CN113709485A - Large-size image rapid and accurate compression method - Google Patents
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Abstract
The invention discloses a method for quickly and accurately compressing large-size images, which comprises a plane template arranged in image processing software, wherein a fixed point A is arranged at any position in the plane template, an orientation scale X and an orientation scale Y extend outwards along the fixed point A respectively, the orientation scale X and the orientation scale Y are vertical, a function coordinate system is formed between the orientation scale X and the orientation scale Y, and the fixed point A is the origin of the function coordinate system. In the process of compressing the image, the original image is divided into a plurality of pixel areas by a certain numerical value, a plurality of pixel areas are independently compressed, and each compressed pixel area is combined, so that the damage problems such as image distortion and the like can be greatly reduced after the compression processing of the whole image is completed, and the quality of the compressed image is ensured.
Description
Technical Field
The invention relates to the technical field of big data processing, in particular to a method for quickly and accurately compressing a large-size image.
Background
The reason why the image data can be compressed is that redundancy exists in the data, and the redundancy of the image data mainly appears as: spatial redundancy due to correlation between adjacent pixels in the image; temporal redundancy caused by correlation between different frames in the image sequence; spectral redundancy due to the correlation of different color planes or spectral bands. The goal of data compression is to reduce the number of bits required to represent the data by removing these data redundancies. Since the amount of image data is enormous, it is very difficult to store, transmit, and process the image data, and thus compression of the image data is very important.
For medical images, drawing engineering precision drawings, icons, cartoons and other image data, in the image compression process, the image size is large, and the compressed image may have the problem of distortion or damage, so that a large proportion of data loss can be caused after the image is compressed, for example, the compressed image content and the original image have different degrees, and in the subsequent image data transmission or decompression process, the decompressed and restored image has a large amount of data loss or unclear image display, so that a rapid and precise compression method for large-size images is provided for solving the problems.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a method for quickly and accurately compressing a large-size image.
In order to achieve the purpose, the invention adopts the following technical scheme:
a large-size image rapid and accurate compression method comprises a plane template arranged in image processing software, wherein a fixed point A is arranged at any position in the plane template, an orientation scale X and an orientation scale Y extend outwards along the fixed point A respectively, the orientation scale X and the orientation scale Y are vertical, a function coordinate system is formed between the orientation scale X and the orientation scale Y, the fixed point A is the origin of the function coordinate system, and the method comprises the following steps in the image compression process:
the first step is as follows: importing and reading an image to be compressed, enabling any diagonal point in the image to coincide with the fixed point A, and marking a plurality of marking points along the outer contour edge of the image;
the second step is that: reading a mark point on an outer contour edge close to the orientation scale X and the orientation scale Y, setting the mark point to be < b1, b2, b3, b4... ann.. bn > and < c1, c2, c3, c4... ann.. cn > respectively along the outward radiation direction of the orientation scale X and the orientation scale Y by taking the fixed point A as a reference, calculating radial distances Q and P between the mark point b1 and the orientation scale X, and between the mark point c1 and the orientation scale Y, and keeping the numerical values of Q and P as 0;
the third step: then, the radial distances < q1, q2, q3, q4... qn >, c2, c3, c4... cng > between b2, b3, b4... bn and the orientation scale X are calculated, respectively, and the radial distances < p1, p2, p3, p4... pn > between < q1, q2, q3, q4... qn > and < p1, p2, p3, p4... pn > are held with reference to the fixed point a, the marking points b1 and c1 coincide with the fixed point a, the marking points bn and cn coincide with the end points of the long and wide sides in the image;
the fourth step: adjusting the image format to gray scale and calculating the actual length and width of the image in terms of radial distance X1 between b1 and bn, radial distance Y1 between c1 and cn, X1 and Y1;
the fifth step: the method comprises the following steps that an orientation scale Y is located at a plurality of division lines in the direction of the orientation scale X and the direction offset of the orientation scale X along the direction of the orientation scale Y, the offset values of the orientation scale X and the orientation scale Y are both K, an image to be compressed is divided into a plurality of pixel areas along the division lines, a central point in each pixel area is calculated, the coordinates (X1, Y1), (X2, Y2) (X3, Y3) of the central point are calculated, the radial distance of each central point is equal to the offset value K, and the picture format in each pixel area is adjusted to be the format of an original image again;
and a sixth step: taking the central point in each pixel area as a reference, carrying out reduction processing on each pixel area by a proportion B, and splitting an image in the state into a plurality of area blocks;
the seventh step: the remaining region blocks are moved in the radial direction of approach by a distance J of K × B with reference to the region block having the coordinates (x1, y1), and the compressed image is derived by recombining the region blocks into a compressed image.
