CN111882564A - Compression processing method for ultra-high definition medical pathological image - Google Patents
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Abstract
The invention relates to a compression processing method of an ultra-high definition medical pathological image, which comprises the steps of firstly converting the format of the medical pathological image obtained by a medical digital slice scanning system into a common image format, then extracting the image into a plurality of sub-images through blocking and raster scanning processing and generating YUV video sequences, then carrying out lossless and lossy compression coding on the obtained sequences by adopting an HEVC video coding tool, wherein the compressed files can be used for storing and transmitting the images, and finally reconstructing a complete high-resolution medical image through decoding the reconstructed video sequences so as to meet the watching requirements of the images. The invention can realize effective compression of the medical image with ultrahigh resolution in a smaller distortion range, which is beneficial to the remote consultation of experts and promotes the realization and popularization of remote intelligent medical treatment.
Description
Technical Field
The invention relates to a compression processing method of an ultra-high definition medical pathological image, belonging to the technical field of medical image processing.
Background
In the medical field, medical resources are unevenly distributed, and cross-region medical treatment is difficult, which is always a pain point for the development of the medical industry. With the rapid development of internet technology and information technology, the interconnection of everything has advanced into the aspects of life, and the arrival of the 5G era makes remote intelligent medical treatment an important way for seeking medical services in different places.
In order to realize the remote consultation of experts, pathological sections need to be digitized and stored through a digital section scanning system, and technologies such as microscopic image processing, Web image browsing and the like are integrated, so that the same section can be browsed by multiple people at different places at the same time, the common consultation of medical experts in different areas is realized through instant communication in modes such as voice, video and the like, and good news is brought to the reduction of the difference of the medical level in the areas.
In medical imaging, accurate diagnosis and assessment of diseases depend largely on the quality of acquired medical images, and the higher the image quality is, the more abundant the information can be expressed, which is more favorable for accurate diagnosis of diseases. In recent years, the image acquisition quality has improved significantly, and medical scanning devices have acquired data at a faster rate and with higher resolution, and a scanned digital slice image has a pixel resolution of several billions, supporting up to 80 times magnification, and a file size of several hundred megabits or several gigabytes. The ultrahigh image resolution ensures the doctor to accurately judge the pathological information, but on the other hand, the local storage and transmission of the images have higher requirements on the equipment capacity and the network bandwidth. Even though there is a dedicated cloud server for storing large-size medical images, it is a concern to effectively compress medical images in the face of a large amount of patient information and medical needs for long-term storage.
On the other hand, in the multimedia field, video is an important form of recording visual information, which occupies at least 83% of all information that people can feel, and compression coding is required for effective storage and transmission of video. Video coding is to remove the space-time redundant information in the video and convert the video sequence into a compact binary code stream for storage and transmission, and a great deal of effort has been made in the academic and industrial circles in the past decades to establish a series of video coding standards and continuously improve the video coding efficiency. ITU-T establishes H.261 and H.263, ISO/IEC establishes MPEG-1 and MPEG-4Visual, ITU-T and ISO/IEC establish H.262/MPEG-2Video, H.264/MPEG-4AVC (advanced Video Coding), H.265/MPEG-H HEVC (High Efficiency Video Coding) standards together, the compression performance of HEVC is obviously improved compared with AVC, and lossless compression and lossy compression are simultaneously supported to meet different application requirements.
In summary, video coding comprises the following steps: the method comprises the steps of intra-frame/inter-frame prediction, transformation, quantization and entropy coding, wherein the intra-frame prediction and the inter-frame prediction can respectively remove spatial and temporal redundant information of a video frame, so that effective compression of the video is realized.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a compression processing method of an ultra-high definition medical pathological image;
from the multimedia perspective, each frame of a video is an image, and a plurality of consecutive images are videos. Therefore, the medical pathological image with ultrahigh resolution can be compressed and coded by an efficient video coding tool so as to be stored and transmitted conveniently. The invention can realize effective compression of the medical pathological image with ultrahigh resolution based on an efficient video coding tool.
Interpretation of terms:
1. ultra High Definition (HD), meaning "High resolution", is High Definition, and the High resolution is 720p (1280 × 720), 1080p (1920 × 1080) being common. Ultra High Definition (Ultra HD) is the formal name of "4K resolution (3840 × 2160)" recently approved by the international telecommunications union, and this name also applies to "8K resolution (7680 × 4320)". In the present invention, ultra high definition refers to a case of 4K resolution or 8K resolution and above.
