Holographic image information processing system

Abstract

The invention discloses a holographic image information processing system, which comprises an edge calculation and data preprocessing module, a data compression and coding module, a holographic image information processing module, a holographic image information processing module, a holographic image information processing module, a holographic image information processing module, a holographic image information processing module and a holographic image information processing module, wherein the edge calculation and data preprocessing module is responsible for acquiring original holographic image data from holographic image acquisition equipment or a storage system; according to the holographic image compression method, lossless compression or lossy compression is decided to be adopted by judging image quality requirements, image data needing to keep high quality of a holographic image are compressed by adopting a Huffman coding lossless compression algorithm, and image data receiving quality loss are compressed by adopting a JPEG lossy compression algorithm. The system can intelligently select a lossless or lossy compression algorithm according to the image quality requirement, effectively reduces the data volume of the holographic image, and guarantees the image quality. The parallel transmission and streaming media processing module can transmit a plurality of data blocks at the same time by using a multi-path parallel transmission technology, so that the overall transmission time is remarkably shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of holographic image processing, and in particular to a holographic image information processing system. Background Art

[0002] Holographic technology is a technology that uses the principles of interference and diffraction to record and reproduce the true three-dimensional image of an object. It breaks through the limitations of traditional sound, light, and electricity, and can present the characteristics of bright colors, high contrast, and high clarity in spatial imaging, while also having a strong sense of space and perspective. Holographic technology records all the information (amplitude, phase, etc.) in the light waves reflected or transmitted by the object, so that the reproduced image can accurately reflect the three-dimensional appearance of the object. Holographic images are characterized by a huge amount of data because they record all the image information of the object, including amplitude, phase, brightness, shape distribution, etc. This comprehensive recording method gives holographic images a significant advantage in presenting the three-dimensional effect of objects.

[0003] In the prior art, holographic images usually contain a large amount of detailed information, resulting in a very large amount of data. When transmitting these large-scale image files, the existing network and transmission technology often face the problem of transmission delay. For example, in telemedicine, doctors use holographic images to view the patient's three-dimensional visceral structure and evaluate the lesion site. However, the patient's holographic image data is very large and needs to be transmitted remotely through the Internet. Due to the limitation of transmission bandwidth, image transmission may encounter long delays, resulting in doctors being unable to obtain the required image information in time, thereby affecting diagnostic efficiency. Therefore, a holographic image information processing system is proposed. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that existing holographic images usually contain a large amount of detailed information, resulting in a very large amount of data. When transmitting these large-scale image files, the existing network and transmission technology often face the problem of transmission delay, and a holographic image information processing system is proposed.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A holographic image information processing system, comprising:

[0007] Edge computing and data preprocessing module: responsible for obtaining the original holographic image data from the holographic image acquisition device or storage system, and preprocessing the original holographic image data, such as denoising, contrast enhancement, and preliminary compression processing, which can reduce the amount of data to be transmitted and improve the image quality, laying the foundation for subsequent efficient transmission and reconstruction, and transmitting the preprocessed holographic image data to the data compression and encoding module;

[0008] Data compression and encoding module: by judging the image quality requirements, it is decided to use lossless compression or lossy compression. The Huffman coding lossless compression algorithm is used to compress image data that needs to maintain high quality of holographic images. When the holographic image needs to be accurately reconstructed for medical diagnosis, scientific research or high-precision measurement, lossless compression is used. The image data that accepts quality loss is compressed using the JPEG lossy compression algorithm. When sharing holographic images on social media, since users usually pay more attention to the visual effect of the image rather than the accuracy, lossy compression can be used to reduce the file size and increase the loading speed. The compressed image data is converted into binary format, and the compressed and encoded data is transmitted to the data segmentation module.