Preferably, the function coordinate system formed by the orientation scale X and the orientation scale Y includes a first quadrant, a second quadrant, a third quadrant, and a fourth quadrant, and the imported image is set in any one or more of the first quadrant, the second quadrant, the third quadrant, and the fourth quadrant.
Preferably, in the fifth step, the sixth step and the seventh step, the following steps are further included:
1): each pixel region is divided into a square and a rectangle, the side length of each square pixel region is K, and the number of the square pixel regions arranged in the directions of the orientation scale X and the orientation scale Y is divided into E and F, wherein the size of the rectangular pixel region arranged opposite to the fixed point a is (Y1-F K) and (X1-E K), wherein the number of the rectangular pixel region arranged in the direction of the orientation scale Y is F, and wherein the size of the rectangular pixel region arranged in the direction of the orientation scale Y is K and (X1-E K), wherein the size of the rectangular pixel region arranged in the direction of the orientation scale X is (Y1-F K) and K;
2): in the seventh step, the rectangular pixel regions are first moved and each of the rectangular pixel regions along the orientation scale X and the orientation scale Y is brought close to the rectangular pixel region disposed opposite to the fixed point a with reference to the rectangular pixel region disposed opposite to the fixed point a, and after the merging of the rectangular pixel regions is completed, the pixel regions brought close to the square are merged again.
Preferably, in the seventh step, the following steps are further included:
1): re-extracting the compressed image, importing the image into image processing software, operating the first step, the second step, the third step and the fourth step again, confirming the sizes of the compressed images X2 and Y2, wherein the sizes of the compressed images in the operation state are X1 × B and Y1 × B;
2): and calculating the error between X2 and Y2 and X1 and Y1, respectively, setting the maximum error value W, and repeating the first step to the seventh step when the maximum error value W is exceeded.
The method for rapidly and accurately compressing the large-size image has the beneficial effects that:
1. in the process of image compression, the method divides the image into a plurality of pixel areas, divides the complete image into a plurality of pixel areas, performs independent proportional compression processing on each pixel area, and finally combines each compressed pixel area into the complete image, thereby reducing the damage problems such as distortion and the like in the compressed image and improving the image quality;
2. and in the process of processing, two steps of calibration and detection are respectively carried out, in the process of calibration, the imported original image is ensured to be in a position relative to the positive direction, so that the stability in the subsequent segmentation operation is ensured, in the process of detection, the actual size and the ideal dust discharge of the compressed image can be automatically calculated, and the error between the actual size and the ideal dust discharge can be calculated, so that the quality of the compressed image is further ensured.
Drawings
FIG. 1 is a layout diagram of a fast and accurate compression method for large-size images according to the present invention;
FIG. 2 is a schematic diagram of an image divided into a plurality of pixel regions;
FIG. 3 is a block diagram of a large-size image compression method according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
Example 1
Referring to fig. 1-3, a method for rapidly and accurately compressing a large-size image includes a planar template set in image processing software, a fixed point a is set at any position in the planar template, an orientation scale X and an orientation scale Y extend outward along the fixed point a, the orientation scale X and the orientation scale Y are vertical, a function coordinate system is formed between the orientation scale X and the orientation scale Y, the fixed point a is an origin of the function coordinate system, and the method includes the following steps in the image compression process:
the first step is as follows: importing and reading an image to be compressed, enabling any diagonal point in the image to coincide with the fixed point A, and marking a plurality of marking points along the outer contour edge of the image;
the second step is that: reading a mark point on an outer contour edge close to the orientation scale X and the orientation scale Y, setting the mark point to be < b1, b2, b3, b4... ann.. bn > and < c1, c2, c3, c4... ann.. cn > respectively along the outward radiation direction of the orientation scale X and the orientation scale Y by taking the fixed point A as a reference, calculating radial distances Q and P between the mark point b1 and the orientation scale X, and between the mark point c1 and the orientation scale Y, and keeping the numerical values of Q and P as 0;
the third step: then, the radial distances < q1, q2, q3, q4... qn >, c2, c3, c4... cng > between b2, b3, b4... bn and the orientation scale X are calculated, respectively, and the radial distances < p1, p2, p3, p4... pn > between < q1, q2, q3, q4... qn > and < p1, p2, p3, p4... pn > are held with reference to the fixed point a, the marking points b1 and c1 coincide with the fixed point a, the marking points bn and cn coincide with the end points of the long and wide sides in the image;
the fourth step: adjusting the image format to gray scale and calculating the actual length and width of the image in terms of radial distance X1 between b1 and bn, radial distance Y1 between c1 and cn, X1 and Y1;
the fifth step: the method comprises the following steps that an orientation scale Y is located at a plurality of division lines in the direction of the orientation scale X and the direction offset of the orientation scale X along the direction of the orientation scale Y, the offset values of the orientation scale X and the orientation scale Y are both K, an image to be compressed is divided into a plurality of pixel areas along the division lines, a central point in each pixel area is calculated, the coordinates (X1, Y1), (X2, Y2) (X3, Y3) of the central point are calculated, the radial distance of each central point is equal to the offset value K, and the picture format in each pixel area is adjusted to be the format of an original image again;
and a sixth step: taking the central point in each pixel area as a reference, carrying out reduction processing on each pixel area by a proportion B, and splitting an image in the state into a plurality of area blocks;
the seventh step: the remaining region blocks are moved in the radial direction of approach by a distance J of K × B with reference to the region block having the coordinates (x1, y1), and the compressed image is derived by recombining the region blocks into a compressed image.
