CN117939149A - Image compression and restoration algorithm and system - Google Patents

Abstract

The invention discloses an image compression and recovery algorithm and a system, and relates to the technical field of image compression, wherein the algorithm is arranged at the front end to realize the compression of the front end of an image, and the rear end realizes the decompression and recovery of the image; the algorithm comprises the following steps: and acquiring all pixel points of the image data, recording the coordinate value and the color value of each pixel point, storing the coordinate of the first pixel point and the quantized color value at the front end, thereby realizing data compression, and recovering the recovered data by the rear end based on a recovery algorithm. The invention can compress the image data on the device by applying the compression algorithm to the front-end device, thereby reducing the data quantity transmitted to the back-end, reducing the requirement of network bandwidth, improving the efficiency and speed of image transmission, reducing the time delay of data transmission and back-end processing and improving the instantaneity; by front-end compression, the size of the image data can be significantly reduced, thereby saving storage space.

Description

Image compression and restoration algorithm and system

Technical Field

The invention relates to the technical field of image compression, in particular to an image compression and recovery algorithm and an image compression and recovery system.

Background

With the continuous development of multimedia technology and communication technology, multimedia entertainment, information highways and the like continuously put higher demands on storage and transmission of information data, and the existing limited bandwidth is also subjected to serious examination, particularly digital image communication with huge data volume is more difficult to transmit and store, and the data transmission of the communication technology is greatly restricted, so that the image compression technology is receiving more and more attention.

In many practical applications, data compression algorithms require compression and decompression tasks to be completed in a limited time. However, there is a certain contradiction between the processing speed and the compression ratio, some algorithms with high compression ratio may require a longer time to complete compression, and some fast algorithms have a lower compression ratio, so that the problem of the processing speed needs to be solved in practical application.

Disclosure of Invention

It is an aim of embodiments of the present invention to provide an image compression and restoration algorithm and system which addresses the above-described deficiencies in the background art.

To achieve the above object, in one aspect, an embodiment of the present invention provides an image compression and restoration algorithm, including:

Collecting a real-time image to obtain image data;

Acquiring all pixel points of the image data, and recording coordinate values and color values of each pixel point, wherein the front end only stores the coordinate of the first pixel point and the quantized color value of the quantized image so as to realize data compression;

and identifying the compressed data at the back end, and restoring the image based on a restoration algorithm to realize data restoration.

Alternatively, the image data is an image obtained by a power device monitored in real time by a plurality of infrared sensors installed in the power detection area.

Optionally, the acquiring all the pixel points of the image data, and recording the coordinate value and the color value of each pixel point includes:

Scanning the image data, obtaining all pixel points of the image data, and defining the coordinate of each pixel point as (i.j), wherein i represents row pixel points, j represents column pixel points, and marking the pixel points as the position coordinates of the pixel points;

And storing the coordinates of the first pixel point and the coordinate difference value, namely the pixel offset, of the subsequent pixel point and the previous pixel point so as to acquire the position coordinates of all the pixel points.

Optionally, the color values of the pixels in the image data are represented by color components, each pixel is composed of different color components, and the value range of each component is (0, 255).

Optionally, the quantized color values include:

Determining the color quantization level as 8, namely determining the number of quantized colors;

Calculating the quantization interval step length of each color component according to the quantization level;

and quantizing the color component of each pixel point to replace the color component value of each pixel in the original image with a corresponding quantization level, and storing the quantized color value in a new array to form quantized image data.

Optionally, acquiring all pixels of the image data, and recording coordinate values and color values of each pixel, where only the coordinate of the first pixel is stored in the front end, and quantizing the color values to obtain a quantized image, so as to implement data compression includes:

defining a color index bit number N, wherein the color of each pixel is represented by an N-bit binary number;

Defining a palette of N different colors to the power of 2 based on the index bit number N, the palette comprising a table of predefined colors that maps with the color value of each pixel in the original image;

selecting a color in the palette according to a quantization level;

Establishing a color index map, and allocating a unique index value for each color in the palette, wherein the index value is an integer from 0;

And storing the quantized image data into two arrays, wherein one array stores a color index and the other array stores an actual color value.

