JPS6019826B2 - Image data encoding method - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION The present invention relates to a method for encoding map and other image data in an information processing device.

In particular, it relates to an encoding method for image data that is passed through logical operations between L data. In conventional image data encoding systems, images are separately sampled, encoded, and stored for each individual image.

For example, when accumulating map information, maps such as maps showing municipal boundaries, population density distribution, elevation distribution, school districts, and pond usage maps showing land use status, etc. Each time was coded separately and stored separately. For this reason, for example, if you want to find out the land area that satisfies the following conditions in city A, population density B people/k, school district C, and altitude D, you can create a distribution map for each content. This required a complex procedure of decoding, extracting regions that satisfy each condition along with position information, and then performing logical operations to obtain the final result. That is, in the conventional method, logical operation processing over multiple images is complicated and the processing time is long. The present invention is an improvement on this, and an object of the present invention is to provide an image data encoding method that can simplify logical operations between image data.

The present invention is characterized in that a plurality of pieces of image data having different information are superimposed in a corresponding position and are collectively encoded. The drawings of the embodiments will be described in detail below.

Figure 1 is a diagram showing an example of an image to be encoded.
00, 300, 400 represent the administrative division map, population density map, elevation distribution map, and school district, and 101, 201, 30
1,401 is the boundary line in each distribution map, 102,202
, 302, 402 are areas in each distribution map, 103, 2
03, 303, 403 are points at corresponding pixel positions in each distribution map, 500 is a coordinate diagram shown for explanation, 501
is the coordinate grid point.

In each distribution map shown in FIG. 1, only the boundary lines of the regions are expressed as binary figures, and the attributes of the regions (for example, administrative district name, population density, school name, etc.) are not included. Second
The figure is an explanatory diagram of an example of a method for specifying the attributes of the regions of each distribution map shown in Figure 1. 600 is a mock diagram of an attribute table, 610,
620, 630, 640'650 are respectively a coordinate column, an administrative district name column, a population density column, an altitude distribution column, and a school district column.

The coordinate field 610 stores the coordinates of the grid points 501 in the coordinate diagram 500 shown in FIG. Attribute columns 620 to 65 store the attributes of the area to which the coordinate points in the coordinate column 610 belong. The grid density on the coordinate map 500 is set as coarsely as possible as long as at least one grid point is included in each distribution map area. Next, a method of encoding such data will be described. Each distribution map shown in FIG. 1 is a binary pattern including only a boundary line, and is expressed by one bit per pixel.

Here, the pixels on the boundary line are set to "1", and the other pixels are set to "0".
encoded as . An image (
(hereinafter referred to as a superimposed image) has N bits/pixel.

This superimposed pixel is encoded and stored. Of course, it is also possible to apply compression processing to reduce the amount of data during this encoding. Next, we will explain how to manipulate image data when using the image data encoding method of the present invention.The image data encoding method of the present invention is mainly applicable to image data banks and image databases. .

In these systems, rather than processing a distribution map of one type of information over a wide range of areas, it is possible to clarify the nature of a specific region by using distribution maps of various different information for a specific partial region. In many cases, it is important to extract an image area that satisfies multiple conditions. An example of the above processing according to FIG. 1 is as follows.

[Processing example A]

Extraction processing of image regions that satisfy multiple conditions.

Administrative district name is i, population density is 1000 people/k, altitude 1
Extract an area with 00 skin and school district 2. First, the attribute table 600 is used to find coordinates that satisfy the above conditions. In the example of FIG. 2, shelf number 114 is found as a column that satisfies the conditions, and the coordinates (i,, J,) are determined. The superimposed image data is accessed using the determined coordinates (i,,j,). The superimposed image data is data of N bits/pixel (N=4 in FIG. 1), and the meaning of N bits per pixel is arbitrarily determined in the order of superimposing each distribution map. Third
The figure shows an example of the meaning of bits compared to Figure 1. The bit data of each binary image data of administrative district distribution, population density distribution, altitude distribution, and school district distribution are superimposed as "0" in the image.
Allocate to bit position, 1 bit position, 2 bit position, and 3 bit position. Since the data of the superimposed image at coordinates (j,,i,) is data within the area of all distribution maps, the data value is "0" (bit array is "0000"). While sequentially looking at the data of surrounding pixels around this point (i,,i,) on the superimposed image, the target area is extracted by extracting continuous areas whose values are OJ.1 When the extraction of one area is completed, a column that satisfies the given conditions on the attribute table 600 is detected again, and the value (i2, i2) of the coordinate column 610 is determined.

If the coordinate value (i2, i2) is included in the area that has already been determined, the next coordinate value that satisfies the condition (
i3, i3). If the coordinate value (j3, j3) is not included in the already calculated area, the coordinate value (j,, L
) to find a new area. Thereafter, in the same manner, all attribute table columns that satisfy the conditions are processed and the process ends. If there are two conditions related to school district and population density distribution, the pixel data is 0*0* (* is 0 or 1).
) can be found in the same way as above.

[Processing example B]

A process that reveals the properties of a given point (x, y) on an image.

Access the image data of the superimposed image using the given coordinates (x, y).