The function coordinate system formed by the orientation scale X and the orientation scale Y comprises a first quadrant, a second quadrant, a third quadrant and a fourth quadrant, and the imported image is arranged in any one area or a plurality of areas of the first quadrant, the second quadrant, the third quadrant and the fourth quadrant.
The working principle is as follows: after an original image is led into image processing software, a certain point of the image is dragged to be overlapped with a fixed point A through a mouse, a plurality of images can be led simultaneously, the number of the images is not more than four, each image can be respectively arranged in any one area or a plurality of areas of a first quadrant, a second quadrant, a third quadrant and a fourth quadrant in a function coordinate system, then a calibration instruction is input, and the calibration of the images is completed according to the second step and the third step;
finally, the fourth step to the seventh step are performed to divide the original image into a plurality of pixel regions, as shown in fig. 2.
Dividing an original image into a plurality of pixel areas, calculating a central point in each pixel area, calculating coordinates of each central point, performing proportional compression on each pixel area according to a certain proportion, and merging each compressed image after compression is completed.
Example 2
In the fifth step, the sixth step and the seventh step, the following steps are further included:
1): each pixel region is divided into a square and a rectangle, the side length of each square pixel region is K, and the number of the square pixel regions arranged in the directions of the orientation scale X and the orientation scale Y is divided into E and F, wherein the size of the rectangular pixel region arranged opposite to the fixed point a is (Y1-F K) and (X1-E K), wherein the number of the rectangular pixel region arranged in the direction of the orientation scale Y is F, and wherein the size of the rectangular pixel region arranged in the direction of the orientation scale Y is K and (X1-E K), wherein the size of the rectangular pixel region arranged in the direction of the orientation scale X is (Y1-F K) and K;
2): in the seventh step, the rectangular pixel regions are first moved and each of the rectangular pixel regions along the orientation scale X and the orientation scale Y is brought close to the rectangular pixel region disposed opposite to the fixed point a with reference to the rectangular pixel region disposed opposite to the fixed point a, and after the merging of the rectangular pixel regions is completed, the pixel regions brought close to the square are merged again.
In the seventh step, the following steps are also included:
1): re-extracting the compressed image, importing the image into image processing software, operating the first step, the second step, the third step and the fourth step again, confirming the sizes of the compressed images X2 and Y2, wherein the sizes of the compressed images in the operation state are X1 × B and Y1 × B;
2): and calculating the error between X2 and Y2 and X1 and Y1, respectively, setting the maximum error value W, and repeating the first step to the seventh step when the maximum error value W is exceeded.
The working principle is as follows: based on example 1, the dimensions of rectangular pixel regions disposed opposite to the fixed point a were calculated as (Y1-F × K) and (X1-E × K), wherein the dimensions of rectangular pixel regions disposed in the direction of the orientation scale Y were K and (X1-E × K), wherein the dimensions of rectangular pixel regions disposed in the direction of the orientation scale X (Y1-F × K) and K were moved first before merging the compressed pixel regions, and each of the rectangular pixel regions disposed opposite to the fixed point a was moved closer to the rectangular pixel region disposed opposite to the fixed point a with reference to the rectangular pixel region disposed opposite to the fixed point a, and after completion of merging of the rectangular pixel regions, the pixel regions are merged closer to the square pixel region;
in addition, after the image compression is completed, the compressed image is re-extracted and introduced into the image processing software, the first step, the second step, the third step and the fourth step are executed again, the sizes of the compressed images X2 and Y2 are confirmed, the sizes of the compressed images in the operation state are X1 × B and Y1 × B, errors between X2 and Y2 and X1 × B and Y1 × B are calculated, the maximum error value W is set, and when the maximum error value W is exceeded, the first step to the seventh step are repeated again.