2. An HEVC Video coding tool, HEVC (high Efficiency Video coding), h.265, is a high Efficiency Video coding standard, and is used to replace h.264/AVC, No. 26/1/2013, and HEVC formally becomes an international standard. HEVC continues to use the well-accepted hybrid coding framework adopted since h.263, including several parts, intra prediction, inter prediction, transform, quantization, entropy coding. Compared with h.264, HEVC adopts many new technologies, mainly including: a large-size quadtree-based segmentation structure, a residual coding structure, adaptive motion parameter coding, an adaptive loop filter and the like. Coding Units (CU), Prediction Units (PU), and Transform Units (TU) are introduced in HEVC, intra prediction modes are increased from 9 to 35 of h.264, and entropy coding employs adaptive binary arithmetic coding (CABAC). The improvement of the technologies improves the coding performance of the HEVC standard, and the compression efficiency of HEVC on high-resolution video images is doubled on the basis of H.264/AVC.
The technical scheme of the invention is as follows:
a compression processing method of ultra-high definition medical pathological images comprises the following steps:
(1) image format conversion
The format of the medical pathological image obtained by the medical digital slice scanning system is related to a specific scanning device, and special viewing software is needed, so that the format of the medical image needs to be converted into a common image format before subsequent processing.
(2) Generating images into YUV video sequence
A. Extracting sub-images
Dividing the medical pathological image obtained after the format conversion in the step (1) into a plurality of subblocks with equal size, wherein the size of each subblock can be determined according to specific conditions. For each sub-block, reading all pixels in each sub-block in a specific scanning mode, extracting pixels at the same position in each sub-block, and forming a sub-image according to the extraction sequence and position to obtain a group of sub-images;
B. generating YUV video sequences
And converting the color mode of the obtained group of sub-images into YUV from RGB, and sequentially writing the YUV into a file according to the sequence of the sub-images to generate a video sequence in a YUV format.
(3) Compression coding
Performing lossless compression coding and lossy compression coding on the YUV video sequence obtained in the step (2) by adopting an HEVC video coding tool to obtain a compressed bit stream; where the lossy compression coding can control the degree of quality loss by the size of the coding parameter QP.
Lossless compression can ensure that information of a video sequence is not lost in the encoding and decoding processes, and the quality loss degree caused by lossy compression can be controlled by a coding parameter QP.
Since medical images are finally watched by experts, and the sensitivities of human eyes to different texture regions are different, the invention adopts an optimized encoder for compression encoding besides a standard HEVC video encoder. The optimized encoder adopts a multi-distortion criterion rate distortion optimization method based on texture characteristics to optimize the rate distortion process of HEVC. The method adopts a structural similarity index SSIM (structural similarity) consistent with human eye perception, takes the weighting of SSE (Sumof Square error) distortion and SSIM distortion as a distortion criterion in a rate distortion process, and takes the corresponding Lagrangian multiplier as the weighting of the corresponding Lagrangian multiplier, and the weighting parameter is determined by the texture degree of an area. The method can improve the subjective quality of the reconstructed video and can also maintain the fidelity of the video.
(4) Decoding reconstructed original image
C. Extracting reconstructed sub-images
Sequentially extracting each frame of image of the reconstructed video sequence obtained by decoding, converting the color mode of each frame of image obtained by decoding from YUV (YUV) to RGB (Red, Green and blue) color mode, and storing to obtain a group of reconstructed sub-images;
D. reconstructing a complete medical image
The order of the sub-images represents the positions of the pixels in the image at the respective sub-blocks during scanning, for example, the pixel in the first sub-image corresponds to the pixel at the first position of the respective sub-block during scanning. And (3) restoring the plurality of sub-images into a complete high-resolution image according to the scanning rule in the step (2), and finishing the reconstruction of the medical image.
According to the present invention, in step (1), the image format conversion means: the medical pathology image is converted into bmp format. Common image formats include bmp, png, tif, jpg, and the like, where the bmp adopts a bit mapping storage format, and does not adopt any compression except that the image depth is selectable, and is mainly applied to processing of uncompressed images, large images, and fineness. Since the quality of medical pathological images has an important influence on the final medical judgment, it is to be ensured that the information of the images is not lost as much as possible during the format conversion process, and the quality of the images is protected to the greatest extent.