Optionally, the identifying the compressed data at the back end and restoring the image based on a restoration algorithm to implement data restoration includes:

The rear end reads the compressed image data, and the quantized color value of each pixel is restored according to the color value information of the compressed image data;

Acquiring a quantization level used in compressing an image;

And resolving the quantized color value of each pixel according to the color value information and the quantization level in the compressed data.

Optionally, the parsing the quantized color value of each pixel includes:

Reading a color index value from the compressed data according to the color index bit number N;

decoding each index value into a corresponding color according to the color index value and the palette in the compressed data;

Replacing the index value of each pixel with the color value obtained by decoding, and filling the color value of each pixel in the original image into an analyzed color value to realize image data restoration;

And calculating the position coordinates of the current pixel point according to the coordinates of the previous pixel point and the difference value of the current pixel point, and realizing image data recovery based on the restored image data and the position coordinates.

In another aspect, the present invention also provides an image compression and restoration system, which is characterized in that the image compression and restoration system includes:

The image acquisition module is used for acquiring image data of a monitoring site;

the operation module is connected with the image acquisition module, receives the image data and executes the image compression and recovery algorithm.

Through the technical scheme, the image compression and recovery algorithm provided by the invention acquires the real-time image to obtain the image data. Then, all the pixel points of the image data are acquired, and the coordinate value and the color value of each pixel point may be recorded. The front end may store only the coordinates of the first pixel point and the quantized color values of the quantized image, and image compression has been achieved. The back end can recognize the compressed data and restore the image based on the restoration algorithm to realize the restoration of the data. The invention can compress the image data on the device by applying the compression algorithm to the front-end device, thereby reducing the data quantity transmitted to the back-end, reducing the requirement of network bandwidth and improving the efficiency and speed of image transmission. By front-end compression, the size of the image data can be significantly reduced, thereby saving storage space.

Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.

Drawings

The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain, without limitation, the embodiments of the invention. In the drawings:

FIG. 1 is a flow chart of an image compression and restoration algorithm according to one embodiment of the present invention;

FIG. 2 is a flow chart of an image compression and restoration algorithm for acquiring pixel locations according to one embodiment of the present invention;

FIG. 3 is a flow chart of a quantization of color components of pixels for an image compression and restoration algorithm according to one embodiment of the present invention;

FIG. 4 is a flow chart of indexing color values for an image compression and restoration algorithm according to one embodiment of the present invention;

FIG. 5 is a flow chart of quantized color values of resolved pixels of an image compression and recovery algorithm according to one embodiment of the invention;

Fig. 6 is a flowchart of image data recovery for an image compression and recovery algorithm according to one embodiment of the present invention.

Detailed Description

The following describes the detailed implementation of the embodiments of the present invention with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.

Fig. 1 is a flow chart of an image compression and restoration algorithm according to an embodiment of the present invention. In the present invention, the flow of the image compression and restoration algorithm may include:

in step S1, a real-time image is acquired to obtain image data.

In step S2, all pixels of the image data are acquired, and coordinate values and color values of each pixel are recorded, wherein only the coordinate of the first pixel and the quantized color value are stored at the front end to obtain a quantized image, so as to realize data compression.

In step S3, the compressed data is identified at the back end, and the image is restored based on the restoration algorithm to achieve data restoration.

In the invention, the image compression algorithm can be built in the front end to realize the compression of the front end of the image, and the image restoration algorithm can be built in the rear end to realize the decompression and restoration of the image. After the image data is obtained, the image data can be compressed in real time, then the compressed image can be transmitted to the back end through a network, and a restoration algorithm can be applied to decompress the image so as to restore the image, thereby saving traffic and network bandwidth requirements.

In the invention, when image data acquisition is carried out, a plurality of infrared sensors can be arranged in the power monitoring area and used for monitoring and acquiring the running image of the power equipment in real time, so that the image data can be formed.

In the image compression technique, the process of splitting an image into minimum units is called quantization. Quantization is the conversion of continuous image data into discrete pixel values, i.e. the division of a picture into a number of smallest units, pixels, each containing a small block of information in the image. In digital image processing, each pixel is typically represented as a number whose size indicates the pixel's location, color value, etc.

In the present invention, as shown in fig. 2, the process of obtaining the position of the pixel point may include:

In step S4, the image data is scanned, all pixels of the image data are acquired, and coordinates of each pixel are defined as (i×j), where i represents a row pixel number and j represents a column pixel number, and are recorded as position coordinates of the pixel.