In summary, the following steps: the original image is divided into a plurality of pixel areas by a certain numerical value, the pixel areas are independently compressed, and each compressed pixel area is combined, so that the damage problems such as image distortion and the like can be reduced to a greater extent after the compression processing of the whole image is completed, and the quality of the compressed image is ensured.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (4)
1. A large-size image rapid and accurate compression method is characterized by comprising a plane template arranged in image processing software, wherein a fixed point A is arranged at any position in the plane template, an orientation scale X and an orientation scale Y extend outwards along the fixed point A respectively, the orientation scale X and the orientation scale Y are vertical, a function coordinate system is formed between the orientation scale X and the orientation scale Y, the fixed point A is the origin of the function coordinate system, and the method comprises the following steps in the image compression process:
the first step is as follows: importing and reading an image to be compressed, enabling any diagonal point in the image to coincide with the fixed point A, and marking a plurality of marking points along the outer contour edge of the image;
the second step is that: reading a mark point on an outer contour edge close to the orientation scale X and the orientation scale Y, setting the mark point to be < b1, b2, b3, b4... ann.. bn > and < c1, c2, c3, c4... ann.. cn > respectively along the outward radiation direction of the orientation scale X and the orientation scale Y by taking the fixed point A as a reference, calculating radial distances Q and P between the mark point b1 and the orientation scale X, and between the mark point c1 and the orientation scale Y, and keeping the numerical values of Q and P as 0;
the third step: then, the radial distances < q1, q2, q3, q4... qn >, c2, c3, c4... cng > between b2, b3, b4... bn and the orientation scale X are calculated, respectively, and the radial distances < p1, p2, p3, p4... pn > between < q1, q2, q3, q4... qn > and < p1, p2, p3, p4... pn > are held with reference to the fixed point a, the marking points b1 and c1 coincide with the fixed point a, the marking points bn and cn coincide with the end points of the long and wide sides in the image;
the fourth step: adjusting the image format to gray scale and calculating the actual length and width of the image in terms of radial distance X1 between b1 and bn, radial distance Y1 between c1 and cn, X1 and Y1;
the fifth step: the method comprises the following steps that an orientation scale Y is located at a plurality of division lines in the direction of the orientation scale X and the direction offset of the orientation scale X along the direction of the orientation scale Y, the offset values of the orientation scale X and the orientation scale Y are both K, an image to be compressed is divided into a plurality of pixel areas along the division lines, a central point in each pixel area is calculated, the coordinates (X1, Y1), (X2, Y2) (X3, Y3) of the central point are calculated, the radial distance of each central point is equal to the offset value K, and the picture format in each pixel area is adjusted to be the format of an original image again;
and a sixth step: taking the central point in each pixel area as a reference, carrying out reduction processing on each pixel area by a proportion B, and splitting an image in the state into a plurality of area blocks;
the seventh step: the remaining region blocks are moved in the radial direction of approach by a distance J of K × B with reference to the region block having the coordinates (x1, y1), and the compressed image is derived by recombining the region blocks into a compressed image.
2. A method for rapidly and accurately compressing large-size images according to claim 1, wherein a function coordinate system composed of an orientation scale X and an orientation scale Y comprises a first quadrant, a second quadrant, a third quadrant and a fourth quadrant, and the imported image is arranged in any one or more of the first quadrant, the second quadrant, the third quadrant and the fourth quadrant.
3. A method for rapidly and accurately compressing a large-size image according to claim 1, wherein in the fifth step, the sixth step and the seventh step, the following steps are further included:
1): each pixel region is divided into a square and a rectangle, the side length of each square pixel region is K, and the number of the square pixel regions arranged in the directions of the orientation scale X and the orientation scale Y is divided into E and F, wherein the size of the rectangular pixel region arranged opposite to the fixed point a is (Y1-F K) and (X1-E K), wherein the number of the rectangular pixel region arranged in the direction of the orientation scale Y is F, and wherein the size of the rectangular pixel region arranged in the direction of the orientation scale Y is K and (X1-E K), wherein the size of the rectangular pixel region arranged in the direction of the orientation scale X is (Y1-F K) and K;
2): in the seventh step, the rectangular pixel regions are first moved and each of the rectangular pixel regions along the orientation scale X and the orientation scale Y is brought close to the rectangular pixel region disposed opposite to the fixed point a with reference to the rectangular pixel region disposed opposite to the fixed point a, and after the merging of the rectangular pixel regions is completed, the pixel regions brought close to the square are merged again.
4. A method for rapidly and accurately compressing large-size images according to claim 1, wherein in the seventh step, the method further comprises the following steps:
1): re-extracting the compressed image, importing the image into image processing software, operating the first step, the second step, the third step and the fourth step again, confirming the sizes of the compressed images X2 and Y2, wherein the sizes of the compressed images in the operation state are X1 × B and Y1 × B;
2): and calculating the error between X2 and Y2 and X1 and Y1, respectively, setting the maximum error value W, and repeating the first step to the seventh step when the maximum error value W is exceeded.
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