A method for performance equalization compression of satellite images

Abstract

The application discloses a performance equalization compression method of satellite images. The method equalizes the excellent compression performance of images and the poor compression performance of images from the perspective of a multi-image system by arranging the images, overcomes the influence of the barrel effect on the practicability of image compression, overcomes the defect that the compression quality of a plurality of images compressed by a JPEG2000 standard compression algorithm is too different, and improves the performance of the standard compression algorithm without increasing complexity. The method has the characteristics of easy software and hardware upgrading and realization, has practical value in a satellite image compression transmission system, and can also play a role in any other multi-source standard compression transmission occasion.

Description

Technical Field

[0001] This invention relates to a data transmission method, and more particularly to a performance-balanced compression method for satellite images, belonging to the field of communications. Background Technology

[0002] With the development of technology, people's demand for high-resolution images is increasing, and the amount of data is also increasing, making data compression imperative. Standard compression algorithms such as JPEG2000 have different compression performance for different images. At the same compression ratio, some images have a high PSNR value before and after compression, while others have a very low PSNR, 5-20dB or even more. It can be said that the compression performance is uneven.

[0003] To ensure that no image is of the worst quality in the entire image transmission system, there is an urgent need for a method that can overcome the "weakest link" effect in image compression and solve the problem that satellite image compression methods are particularly ineffective at compressing certain images. Summary of the Invention

[0004] The technical problem solved by this invention is to overcome the shortcomings of the prior art and provide a performance-balanced compression method for satellite images, which overcomes the impact of the "weakest link" effect on the practicality of image compression and meets user needs.

[0005] The technical solution of this invention is: a performance-balanced compression method for satellite images, comprising:

[0006] Rearrange the n images in the satellite image set X = X1, X2, ... Xn to obtain the image set Y = Y1, Y2, ... Yn, and record the sequence correspondence table between the satellite image set Y and X;

[0007] Combine the images in the image set Y to form an image set Z = Z1, Z2, ..., Zk, where k is a positive integer less than or equal to n;

[0008] Compress k images in image set Z to obtain data set C, store and transmit C;

[0009] The receiving end decompresses the data set C to obtain a set of k images Z;

[0010] Decompose the image set Z to obtain the image set Y;

[0011] The image set X is recovered based on the sequence number correspondence table.

[0012] Following the original order of the image set X without rearranging it, we obtain the image set Y = Y1, Y2, ..., Yn, which is Y = X.

[0013] The step of rearranging n images in the satellite image set X = X1, X2, ... Xn to obtain the image set Y = Y1, Y2, ... Yn includes: shuffling the order of the image set X and randomly arranging the n images to obtain the image set Y = Y1, Y2, ... Yn.

[0014] The process of rearranging n images in the satellite image set X = X1, X2, ... Xn to obtain the image set Y = Y1, Y2, ... Yn includes:

[0015] Calculate the parameter S for each image in the satellite image set X, namely S1, S2, ..., Sn. The parameter S refers to the average of the absolute value of the gray level difference between adjacent rows and the sum of the absolute values ​​of the gray level difference between adjacent columns in each satellite image. Rearrange the satellite images in order from high to low according to the value of parameter S to obtain the image set Y = Y1, Y2, ..., Yn.

[0016] The process of combining images in image set Y to form image set Z = Z1, Z2…Zk includes:

[0017] The combination method uses two images: the first image and the last image of Y.

[0018] Z1 = [Y1, Yn], Z2 = [Y2, Yn-1], where k = n / 2 and n is a multiple of 2.

[0019] The process of combining images in image set Y to form image set Z = Z1, Z2…Zk includes:

[0020] The combination method uses a combination of four images: the first two images and the last two images.

[0021] Z1 = [Y1, Y2, Yn-1, Yn], Z2 = [Y3, Y4, Yn-3, Yn-2], where k = n / 4 and n is a multiple of 4.

[0022] The process of combining images in image set Y to form image set Z = Z1, Z2…Zk includes:

[0023] The combination method uses a combination of three images: the first image and the last two images.

[0024] Z1 = [Y1, Yn-1, Yn], Z2 = [Y2, Yn-3, Yn-2], where k = n / 3 and n is a multiple of 3.

[0025] When performing compression or decompression, JPEG2000 compression / decompression is used.

[0026] The advantages of this invention compared to the prior art are as follows:

[0027] (1) This method improves the compression performance of images with poor standard compression performance by complementing the characteristics of multiple images, thereby enhancing the engineering practicality of the standard compression method.

[0028] (2) This method classifies and arranges images through an innovative approach. From a system perspective, it appropriately reduces the compression performance of excellent images, thereby greatly improving the compression performance of previously poor images. It combines some high-quality images with low-quality images, achieving the effect of "the rich leading the poor" and realizing "common prosperity." It ensures that there are no worst images in the entire image transmission system, solving the defect of the original satellite image compression method that has particularly poor compression effect on some images.

[0029] (3) This method opens up a new technical path for the practical application of satellite data compression technology. It does not require the purchase of additional new components. It can use the stock chips to upgrade the product, greatly shorten the satellite development cycle and reduce the development cost. Attached Figure Description

[0030] Figure 1 This is a way of combining four images.