In step S5, the coordinates of the first pixel and the coordinate difference values, i.e., the pixel offsets, between the subsequent pixel and the previous pixel are stored to obtain the position coordinates of all the pixels.

In the present invention, when a pixel is located, coordinates of each pixel may be defined, and the position of each pixel point determines its specific position in the image. By recording the position information of each pixel, the spatial structure of the image can be correctly restored at the time of decoding. In the compression algorithm, the position information of the pixel point is generally represented by a coordinate difference to reduce the storage space. When the positions of the rest pixel points in the image data are recorded, the coordinates of the first pixel point and the coordinate difference values of the subsequent pixel points and the previous pixel point can be stored, wherein the coordinate difference is the coordinate difference between the adjacent pixel points to represent the position information, namely the pixel offset, so that the position coordinates of all the pixel points can be obtained. By adopting the coordinate difference technique, the space required for storing the position information can be effectively reduced.

Examples: original pixel coordinate sequence: [ (0, 0), (0, 1), (1, 0), (1, 1), (1, 2) ] using coordinate differential encoding: reference point: (0, 0) coding sequence: [ (0,0), (+0,+1), (+1, -1), (+0,+1), (+0,+1) ]

In this example, we only store the coordinates of the first pixel point, and then record the difference between the coordinates of the rest of pixel points and the previous pixel point through coordinate differential encoding, so that the coordinate positions of all the pixel points can be obtained only from the first coordinate point, and the storage space is greatly saved.

In one embodiment of the present invention, when image data is obtained, color values of pixel points in the image data may be represented by color components. Each pixel may be composed of a different color component. For color values of an image, an RGB model representation may typically be used, and the value range for each color component (red, green, blue) may be 0 to 255.

In one embodiment of the present invention, as shown in fig. 3, the process of quantizing the color components of the pixel point may include:

In step S6, the color quantization level is determined to be 8, i.e., the number of colors after quantization is determined.

In step S7, a quantization interval step size for each color component is calculated from the quantization levels.

In step S8, the color component of each pixel is quantized to replace the color component value of each pixel in the original image with a corresponding quantization level, and the quantized color values are stored in a new array to form quantized image data.

In the invention, after the color values of all the pixel points are obtained, the color values of the pixel points can be quantized. The quantized image refers to a new image obtained after performing color quantization, which is a method of reducing the number of colors in an image by mapping each pixel in an original image to a smaller number of color values, and the quantized image can more compactly represent the original image while retaining a main shape and characteristics to some extent.

In the color space, each component generally refers to a different attribute or dimension representing a color. Common color spaces such as RGB, HSV, and Lab are all composed of multiple components. Taking the RGB color space as an example, each component therein represents three color channels, red, green and blue, respectively. In the RGB color space, the color value of each pixel is represented by three component values, typically ranging from 0 to 255, representing the intensity of the color channel.

In the present invention, after the quantized color number is determined, for the color value of an image, an RGB model representation is generally used, and the value range of each color component is 0 to 255. If the color of the image is chosen to be quantized 8 bits, i.e. 8 quantization levels, each color component may be divided into 8 equidistant intervals between 0 and 255, so that each color component may only take values of 0, 32, 64, 96, 128, 160, 192, 224 and 255, and not all possible values in succession. By reducing the continuity of the color values, quantizing them to fewer levels, the storage space of the image data can be effectively reduced, and the processing procedure can be simplified. After determining the number of quantized colors, a quantization interval step size may be defined. According to the quantization level, a quantization interval step size of each color component can be calculated, the range of the value of the color component is 0 to 255, and the color component is divided into 8 levels, and the quantization interval step size= (255-0)/quantization level is: (255-0)/8=31.875, rounded to 32. After the quantization interval step is obtained, the color component of each pixel point may be quantized, so that the color component of each pixel in the original image may be replaced with a corresponding quantization level.

Assuming that the original color value of a pixel is (131,245,76), we quantize it into 8 levels according to the procedure described above. The calculated quantization interval is 32, and then quantization is performed:

the red component is quantized: quantized _red=int (131/32) ×32=128

The green component is quantized: quantized _green=int (245/32) ×32=224

The blue component is quantized: quantized _blue=int (76/32) ×32=64

Therefore, the color value of the pixel after quantization is (128,224,64). This quantized color value will be used to compose quantized image data.

