and the requirement of visual lossless cannot be met, and when the Bit number reaches 20, the display requirement can be met at a ratio of 1: 100 ten thousand as the display scale. The compression ratio of the data can reach 97.18%. Through the verification, the vector space data compression algorithm provided by the patent has considerable compression efficiency.
In summary, the spatial vector data compression method provided by the present disclosure takes visualization of basic geospatial data as an application scenario, sets a minimum resolution distance that satisfies visual lossless screen display as a precision requirement, implements vector spatial data compression processing, and further improves data storage efficiency by combining grid filtering and binary offset storage. The compression ratio is also higher compared to the prior art.
The above description is only for the purpose of describing the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention, and any variations and modifications made by those skilled in the art based on the above disclosure are within the scope of the appended claims.
Claims (8)
1. A method of multi-stage compression of vector space data, the method comprising the steps of:
bisection subdivision is carried out on the space expressed by the geographic coordinates step by step in the horizontal and vertical directions so as to obtain a multi-layer grid for dividing the geographic space;
determining the position of a coordinate point of data to be compressed in the grid, and continuously subdividing the grid so as to replace the coordinate value of the coordinate point of the data to be compressed by using the coordinate of the central point of the subdivided grid; the coordinate deviation between the center point of the subdivided grid and the coordinate point of the data to be compressed accords with a preset value;
setting a local coordinate reference system by taking a single data file to be compressed as a unit, and calculating the binary coordinate deviation of the coordinate point of the data to be compressed and the central point of the subdivided grid to obtain the binary offset;
storing geographic coordinates of the vector space data at the binary offset.
2. The method of multi-stage compression of vector space data according to claim 1, wherein when splitting in horizontal and vertical directions, the left and right or up and down subspaces are split using binary notation.
3. The method of multi-stage compression of vector space data according to claim 1, wherein the plurality of preset values are minimum resolvable distances expressed under visually lossless conditions.
4. The method of multi-stage compression of vector space data according to claim 1, wherein the step of determining the positions of coordinate points of data to be compressed located in the mesh and continuously subdividing the mesh to replace the coordinate values of the coordinate points of the data to be compressed with the coordinates of the center point of the subdivided mesh comprises:
acquiring coordinate data of coordinate points of data to be compressed;
acquiring the minimum value and the maximum value of a grid latitude coverage range containing coordinate points of data to be compressed in the geographic space data;
judging whether the latitude coordinate of the coordinate point of the data to be compressed is larger than the middle value of the latitude coverage range;
if the latitude coordinate is larger than the middle value of the corresponding latitude coverage range, subdividing the grid to reduce the latitude coverage range of the grid to the middle value to the maximum value;
if the latitude coordinate is less than or equal to the middle value of the latitude coverage range, subdividing the grid to reduce the latitude coverage range of the grid to the minimum value to the middle value;
and recursively dividing the grid into latitudes, and similarly dividing the grid into longitudes so that the deviation between the central point of the grid and the coordinate point of the data to be compressed conforms to the preset value.
5. The multi-stage compression method for vector space data according to claim 1, wherein the maximum partition error between the center point of the subdivided mesh and the coordinate point of the data to be compressed is calculated by the following formula:
wherein Width is the Width of the grid, Height is the Height of the grid, and Scale is a Scale.
6. The method for multi-stage compression of vector space data according to claim 5, wherein the Width of the mesh and the Height of the mesh are calculated by the following equations:
Width＝width/2n；
Height＝height/2n。
7. the multi-stage compression method of vector space data according to claim 1, wherein when the data to be compressed is a point vector element, the binary offset calculation step of the point vector element is as follows:
the point vector elements are rearranged in sequence according to the binary code;
recording a first point as an original binary code, storing the latter point as the number of grids deviated from the former point, and converting the grid number into binary storage;
the length of the offset binary code is larger than 1 and smaller than that of the original binary code, and the length of the offset binary code is uniformly used as the maximum length of all the lengths of the offset binary code;
for records with insufficient offset bits, the binary code sequence is padded with 0 at the front.
8. The vector space data multi-stage compression method according to claim 1, wherein when the data to be compressed is a line vector element or a plane vector element, the binary offset calculation step of the line vector element or the plane vector element is as follows:
recording the node number and the initial point coordinate of an element object in each vector element, and performing offset calculation by taking the element object as a unit;
setting a cubic bit, calculating the offset direction of each coordinate point and the previous coordinate point, and storing eight offset directions which may be generated by each point by using two-bit binary coding;
calculating offset, recording the offset of the row and the column along the horizontal direction and the vertical direction respectively, converting the offset into binary codes and then alternately storing the binary codes as final offset;
the length of the offset binary code is greater than 1 and smaller than that of the original binary code, and the length of the uniform offset binary code is the maximum length of all the offset binary code lengths;
for records with insufficient offset bits, padding is performed at the front with 0.
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| CN116248126B (en) * | 2022-11-21 | 2024-04-16 | 华北理工大学 | Vector data compression method based on minimum length loss |
| CN117389972A (en) * | 2023-12-11 | 2024-01-12 | 云粒智慧科技有限公司 | Strip-shaped space data compression method and device, electronic equipment and storage medium |
| CN117389972B (en) * | 2023-12-11 | 2024-02-27 | 云粒智慧科技有限公司 | Strip-shaped space data compression method and device, electronic equipment and storage medium |
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