If the point is within the area of all distribution maps, it is outside the pixel data or "0"
(=0000)). Therefore, the grid point (i,
Find j). Using the detected lattice point coordinates (i, i), the attribute table 600 is searched and the value of the corresponding attribute column is obtained. For example, when the vicinity of grid point (i, 105) in Figure 2 is specified, if grid point (i, 105) and the specified point are in the same area, the property of the specified point is that it belongs to administrative district f. The population density is 2000 people/11, the altitude value is 100W, and the school district is RO. As described above, the basic processes of extracting a specific image area from given conditions and clarifying the nature of a given point on the image can be efficiently executed.

FIG. 4 is a block diagram of a device according to an embodiment of the present invention.

710 is an image data input unit; 72 is a calculation processing unit;
30 is a buffer memory, 740 is an image data file,
750 is a data input/output section. Image data input section 71
Starting from 0, each distribution with a unified size and magnification is input.

Line maps such as administrative district maps and school district maps can be viewed on tablet devices.
Further, for color distribution maps and the like, an input device suitable for the image, such as a flying spot scanner or a drum scanner, is used. The arithmetic processing unit 720 performs positioning processing for overlapping. If the input image is a line figure, it is processed as a binary pattern and stored in the buffer memory 730.
Store it in At the same time, data on the lattice points is input from the data input/output unit 750 using a keyboard, paper tape, or card, and is stored in the attribute table in the buffer memory 730. For a color image or a grayscale image, the same color area and density area are extracted, and the boundaries of these areas are extracted as a binary pattern and stored in a buffer memory. Further, color information and density information on the grid points are stored in the attribute table as attribute data. After performing the above processing on all necessary input distribution maps, bit information at the same position on each distribution map is extracted from the buffer memory 730, pixel data of N bits/pixel is configured, and image data is Store in file 740.

Further, the attribute table is stored in the image data file 7404 in association with the image data. Data compression processing is also performed on the superimposed image data within the arithmetic processing device as needed. Further, processing for image data is performed by a data input section 7.
By specifying logical operation conditions and addresses on the image from the keyboard 58, the arithmetic processing unit 72 performs the necessary processing according to the method described above, and the results are sent to the data input/output unit 760 and the image input/output unit 710. display on the display. In the case of large-scale image information such as maps, each distribution map may be divided into appropriate sizes, such as by Kanto region or prefecture, and a superimposed image may be created for each division unit. . In addition, when performing data compression encoding processing, if the superimposition processing described in the present invention is performed, 1
Since all the necessary distribution map data can be obtained by decoding once, the number of decodings can be reduced. In the above description, all the distribution maps to be superimposed are assumed to be binary patterns, but images that cannot be expressed as binary patterns can be superimposed as multi-valued images.

FIG. 5 is a diagram showing an example of the bit structure of pixel data of a superimposed image when a multivalued image is included.

In the example shown in FIG. 5, a multivalued image is expressed in qubits (so=3) and is superimposed on another binary image. At this time, 2, 3,
By interpreting the bit information at the 4-bit position as multivalued image data, logical operation processing can be performed in the same manner as described above. As explained above, by aligning the distribution maps of multiple types of different information, collapsing them, and encoding them all at once, it is possible to efficiently perform various logical operations between the distribution maps of different types of information. Become.

By applying the present invention to various image data banks and image data base systems, there is an advantage that the processing efficiency of the system can be improved.

[Brief explanation of the drawing]

Fig. 1 is a diagram showing examples of various distribution charts, Fig. 2 is a diagram showing an example of an attribute table, Fig. 3 is a bit configuration diagram of pixel data,
0 to 3 represent bit positions, A represents administrative district distribution, B represents population density distribution, C represents altitude distribution, and D represents school district distribution. FIG. 4 is a block diagram of a device according to an embodiment of the present invention, and FIG. 5 is a bit configuration diagram of pixel data (in the case of 3). 100...
Administrative district map, 101... Boundary line, 102... Area, 20
0...Population density distribution map, 201...Border line, 202...
- Boundary line, 203... Pixel position, 300... Elevation distribution map, 301... Boundary line, 302... Area, 303
... Pixel position, 400... School district map, 401... Boundary line, 402... Area, 403... Pixel position, 500...
... Coordinate map, 501... Grid point, 600... Attribute table, 610... Coordinate column, 620... Administrative district name column,
630...Population density column, 640...Elevation distribution column, 6
50... School district column, 710... Image data input/output section, 720... Arithmetic processing section, 730... Buffer memory, 740... Image data file, 750... Data input/output section. Figure 1 Figure 2 Figure 3 Figure 4 Figure 5

Claims (1)

[Claims] 1. Image data in which digital image data sampled from multiple types of images having different information contents are superimposed with corresponding positions, and the superimposed image data is collectively encoded. Encoding method. 2. In a method in which digital image data sampled from multiple types of images with different information contents are superimposed with corresponding positions, and the superimposed image data is collectively encoded, each image to be superimposed is Image data encoding characterized in that an attribute table representing data values corresponding to the above-mentioned positions is provided in a memory area, and control is performed using this attribute table to perform logical operation processing between image data to be superimposed. method.