In one embodiment of the present invention, as shown in fig. 4, the process of indexing the color values may include:

In step S9, a color index bit number N is defined, and the color of each pixel is represented by an N-bit binary number.

In step S10, a palette is defined based on the index bit number N, the palette being N different colors to the power of 2, the palette comprising a set of tables of predefined colors, the tables being mapped with the color value of each pixel in the original image.

In step S11, colors are selected in the palette according to the quantization levels.

In step S12, a color index map is established, assigning a unique index value to each color in the palette, the index value being an integer starting from 0.

In step S13, the quantized image data is stored as two arrays, one storing the color index and the other storing the actual color value.

After the image data is quantized, a data index may be created for the quantized image data. When creating the data index, the number of color index bits N may be defined first, and each color value may establish a unique index for identifying the color value when the original image data is subsequently restored. For example, for the quantized color value (128,224,64) described above, its corresponding index may be set to 0, since it is the first quantized color value to appear. In storing the quantized image data, it may be stored as two arrays, one of which may store a color index and the other may store an actual color value. The indexes in the two arrays are in one-to-one correspondence, namely, the color value corresponding to the ith color index is the value in the ith color value array.

In one embodiment of the present invention, as shown in fig. 5, the process of resolving the quantized color values of the pixels may include:

in step S14, the back end reads the compressed image data, and restores the quantized color value of each pixel according to the color value information of the compressed image data.

In step S15, a quantization level used in compressing an image is acquired.

In step S16, the quantized color value of each pixel is parsed from the color value information and the quantization level in the compressed data.

In the present invention, after the compressed image data is read by the back end, the quantized color value of each pixel can be restored according to the color value information of the compressed image data. In restoring the quantized color value of each pixel, the quantization level used in compressing the image may be acquired first, and then the quantized color value of each pixel may be parsed according to the information of the color value and the quantization level in the compressed data.

In one embodiment of the present invention, as shown in fig. 6, the flow of image data recovery may include:

In step S17, the color index value is read from the compressed data in accordance with the color index bit number N.

In step S18, each index value is decoded into a corresponding color according to the color index value and the palette in the compressed data.

In step S19, the index value of each pixel is replaced with the color value obtained by decoding, and the color value of each pixel in the original image is filled with the resolved color value, so as to implement image data restoration.

In step S20, the position coordinates of the current pixel point are calculated according to the coordinates of the previous pixel point and the difference value of the current pixel point, and the image data recovery is realized based on the restored image data and the position coordinates.

In the present invention, when analyzing the quantized color value of each pixel, the color index value may be read from the compressed data in accordance with the number of color index bits N. Each index value may then be decoded into a corresponding color based on the color index value and the palette in the compressed data for the purpose of decoding the color index. After decoding the color index, the image data can be restored, and when the image data is restored, the index value of each pixel can be replaced by the color value obtained by decoding, and the color value of each pixel in the original image is filled into the analyzed color value, so that the image data restoration can be realized. The restoration is for color values, and the positions of the pixels need to be restored. In the restoration, the position coordinate of the current pixel point can be calculated according to the coordinate of the previous pixel point and the difference value of the current pixel point, and then the restoration of the image data can be realized based on the restored image data and the position coordinate.

When the quantized color values and index values are decoded for image data recovery, the quantized color values and index values may be decoded according to a compression algorithm, and for each pixel, the decoded color values and index values may be combined into a tuple or object. From the index value and the palette, the corresponding color value may be looked up and assigned to the current pixel point. After the restored pixel values and position coordinates are obtained, the compressed image may be restored.

In another aspect, the present invention also provides an image compression and restoration system, including: and the image acquisition module and the operation module. The image acquisition module may be used to acquire image data of a monitoring site. The operation module may be connected to the image acquisition module, receive the image data, and may perform the image compression and restoration algorithm as described above.