[0009] Data block module: responsible for dividing the compressed holographic image data into multiple data blocks, each of which contains a part of the image information, and identifying and managing each data block, and transmitting the divided multiple data blocks to the parallel transmission and streaming media processing module;

[0010] Parallel transmission and streaming media processing module: responsible for using multi-path parallel transmission technology to simultaneously transmit multiple data blocks to the holographic image decoding and reconstruction module. During the parallel transmission process, it manages the sending and receiving of data packets to ensure that there is no packet loss or congestion during the transmission process. At the same time, it supports streaming media processing functions to ensure that image data transmission is efficient and has low latency;

[0011] Holographic image decoding and reconstruction module: responsible for receiving parallel transmitted image data streams, ensuring that all data blocks are received in sequence, reordering data blocks when necessary, and decoding the received image data according to the compression coding algorithm to restore the original pixel data of the image. For lossy compression, the image obtained after decoding may be different from the original image; for lossless compression, the image will be completely restored, and the decoded image data will be reconstructed into a holographic image in sequence.

[0012] The above technical solution further includes:

[0013] Preferably, the specific steps of acquiring the original holographic image data from the holographic image acquisition device or storage system are:

[0014] The edge computing and data preprocessing module initializes the connection with the holographic image acquisition device or storage system, including establishing network communication, verifying identity, configuring necessary parameters, and then identifying the type of connected device or system, which helps determine the subsequent data acquisition method and processing flow;

[0015] Based on the recognition results, the edge computing and data preprocessing module sends a data request to the holographic image acquisition device or storage system, including the required image data range, resolution, format and other information;

[0016] After receiving the request, the holographic image acquisition device or storage system starts to transmit the original holographic image data;

[0017] During the data reception process, the edge computing and data preprocessing modules perform preliminary data integrity checks, including verifying the integrity of the data packet, confirming the order and continuity of the data, etc.

[0018] Preferably, the specific steps of determining whether to use lossless compression or lossy compression by judging the image quality requirement are:

[0019] Image quality assessment: The image quality is assessed by calculating the image's signal-to-noise ratio (SNR) and structural similarity index (SSIM). The signal-to-noise ratio (SNR) calculation formula is: Among them, x i,j is the original pixel value, is the pixel value after decoding. If the SNR is high, it means that the quality requirement is high and lossless compression is suitable. The calculation formula of the structural similarity index SSIM is: Among them, μ x and are the means of the original image and the reconstructed image, σ X and is the variance, is the covariance, C1 and C2 are constants to prevent zero errors;

[0020] Compression algorithm selection: When SNR ≥ 30dB (it is considered that the visual loss is minimal and the quality is very high) and SSIM ≥ 0.98 (close to the original image), a lossless compression algorithm is used; when SNR < 30dB and SSIM < 0.98, a lossy compression algorithm is used.

[0021] Preferably, the specific steps of using Huffman coding lossless compression for image data that needs to maintain high quality of the holographic image are:

[0022] Calculate the frequency of each pixel value: for the preprocessed image data X p Each pixel value in the image is counted and its frequency of occurrence is calculated. Suppose there are N different pixel values ​​x1, x2, ..., x n , in image X p The frequencies in are p1, p2, ..., p n , then the frequency of each pixel value p(x i )for: Where W×H represents the total number of pixels in the image;

[0023] Construct a Huffman tree: treat all pixel values ​​and their frequencies as leaf nodes, sort them by frequency from small to large, select the two nodes with the smallest frequency to merge, and form a new node whose frequency is the sum of the frequencies of the two nodes. Repeat this process until all nodes are merged into a Huffman tree. The construction process of the Huffman tree can be expressed as a recursive merging process. Suppose there are currently n nodes. Each merging step in the construction process will reduce the number of nodes until a tree is finally obtained.

[0024] Generate Huffman code: Through the Huffman tree, each pixel value is assigned a variable-length binary code. The Huffman code length is L(x i ) corresponds to its depth in the tree, let the pixel value x i The Huffman code of i ), then C(x i )'s length L(x i ) is the path length in the Huffman tree, and the total length of the compressed image data is L c for: Among them, L(x i ) is the length of the Huffman code;

[0025] Lossless compression: Use Huffman coding to convert each pixel value x i Replace it with its corresponding Huffman code C(x i ), the compressed data stream is a binary stream, expressed as: D c =HuffmanEncode(X p ).