According to the present invention, in step a, the medical pathology image obtained by format conversion in step (1) is divided into 8 × 8, 16 × 16, 32 × 32 or 64 × 64 sub-blocks of equal size. The larger the sub-blocks, the more sub-images are obtained, while the smaller the resolution of each sub-image, the smaller the sub-blocks, the fewer the sub-images are obtained, and the greater the resolution of each sub-image.
Preferably, in step a, all pixels in each sub-block are read in a Raster Scan (Raster-Scan) order from left to right and from top to bottom. Raster scanning refers to scanning a line from left to right and from top to bottom, and then moving to the starting position of the next line to continue scanning.
Preferably, in step a, all pixels in each sub-block are read by a zigzag scanning (Z-Scan) method. Zigzag scanning means that the scanning order is like the letter "Z", and the specific scanning process is as shown in fig. 2.
Preferably, in step a, all pixels in each sub-block are read by an interlaced scanning method. Interlaced scanning refers to scanning odd lines first and then even lines during the scanning process.
Preferably, in step B, RGB conversion into YUV is achieved by formulas (i), (II), (iii):
Y＝0.299R+0.587G+0.114B(Ⅰ)
U＝-0.169R-0.331G+0.500B+128(II)
V＝0.500R-0.419G-0.081B+128(Ⅲ)
in the formulas (I), (II) and (III), Y is a brightness signal, U and V are color difference signals, and R, G, B respectively refers to the colors of red, green and blue channels; commonly used YUV formats are YUV444 and YUV 420. YUV444 is that each pixel is composed of three YUV components, YUV data occupies 8 bits each, and YUV420 is a result of sampling the UV component. The invention respectively generates YUV444 and YUV420 format sequences for subsequent coding of a plurality of sub-images.
Preferably, in step (3), the quality loss caused by lossy compression is controlled by the coding parameter QP, and in order to ensure the image quality, the invention uses a smaller QP, wherein QP is 1, 2, 3, 4, 5 or 6.
Preferably, in step C, YUV conversion to RGB is achieved by formulae (iv), (v), (vi):
R＝Y+1.403×(V-128)(Ⅳ)
G＝Y–0.343×(U–128)–0.714×(V–128)(Ⅴ)
B＝Y+1.770×(U–128)(Ⅵ)。
the invention has the beneficial effects that:
1. the invention is suitable for the compression processing method of the ultra-high definition medical pathological image, and the medical image is converted into the video sequence by processing the medical image such as blocking, raster scanning and the like, so that the medical image with ultra-high resolution can be effectively compressed by utilizing an efficient video coding tool, and the local storage and transmission of the medical image are facilitated.
2. According to the invention, the YUV420 sequence is generated from the medical image and HEVC lossy compression is performed by using a multi-distortion criterion, so that the compression rate of the image can be further improved, the medical image can be effectively compressed in a smaller distortion range, and the quality of the image is not greatly influenced.
3. The invention can reconstruct a complete medical image with ultrahigh resolution by the video sequence obtained by decoding, can meet the watching requirement of the medical image, is beneficial to the remote consultation of experts and promotes the realization and popularization of remote intelligent medical treatment.