[0031] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0032] like Figure 2 As shown, the implementation process of a performance-balanced compression method for satellite images is as follows:

[0033] (1) Arrange the 4 or 2 images of the satellite image set X to obtain the image set Y, and then form the image set Z = Z1 based on the image set Y;

[0034] (2) Compress the set of k images Z = Z1 using JPEG2000 to obtain a data set C = C1, which is then stored and transmitted.

[0035] (3) The receiving end performs JPEG2000 decompression on the data set C to obtain the image set Z = Z1.

[0036] (4) Decompose the set of k images Z to obtain the set of images Y;

[0037] (5) Obtain the image set X = X1, X2, ..., Xn, where n = 4 or 2, according to the serial number correspondence table.

[0038] The performance of this invention was verified through simulation. Multiple 8-bit international standard grayscale images of size 512×512 were used for compression and restoration in the experiment. The PSNR (Power Sampling Noise Ratio) of the compressed and restored images at the same compression ratio was used to evaluate the performance of this method.

[0039] Peak Signal-to-Noise Ratio (PSNR) is used to measure the performance of compression algorithms. For an 8-bit digital image of size H×W, PSNR is defined as follows:

[0040]

[0041] In the formula, MSE is the mean square error between the original image and the restored image, and the calculation formula is:

[0042]

[0043] Here x ij , These represent the pixel values ​​at (i,j) in the original image and the restored image, respectively.

[0044] As shown in Tables 1-3, these are the compression effects of multiple standard test images from this invention, wherein:

[0045] If there are only n=4 images, they can be directly combined. Combination method 2: the first 2 images and the last 2 images are combined into a square matrix. No parameter S needs to be calculated.

[0046] If there are only n=2 images, they can be directly combined according to combination method 1: the first image and the last image are combined, without calculating the parameter S;

[0047] If there are n = 8k images, they can be combined in groups of 4, arranged in a square matrix according to combination method 2. If high overall image quality is required, parameter S can be calculated. If general image quality is required, the calculation of parameter S can be omitted.

[0048] Table 1. Compression effect of four standard test images

[0049]

[0050] Table 2 shows the compression effect of two standard test images.

[0051]

[0052] Table 3 shows the compression effect of two standard test images.

[0053]

[0054] This invention provides a performance-balanced compression method for satellite images, improving the compression performance of images with poor compression quality, ensuring similar compression quality across multiple images, and enhancing the balance of compression performance to meet user requirements. Compared to the original standard method, this invention significantly improves the overall image quality balance, reduces image differences, and lowers the PSNR difference from 12dB to approximately 1dB-3dB. The quality of previously poorly compressed images is greatly improved, while the quality of previously high-compression images, although slightly reduced, remains essentially unchanged in visual quality. The objective evaluation metric is Peak Signal-to-Noise Ratio (PSNR). Simulation experiments with typical images demonstrate that the improvement in compression quality for previously poorly compressed images is greater than 2dB-6dB.

[0055] The contents not described in detail in this specification are common knowledge to those skilled in the art.

Claims

1. A performance-balanced compression method for satellite images, characterized in that, include: Rearrange the n images in the satellite image set X = X1, X2, ... Xn to obtain the image set Y = Y1, Y2, ... Yn, and record the sequence correspondence table between the satellite image set Y and X; Combine the images in the image set Y by concatenating them end to end, forming an image set Z = Z1, Z2, ..., Zk, where k is a positive integer less than or equal to n; Compress k images in image set Z to obtain data set C, store and transmit C; The receiving end decompresses the data set C to obtain a set of k images Z; Decompose the image set Z to obtain the image set Y; The image set X is recovered based on the sequence number correspondence table; The process of rearranging n images in the satellite image set X = X1, X2, ... Xn to obtain the image set Y = Y1, Y2, ... Yn includes: Calculate the parameter S for each image in the satellite image set X, namely S1, S2, ..., Sn. The parameter S refers to the average of the absolute value of the gray level difference between adjacent rows and the sum of the absolute values ​​of the gray level differences between adjacent columns in each satellite image. Rearrange the satellite images in order from high to low according to the value of parameter S to obtain the image set Y = Y1, Y2, ..., Yn.

2. The performance-balanced compression method for satellite images according to claim 1, characterized in that, The process of combining images in image set Y to form image set Z = Z1, Z2…Zk includes: The combination method uses two images: the first image and the last image of Y. Z1=[Y1,Yn], Z2=[Y2,Yn-1], where k=n / 2 and n is a multiple of 2.

3. The performance-balanced compression method for satellite images according to claim 1, characterized in that, The process of combining images in image set Y to form image set Z = Z1, Z2…Zk includes: The combination method uses a combination of four images: the first two images and the last two images. Z1=[Y1, Y2, Yn-1, Yn], Z2=[Y3, Y4, Yn-3, Yn-2], where k=n / 4 and n is a multiple of 4.

4. The performance-balanced compression method for satellite images according to claim 1, characterized in that, The process of combining images in image set Y to form image set Z = Z1, Z2…Zk includes: The combination method uses a combination of three images: the first image and the last two images. Z1 = [Y1, Yn-1, Yn], Z2 = [Y2, Yn-3, Yn-2], where k = n / 3 and n is a multiple of 3.

5. A performance-balanced compression method for satellite images according to any one of claims 1-4, characterized in that, When performing compression or decompression, JPEG2000 compression / decompression is used, with a compression ratio of 4 to 12 times.

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