[0026] Preferably, the specific steps of compressing the image data subject to quality loss using the JPEG lossy compression algorithm are:

[0027] Image segmentation: Divide the image into N small blocks. Let the original image be I. Then each block B of the image k It is expressed as: I = {B1, B2, ..., B N}, where B k is the kth block, N is the total number of blocks in the image, and each small block B k Contains 8×8 pixel values;

[0028] Discrete cosine transform: For each image block B k Perform discrete cosine transform to convert the image from the spatial domain to the frequency domain. The goal of DCT is to concentrate low-frequency information in the upper left corner and high-frequency information in the lower right corner to facilitate subsequent compression. The DCT formula is: Among them, Y k (i,j) is the frequency coefficient matrix after transformation, B k(m,n) is the image block B k The original pixel value, M is the size of the image block, i, j is the index of the transformed frequency coefficient;

[0029] Quantization: After DCT processing, the frequency coefficients will be quantized according to the quantization matrix. By dividing by the quantization matrix Q(i,j) and rounding, the accuracy of the low-frequency part is retained and the high-frequency part is discarded. The quantization formula is: Among them, QY k (i, j) is the quantized frequency coefficient, Q(i, j) is the quantization matrix, which is used to control the quantization strength. The low-frequency part is quantized more finely, and the high-frequency part is quantized more coarsely. is a rounding operation;

[0030] Zig-zag scanning and Huffman coding: The quantized frequency coefficients are rearranged in a Zig-zag scanning manner to better utilize Huffman coding to compress the frequency coefficients. The goal of Zig-zag scanning is to put the low-frequency coefficients in front, and generate a Huffman tree and encode them based on the frequency of occurrence of the frequency coefficient sequence after scanning.

[0031] Preferably, the specific steps of dividing the compressed holographic image data into a plurality of data blocks are:

[0032] Conduct preliminary analysis of the image content, such as image size, compression quality, data integrity requirements, etc.

[0033] Define the size of each data block according to system requirements and dynamically adjust it according to the complexity of the image;

[0034] The compressed image data is divided into a plurality of continuous data blocks according to the data block size, and each data block includes an image data portion, identification information and control data.

[0035] Preferably, the specific process of decoding the received image data according to the compression coding algorithm and restoring the original pixel data of the image is:

[0036] Lossless decoding: read the Huffman-coded bit stream, decode it according to the Huffman tree, restore the compressed bit stream to the original pixel data, and restore the decoded data to the pixel value of the original image;

[0037] Lossy decoding: Receive compressed DCT coefficients, inverse quantize the DCT coefficients of the compressed image to restore frequency domain data close to the original, perform inverse DCT transform on the inverse quantized DCT coefficients to restore the pixel values ​​of the image, combine the results of the inverse DCT transform with the original image data to restore pixel values ​​close to the original.

[0038] Preferably, the specific process of sequentially reconstructing the decoded image data into a holographic image is:

[0039] Assume the decoded pixel data blocks are I1, I2, ..., I n , assemble the decoded pixel information into a complete image in the correct position order: I final =Merge(I1,I2,...,I n ), where the Merge operation is to concatenate multiple decoded image blocks;

[0040] The restored image data I final Convert to holographic image: Among them, H final is the final reconstructed holographic image, represents inverse Fourier transform;

[0041] After holographic image reconstruction, the obtained holographic image H final The image is output to the display device to complete the image display.

[0042] The present invention has the following beneficial effects:

[0043] 1. In the present invention, through the data compression and encoding module, the system can intelligently select lossless or lossy compression algorithms according to the image quality requirements, effectively reduce the data volume of the holographic image, and ensure the image quality. The parallel transmission and streaming media processing module uses multi-path parallel transmission technology to transmit multiple data blocks at the same time, thereby significantly shortening the overall transmission time. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a system architecture diagram of a holographic image information processing system proposed in the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] like Figure 1 As shown, a holographic image information processing system comprises:

[0047] Edge computing and data preprocessing module: responsible for obtaining the original holographic image data from the holographic image acquisition device or storage system, preprocessing the original holographic image data, and transmitting the preprocessed holographic image data to the data compression and encoding module;

[0048] Data compression and encoding module: by judging the image quality requirements, it is decided to use lossless compression or lossy compression. The image data that needs to maintain the high quality of the holographic image is compressed using the Huffman coding lossless compression algorithm. The image data that accepts quality loss is compressed using the JPEG lossy compression algorithm. The compressed image data is converted into binary format and the compressed and encoded data is transmitted to the data segmentation module.