Drawings
FIG. 1 is a schematic flow chart of the compression processing method of the ultra-high definition medical pathological image according to the invention;
FIG. 2 is a schematic diagram of a method of zigzag scanning;
FIG. 3(a) is a schematic diagram of the extraction of sub-images by raster scanning;
FIG. 3(b) is a schematic diagram of the reduction of multiple sub-images into a complete high resolution image;
FIG. 4(a) is a schematic diagram of an original image;
fig. 4(b) is a schematic diagram of an image after lossy compression reconstruction by generating a YUV444 sequence (QP ═ 1) from the original image shown in fig. 4 (a);
fig. 4(c) is a schematic diagram of an image after lossy compression reconstruction by generating a YUV444 sequence (QP ═ 3) from the original image shown in fig. 4 (a);
fig. 4(d) is a schematic diagram of an image after lossy compression reconstruction by generating a YUV444 sequence (QP ═ 6) from the original image shown in fig. 4 (a);
FIG. 5(a) is a schematic diagram of an original image;
fig. 5(b) is a schematic diagram of an image after lossy compression reconstruction with a multi-distortion criterion by generating a YUV444 sequence (QP ═ 1) from the original image shown in fig. 5 (a);
fig. 5(c) is a schematic diagram of an image after lossy compression reconstruction with a multi-distortion criterion by generating a YUV444 sequence (QP ═ 3) from the original image shown in fig. 5 (a);
fig. 5(d) is a schematic diagram of an image after lossy compression reconstruction with a multi-distortion criterion by generating a YUV444 sequence (QP ═ 6) from the original image shown in fig. 5 (a);
FIG. 6(a) is a schematic diagram of an original image;
fig. 6(b) is a schematic diagram of an image after lossy compression reconstruction by generating a YUV420(QP ═ 1) sequence from the original image shown in fig. 6 (a);
fig. 6(c) is a schematic diagram of an image after lossy compression reconstruction in the original image generation YUV420(QP ═ 3) sequence shown in fig. 6 (a);
fig. 6(d) is a schematic diagram of an image after lossy compression reconstruction in the original image generation YUV420(QP ═ 6) sequence shown in fig. 6 (a);
FIG. 7(a) is a schematic diagram of an original image;
fig. 7(b) is a schematic diagram of an image after lossy compression reconstruction with a multi-distortion criterion by using the YUV420 sequence (QP ═ 1) generated from the original image shown in fig. 7 (a);
fig. 7(c) is a schematic diagram of an image after lossy compression reconstruction with a multi-distortion criterion by using the YUV420 sequence (QP ═ 3) generated from the original image shown in fig. 7 (a);
fig. 7(d) is a schematic diagram of an image after lossy compression reconstruction with a multi-distortion criterion using the YUV420 sequence (QP 6) generated from the original image shown in fig. 7 (a).
Detailed Description
The invention is further defined in the following, but not limited to, the figures and examples in the description.
Example 1
A compression processing method for ultra high definition medical pathology images, as shown in fig. 1, includes the following steps:
(1) image format conversion
The format of the medical pathological image obtained by the medical digital slice scanning system is related to a specific scanning device, and special viewing software is needed, so that the format of the medical image needs to be converted into a common image format before subsequent processing.
The medical pathology image is converted into a 24-bit bmp format to maximally preserve the quality of the image. Common image formats include bmp, png, tif, jpg, and the like, where the bmp adopts a bit mapping storage format, and does not adopt any compression except that the image depth is selectable, and is mainly applied to processing of uncompressed images, large images, and fineness. Since the quality of medical pathological images has an important influence on the final medical judgment, it is to be ensured that the information of the images is not lost as much as possible during the format conversion process, and the quality of the images is protected to the greatest extent.
(2) Generating images into YUV video sequence
A. Extracting sub-images
After the medical image with ultrahigh resolution is subjected to format conversion, the size of the medical image exceeds the input range of an encoding tool, so that the medical image cannot be directly subjected to compression encoding. Dividing the medical pathological image obtained after the format conversion in the step (1) into a plurality of subblocks with equal size, wherein the size of each subblock can be determined according to specific conditions. For each sub-block, reading all pixels in each sub-block from left to right and from top to bottom in a raster scanning mode, extracting pixels at the same position in each sub-block, and forming a sub-image according to the extraction sequence and position to obtain a group of sub-images; the sub-block size selected in this embodiment is 16 × 16, and 256 sub-images are extracted in total. Taking an image resolution of 16 × 16 and a sub-block size of 4 × 4 as an example, a sub-image is extracted by raster scanning, as shown in fig. 3 (a).
B. Generating YUV video sequences
Since the input requirement of HEVC is a sequence in YUV format, the color pattern of the 256 bmp sub-images obtained needs to be converted from RGB to YUV, and a video sequence is generated in sequence.
And converting the color mode of the obtained group of sub-images into YUV from RGB, and sequentially writing the YUV into a file according to the sequence of the sub-images to generate a video sequence in a YUV format. Namely: reading the pixel information of the image, converting the pixel information into a YUV color mode, and writing Y, U, V three-component information into a file according to the sequence of sub-images to obtain a video sequence file in a YUV format.