Through the technical scheme, the image compression and recovery algorithm provided by the invention acquires the real-time image to obtain the image data. Then, all the pixel points of the image data are acquired, and the coordinate value and the color value of each pixel point may be recorded. The front end may store only the coordinates of the first pixel point and the quantized color values of the quantized image, and image compression has been achieved. The back end can recognize the compressed data and restore the image based on the restoration algorithm to realize the restoration of the data. The invention can compress the image data on the device by applying the compression algorithm to the front-end device, thereby reducing the data quantity transmitted to the back-end, reducing the requirement of network bandwidth and improving the efficiency and speed of image transmission. By front-end compression, the size of the image data can be significantly reduced, thereby saving storage space.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.

The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.

Claims (9)

1. An image compression and restoration algorithm, the image compression and restoration algorithm comprising:

Collecting a real-time image to obtain image data;

Acquiring all pixel points of the image data, and recording coordinate values and color values of each pixel point, wherein the front end only stores the coordinate of the first pixel point and the quantized color value of the quantized image so as to realize data compression;

and identifying the compressed data at the back end, and restoring the image based on a restoration algorithm to realize data restoration.

2. The image compression and restoration algorithm according to claim 1, wherein the image data is an image obtained by a power device monitored in real time by a plurality of infrared sensors installed in a power detection area.

3. The image compression and restoration algorithm according to claim 1, wherein the acquiring all the pixels of the image data and recording the coordinate value and the color value of each of the pixels comprises:

Scanning the image data, obtaining all pixel points of the image data, and defining the coordinate of each pixel point as (i.j), wherein i represents row pixel points, j represents column pixel points, and marking the pixel points as the position coordinates of the pixel points;

And storing the coordinates of the first pixel point and the coordinate difference value, namely the pixel offset, of the subsequent pixel point and the previous pixel point so as to acquire the position coordinates of all the pixel points.

4. An image compression and restoration algorithm according to claim 3, wherein the color values of the pixels in the image data are represented by color components, each pixel being composed of different color components, and the range of values of each component being (0, 255).

5. The image compression and restoration algorithm according to claim 4, wherein the obtaining the quantized image from the quantized color values comprises:

Determining the color quantization level as 8, namely determining the number of quantized colors;

Calculating the quantization interval step length of each color component according to the quantization level;

and quantizing the color component of each pixel point to replace the color component value of each pixel in the original image with a corresponding quantization level, and storing the quantized color value in a new array to form quantized image data.

6. The image compression and restoration algorithm according to claim 5, wherein acquiring all pixels of the image data and recording coordinate values and color values of each of the pixels, wherein storing only the coordinates of the first pixel at a front end and quantizing the color values to obtain a quantized image, to achieve data compression, comprises:

defining a color index bit number N, wherein the color of each pixel is represented by an N-bit binary number;

Defining a palette of N different colors to the power of 2 based on the index bit number N, the palette comprising a table of predefined colors that maps with the color value of each pixel in the original image;

selecting a color in the palette according to a quantization level;

Establishing a color index map, and allocating a unique index value for each color in the palette, wherein the index value is an integer from 0;

And storing the quantized image data into two arrays, wherein one array stores a color index and the other array stores an actual color value.

7. The image compression and restoration algorithm according to claim 6, wherein the identifying the compressed data at the back end and restoring the image based on the restoration algorithm to achieve the data restoration comprises:

The rear end reads the compressed image data, and the quantized color value of each pixel is restored according to the color value information of the compressed image data;

Acquiring a quantization level used in compressing an image;

And resolving the quantized color value of each pixel according to the color value information and the quantization level in the compressed data.

8. The image compression and restoration algorithm according to claim 7, wherein the parsing the quantized color value of each pixel includes:

Reading a color index value from the compressed data according to the color index bit number N;

decoding each index value into a corresponding color according to the color index value and the palette in the compressed data;

Replacing the index value of each pixel with the color value obtained by decoding, and filling the color value of each pixel in the original image into an analyzed color value to realize image data restoration;

And calculating the position coordinates of the current pixel point according to the coordinates of the previous pixel point and the difference value of the current pixel point, and realizing image data recovery based on the restored image data and the position coordinates.

9. An image compression and restoration system, the image compression and restoration system comprising:

The image acquisition module is used for acquiring image data of a monitoring site;

An operation module connected to the image acquisition module, receiving the image data, and executing the image compression and restoration algorithm according to any one of claims 1 to 8.