[0049] Data block module: responsible for dividing the compressed holographic image data into multiple data blocks, each of which contains a part of the image information, and identifying and managing each data block, and transmitting the divided multiple data blocks to the parallel transmission and streaming media processing module;

[0050] Parallel transmission and streaming media processing module: responsible for using multi-path parallel transmission technology to simultaneously transmit multiple data blocks to the holographic image decoding and reconstruction module. During the parallel transmission process, it manages the sending and receiving of data packets and supports streaming media processing functions.

[0051] Holographic image decoding and reconstruction module: responsible for receiving the parallel transmitted image data stream, decoding the received image data according to the compression coding algorithm, restoring the original pixel data of the image, and reconstructing the decoded image data into a holographic image in sequence.

[0052] In an embodiment of the present invention, the edge computing and data preprocessing module performs preprocessing at the source of data generation (i.e., near the holographic image acquisition device or storage system). The system can reduce the amount of data transmitted to the core network, thereby reducing the network burden. The data compression and encoding module selects a suitable compression method according to the image quality requirements through lossless compression and lossy compression algorithms to reduce the size of the holographic image data. Lossless compression guarantees image quality, while lossy compression further reduces the amount of data while accepting a certain quality loss. This can significantly reduce the data transmission burden and improve transmission efficiency. The system divides the compressed image data into multiple small data blocks for parallel transmission. Each data block can be transmitted independently, and supports the simultaneous transmission of multiple data blocks through multi-path parallel transmission technology, thereby greatly improving the data transmission rate and reducing the delay in the transmission process. The parallel transmission and streaming media processing module of the system not only supports efficient data transmission, but also processes and adjusts the data stream in real time to ensure that the data is not lost during transmission and minimizes transmission delays. It is particularly suitable for scenarios that require real-time viewing and analysis, such as telemedicine.

[0053] In one embodiment, the specific steps of acquiring the original holographic image data from the holographic image acquisition device or storage system are:

[0054] The edge computing and data preprocessing module initializes the connection with the holographic image acquisition device or storage system, and then identifies the type of the connected device or system;

[0055] Based on the recognition results, the edge computing and data preprocessing module sends a data request to the holographic image acquisition device or storage system;

[0056] After receiving the request, the holographic image acquisition device or storage system starts to transmit the original holographic image data;

[0057] During the data reception process, the edge computing and data preprocessing modules perform preliminary data integrity checks.

[0058] In the embodiment of the present invention, the module identifies the type of connected device or system to help determine the subsequent data acquisition method and processing flow. For example, if the data is acquired from a holographic image acquisition device, a specific data acquisition protocol may need to be started; if the data is acquired from a storage system, the corresponding storage path needs to be accessed.

[0059] In one embodiment, the specific steps of determining whether to use lossless compression or lossy compression by judging the image quality requirement are:

[0060] Image quality assessment: The image quality is assessed by calculating the image's signal-to-noise ratio (SNR) and structural similarity index (SSIM). The signal-to-noise ratio (SNR) calculation formula is: Among them, x i,j is the original pixel value, is the decoded pixel value, and the calculation formula of the structural similarity index SSIM is: Among them, μ x and are the means of the original image and the reconstructed image, σ X and is the variance, is the covariance, C1 and C2 are constants to prevent zero errors;

[0061] Compression algorithm selection: When SNR≥30dB and SSIM≥0.98, a lossless compression algorithm is used; when SNR<30dB and SSIM<0.98, a lossy compression algorithm is used.

[0062] In the embodiment of the present invention, the signal-to-noise ratio (SNR) is an indicator for measuring image quality, which represents the ratio of signal strength (i.e., the original information of the image) to noise (i.e., the distortion or error during the compression process). A higher SNR means less distortion of the image. SSIM measures the similarity of two images in structure, brightness, and contrast, and the value ranges from 0 to 1, where 1 means that the two images are exactly the same. An SSIM value close to 1 indicates that there is no obvious damage to the image structure during the compression process.