The conversion of RGB into YUV is realized through formulas (I), (II) and (III):
Y＝0.299R+0.587G+0.114B(Ⅰ)
U＝-0.169R-0.331G+0.500B+128(II)
V＝0.500R-0.419G-0.081B+128(Ⅲ)
in the formulas (I), (II) and (III), Y is a brightness signal, U and V are color difference signals, and R, G, B respectively refers to the colors of red, green and blue channels; commonly used YUV formats are YUV444 and YUV 420. YUV444 is that each pixel is composed of three YUV components, YUV data occupies 8 bits each, and YUV420 is a result of sampling the UV component. The invention respectively generates YUV444 and YUV420 format sequences for subsequent coding of a plurality of sub-images.
(3) Compression coding
And performing lossless and lossy compression coding on the obtained YUV444 and YUV420 sequences by adopting an HEVC coding tool to obtain compressed bit streams. Where the lossy compression coding can control the degree of quality loss by the size of the coding parameter QP.
Lossless compression can ensure that information of a video sequence is not lost in the encoding and decoding processes, and the quality loss degree caused by lossy compression can be controlled by a coding parameter QP. The degree of quality loss due to lossy compression is controlled by the coding parameter QP, which is 1, 2, 3, 4, 5 or 6, and the present invention uses a smaller QP in order to ensure the quality of the image.
FIG. 4(a) is a schematic diagram of an original image; fig. 4(b) is a schematic diagram of an image after lossy compression reconstruction by generating a YUV444 sequence (QP ═ 1) from the original image shown in fig. 4 (a); fig. 4(c) is a schematic diagram of an image after lossy compression reconstruction by generating a YUV444 sequence (QP ═ 3) from the original image shown in fig. 4 (a); fig. 4(d) is a schematic diagram of an image after lossy compression reconstruction by generating a YUV444 sequence (QP ═ 6) from the original image shown in fig. 4 (a).
FIG. 5(a) is a schematic diagram of an original image; fig. 5(b) is a schematic diagram of an image after lossy compression reconstruction with a multi-distortion criterion by generating a YUV444 sequence (QP ═ 1) from the original image shown in fig. 5 (a); fig. 5(c) is a schematic diagram of an image after lossy compression reconstruction with a multi-distortion criterion by generating a YUV444 sequence (QP ═ 3) from the original image shown in fig. 5 (a); fig. 5(d) is a schematic diagram of an image after lossy compression reconstruction with a multi-distortion criterion by generating a YUV444 sequence (QP ═ 6) for the original image shown in fig. 5 (a).
FIG. 6(a) is a schematic diagram of an original image; fig. 6(b) is a schematic diagram of an image after lossy compression reconstruction by generating a YUV420(QP ═ 1) sequence from the original image shown in fig. 6 (a); fig. 6(c) is a schematic diagram of an image after lossy compression reconstruction in the original image generation YUV420(QP ═ 3) sequence shown in fig. 6 (a); fig. 6(d) is a schematic diagram of an image after lossy compression reconstruction in the original image generation YUV420(QP ═ 6) sequence shown in fig. 6 (a).
FIG. 7(a) is a schematic diagram of an original image; fig. 7(b) is a schematic diagram of an image after lossy compression reconstruction with a multi-distortion criterion by using the YUV420 sequence (QP ═ 1) generated from the original image shown in fig. 7 (a); fig. 7(c) is a schematic diagram of an image after lossy compression reconstruction with a multi-distortion criterion by using the YUV420 sequence (QP ═ 3) generated from the original image shown in fig. 7 (a); fig. 7(d) is a schematic diagram of an image after lossy compression reconstruction with a multi-distortion criterion using the YUV420 sequence (QP 6) generated from the original image shown in fig. 7 (a).
Since medical images are finally watched by experts, and the sensitivities of human eyes to different texture regions are different, the invention adopts an optimized encoder for compression encoding besides a standard HEVC video encoder. The optimized encoder adopts a multi-distortion criterion rate distortion optimization method based on texture characteristics to optimize the rate distortion process of HEVC. The method adopts a structural similarity index SSIM (structural similarity) consistent with human eye perception, takes the weighting of SSE (Sumof Square error) distortion and SSIM distortion as a distortion criterion in a rate distortion process, and takes the corresponding Lagrangian multiplier as the weighting of the corresponding Lagrangian multiplier, and the weighting parameter is determined by the texture degree of an area. The method can improve the subjective quality of the reconstructed video and can also maintain the fidelity of the video.