[0063] In one embodiment, the specific steps of using Huffman coding lossless compression for image data that needs to maintain high quality of holographic images are:

[0064] Calculate the frequency of each pixel value: for the preprocessed image data X p Each pixel value in the image is counted and its frequency of occurrence is calculated. Suppose there are N different pixel values ​​x1, x2, ..., x n , in image X p The frequencies in are p1, p2, ..., p n , then the frequency of each pixel value p(x i )for: Where W×H represents the total number of pixels in the image;

[0065] Construct a Huffman tree: treat all pixel values ​​and their frequencies as leaf nodes, sort them by frequency from small to large, select the two nodes with the smallest frequency to merge, and form a new node whose frequency is the sum of the frequencies of the two nodes. Repeat this process until all nodes are merged into a Huffman tree.

[0066] Generate Huffman code: Through the Huffman tree, a variable-length binary code is assigned to each pixel value. The Huffman code length is L(x i ) corresponds to its depth in the tree, let the pixel value x i The Huffman code of i ), then C(x i )'s length L(x i ) is the path length in the Huffman tree, and the total length of the compressed image data is L c for: Among them, L(x i ) is the length of the Huffman code;

[0067] Lossless compression: Use Huffman coding to convert each pixel value x i Replace it with its corresponding Huffman code C(x i ), the compressed data stream is a binary stream, expressed as: D c =HuffmanEncode(X p ).

[0068] In the embodiment of the present invention, Huffman coding is a lossless compression method that uses symbol frequency distribution to assign variable-length codes to different pixel values ​​in order to reduce redundancy during data transmission.

[0069] In one embodiment, the specific steps of compressing the image data that accepts quality loss using the JPEG lossy compression algorithm are as follows:

[0070] Image segmentation: Divide the image into N small blocks. Let the original image be I. Then each block B of the image k It is expressed as: I = {B1, B2, ..., B N}, where B k is the kth block, N is the total number of blocks in the image, and each small block B k Contains 8×8 pixel values;

[0071] Discrete cosine transform: For each image block B k Perform discrete cosine transform to convert the image from the spatial domain to the frequency domain. The DCT formula is: Among them, Y k (i,j) is the frequency coefficient matrix after transformation, B k (m,n) is the image block B k The original pixel value, M is the size of the image block, i, j is the index of the transformed frequency coefficient;

[0072] Quantization: After DCT processing, the frequency coefficients will be quantized according to the quantization matrix. By dividing by the quantization matrix Q(i,j) and rounding, the accuracy of the low-frequency part is retained and the high-frequency part is discarded. The quantization formula is: Among them, QY k (i,j) is the quantized frequency coefficient, Q(i,j) is the quantization matrix, is a rounding operation;

[0073] Zig-zag scanning and Huffman coding: The quantized frequency coefficients are rearranged in a Zig-zag scanning manner, and a Huffman tree is generated and encoded based on the frequency of occurrence of the frequency coefficient sequence after scanning.

[0074] In one embodiment, the specific steps of dividing the compressed holographic image data into multiple data blocks are:

[0075] Conduct a preliminary analysis of the image's content;

[0076] Define the size of each data block according to system requirements;

[0077] The compressed image data is divided into a plurality of continuous data blocks according to the data block size, and each data block includes an image data portion, identification information and control data.

[0078] In the embodiment of the present invention, the preliminary analysis includes: image size: the resolution of the holographic image and the amount of data after compression, compression quality: for lossy compressed images, it is necessary to ensure that the distribution of low-frequency and high-frequency information meets the requirements of block segmentation, data integrity requirements: depending on the compression method (such as lossless or lossy), the quality and integrity requirements of the image are also different, which affects the partitioning strategy. The size of the data block needs to consider the bandwidth and delay of the network transmission, the compression rate of the image and the balance of the size of each data block, and whether each data block is large enough to contain valid image information, but not too large to affect the transmission efficiency. Each data block includes: image data part: that is, the compressed or encoded image content, including image blocks, compressed DCT coefficients, quantized data, etc., identification information: used to identify the position of the data block in the entire image, as well as the compression type and encoding method of the block, control data: including information such as block size, compression ratio, decoding requirements, etc., which are metadata required by the decoding module.

[0079] In one embodiment, the specific process of decoding the received image data according to the compression coding algorithm and restoring the original pixel data of the image is as follows:

[0080] Lossless decoding: read the Huffman-coded bit stream, decode it according to the Huffman tree, restore the compressed bit stream to the original pixel data, and restore the decoded data to the pixel value of the original image;

[0081] Lossy decoding: Receive compressed DCT coefficients, inverse quantize the DCT coefficients of the compressed image to restore frequency domain data close to the original, perform inverse DCT transform on the inverse quantized DCT coefficients to restore the pixel values ​​of the image, combine the results of the inverse DCT transform with the original image data to restore pixel values ​​close to the original.

[0082] In the embodiment of the present invention, the formula of inverse DCT used in lossy compression is:

[0083] In one embodiment, the specific process of sequentially reconstructing the decoded image data into a holographic image is as follows:

[0084] Assume the decoded pixel data blocks are I1, I2, ..., I n , assemble the decoded pixel information into a complete image in the correct position order: I final =Merge(I1,I2,...,I n ), where the Merge operation is to concatenate multiple decoded image blocks;

[0085] The restored image data I final Convert to holographic image: Among them, H final is the final reconstructed holographic image, represents inverse Fourier transform;

[0086] After holographic image reconstruction, the obtained holographic image H final The image is output to the display device to complete the image display.

[0087] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A holographic image information processing system, characterized in that: include: Edge computing and data preprocessing module: responsible for obtaining the original holographic image data from the holographic image acquisition device or storage system, preprocessing the original holographic image data, and transmitting the preprocessed holographic image data to the data compression and encoding module; Data compression and encoding module: by judging the image quality requirements, it is decided to use lossless compression or lossy compression. The image data that needs to maintain the high quality of the holographic image is compressed using the Huffman coding lossless compression algorithm. The image data that accepts quality loss is compressed using the JPEG lossy compression algorithm. The compressed image data is converted into binary format and the compressed and encoded data is transmitted to the data segmentation module. Data block module: responsible for dividing the compressed holographic image data into multiple data blocks, each of which contains a part of the image information, and identifying and managing each data block, and transmitting the divided multiple data blocks to the parallel transmission and streaming media processing module; Parallel transmission and streaming media processing module: responsible for using multi-path parallel transmission technology to simultaneously transmit multiple data blocks to the holographic image decoding and reconstruction module. During the parallel transmission process, it manages the sending and receiving of data packets and supports streaming media processing functions. Holographic image decoding and reconstruction module: responsible for receiving the parallel transmitted image data stream, decoding the received image data according to the compression coding algorithm, restoring the original pixel data of the image, and reconstructing the decoded image data into a holographic image in sequence.

2. A holographic image information processing system according to claim 1, characterized in that: The specific steps of obtaining the original holographic image data from the holographic image acquisition device or storage system are: The edge computing and data preprocessing module initializes the connection with the holographic image acquisition device or storage system, and then identifies the type of the connected device or system; Based on the recognition results, the edge computing and data preprocessing module sends a data request to the holographic image acquisition device or storage system; After receiving the request, the holographic image acquisition device or storage system starts to transmit the original holographic image data; During the data reception process, the edge computing and data preprocessing modules perform preliminary data integrity checks.

3. A holographic image information processing system according to claim 1, characterized in that: The specific steps of determining whether to use lossless compression or lossy compression by judging the image quality requirement are: Image quality assessment: The image quality is assessed by calculating the image's signal-to-noise ratio (SNR) and structural similarity index (SSIM). The signal-to-noise ratio (SNR) calculation formula is: Among them, x i,j is the original pixel value, is the decoded pixel value, and the calculation formula of the structural similarity index SSIM is: Among them, μ x and are the means of the original image and the reconstructed image, σ X and is the variance, is the covariance, C1 and C2 are constants to prevent zero errors; Compression algorithm selection: When SNR≥30dB and SSIM≥0.98, a lossless compression algorithm is used; when SNR<30dB and SSIM<0.98, a lossy compression algorithm is used.

4. A holographic image information processing system according to claim 1, characterized in that: The specific steps of using Huffman coding lossless compression for image data that needs to maintain high quality of holographic images are: Calculate the frequency of each pixel value: for the preprocessed image data X p Each pixel value in the image is counted and its frequency of occurrence is calculated. Suppose there are N different pixel values ​​x1, x2, ..., x n , in image X p The frequencies in are p1, p2, ..., p n , then the frequency of each pixel value p(x i )for: Where W×H represents the total number of pixels in the image; Construct a Huffman tree: treat all pixel values ​​and their frequencies as leaf nodes, sort them by frequency from small to large, select the two nodes with the smallest frequency to merge, and form a new node whose frequency is the sum of the frequencies of the two nodes. Repeat this process until all nodes are merged into a Huffman tree. Generate Huffman code: Through the Huffman tree, each pixel value is assigned a variable-length binary code. The Huffman code length is L(x i ) corresponds to its depth in the tree, let the pixel value x i The Huffman code of i ), then C(x i )'s length L(x i ) is the path length in the Huffman tree, and the total length of the compressed image data is L c for: Among them, L(x i ) is the length of the Huffman code; Lossless compression: Use Huffman coding to convert each pixel value x i Replace it with its corresponding Huffman code C(x i ), the compressed data stream is a binary stream, expressed as: D c =HuffmanEncode(X p ).

5. The holographic image information processing system according to claim 1, characterized in that: The specific steps of compressing the image data that accepts quality loss using the JPEG lossy compression algorithm are as follows: Image segmentation: Divide the image into N small blocks. Let the original image be I. Then each block B of the image k It is expressed as: I = {B1, B2, ..., B N }, where B k is the kth block, N is the total number of blocks in the image, and each small block B k Contains 8×8 pixel values; Discrete cosine transform: For each image block B k Perform discrete cosine transform to convert the image from the spatial domain to the frequency domain. The DCT formula is: Among them, Y k (i,j) is the frequency coefficient matrix after transformation, B k (m,n) is the image block B k The original pixel value, M is the size of the image block, i, j is the index of the transformed frequency coefficient; Quantization: After DCT processing, the frequency coefficients will be quantized according to the quantization matrix. By dividing by the quantization matrix Q(i,j) and rounding, the accuracy of the low-frequency part is retained and the high-frequency part is discarded. The quantization formula is: Among them, QY k (i,j) is the quantized frequency coefficient, Q(i,j) is the quantization matrix, is a rounding operation; Zig-zag scanning and Huffman coding: The quantized frequency coefficients are rearranged in a Zig-zag scanning manner, and a Huffman tree is generated and encoded based on the frequency of occurrence of the frequency coefficient sequence after scanning.

6. A holographic image information processing system according to claim 1, characterized in that: The specific steps of dividing the compressed holographic image data into multiple data blocks are: Conduct a preliminary analysis of the image's content; Define the size of each data block according to system requirements; The compressed image data is divided into a plurality of continuous data blocks according to the data block size, and each data block includes an image data portion, identification information and control data.

7. A holographic image information processing system according to claim 1, characterized in that: The specific process of decoding the received image data according to the compression coding algorithm and restoring the original pixel data of the image is as follows: Lossless decoding: read the Huffman-coded bit stream, decode it according to the Huffman tree, restore the compressed bit stream to the original pixel data, and restore the decoded data to the pixel values ​​of the original image; Lossy decoding: Receive compressed DCT coefficients, inverse quantize the DCT coefficients of the compressed image to restore frequency domain data close to the original, perform inverse DCT transform on the inverse quantized DCT coefficients to restore the pixel values ​​of the image, combine the results of the inverse DCT transform with the original image data to restore pixel values ​​close to the original.

8. The holographic image information processing system according to claim 1, characterized in that: The specific process of sequentially reconstructing the decoded image data into a holographic image is as follows: Assume the decoded pixel data blocks are I1, I2, ..., I n , assemble the decoded pixel information into a complete image in the correct position order: I final =Merge(I1,I2,...,I n ), where the Merge operation is to concatenate multiple decoded image blocks; The restored image data I final Convert to holographic image: Among them, H final is the final reconstructed holographic image, represents inverse Fourier transform; After holographic image reconstruction, the obtained holographic image H final The image is output to the display device to complete the image display.

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