(4) Decoding reconstructed original image
C. Extracting reconstructed sub-images
Sequentially extracting each frame of image of the reconstructed video sequence obtained by decoding, converting the color mode of each frame of image obtained by decoding from YUV (YUV) to RGB (Red, Green and blue) color mode, and storing to obtain a group of reconstructed sub-images;
YUV is converted into RGB through formulas (IV), (V) and (VI):
R＝Y+1.403×(V-128)(Ⅳ)
G＝Y–0.343×(U–128)–0.714×(V–128)(Ⅴ)
B＝Y+1.770×(U–128)(Ⅵ)。
D. reconstructing a complete medical image
The order of the sub-images represents the positions of the pixels in the image at the respective sub-blocks during scanning, for example, the pixel in the first sub-image corresponds to the pixel at the first position of the respective sub-block during scanning. And (3) restoring the plurality of sub-images into a complete high-resolution image according to the scanning rule in the step (2), and finishing the reconstruction of the medical image. Taking an image resolution of 16 × 16 and a sub-block size of 4 × 4 as an example, the multiple sub-images are restored to a complete high-resolution image, as shown in fig. 3 (b).
The effect of this example can be further illustrated by experiments. Table 1 compares the information of the original medical pathology image and the converted file, table 2 compares the result of compression-encoding the generated YUV444 sequence, and table 3 compares the result of compression-encoding the generated YUV420 sequence.
TABLE 1
| File type | Resolution ratio | Data size |
| Original medical image file (kfb format) | 38029*38182 | 265M |
| Converting the generated bmp image | 18944*18944 | 1G |
| Generated YUV444 sequence | 1184*1184 | 1G |
| Generated YUV420 sequence | 1184*1184 | 513M |
TABLE 2
TABLE 3
As shown in tables 1, 2, and 3, the size of the medical image file selected in this embodiment is 265M, and after the file is converted into a YUV444 sequence, lossy compression with QP of 6 is performed by HEVC, and the size of the obtained file is 113M, which is reduced by half compared with the original file data amount; after conversion into YUV420 sequence, lossy compression with QP 6 with HEVC results in a file size of 63.3M, about one quarter of the original file size. The obtained YUV444 and YUV420 sequences are subjected to HEVC lossy coding with a multi-distortion criterion, the data volume after compression coding is smaller under the condition of the same QP, and meanwhile, the quality of a reconstructed image cannot be reduced. Experimental results show that the invention can realize effective compression of medical images in a smaller distortion range.
Example 2
The compression processing method for the ultra-high definition medical pathological image according to embodiment 1 is different from the following steps: in step A, all pixels in each sub-block are read by a zigzag scanning (Z-Scan) method. Zigzag scanning means that the scanning order is like the letter "Z", and the specific scanning process is as shown in fig. 2.
Example 3
The compression processing method for the ultra-high definition medical pathological image according to embodiment 1 is different from the following steps: in step a, all pixels in each sub-block are read by an interlaced scanning method. Interlaced scanning refers to scanning odd lines first and then even lines during the scanning process.
Claims (9)
1. A compression processing method for ultra-high definition medical pathological images is characterized by comprising the following steps:
(1) image format conversion
(2) Generating images into YUV video sequence
A. Extracting sub-images
Dividing the medical pathological image obtained after format conversion in the step (1) into a plurality of subblocks with equal sizes, scanning and reading all pixels in each subblock for each subblock, extracting pixels at the same position in each subblock, and forming a sub-image according to the extraction sequence and position to obtain a group of sub-images;
B. generating YUV video sequences
Converting the color mode of the obtained group of sub-images from RGB into YUV, and sequentially writing the YUV into a file according to the sequence of the sub-images to generate a video sequence in a YUV format;
(3) compression coding
Performing lossless compression coding and lossy compression coding on the YUV video sequence obtained in the step (2) by adopting an HEVC video coding tool to obtain a compressed bit stream;
(4) decoding reconstructed original image
C. Extracting reconstructed sub-images
Sequentially extracting each frame of image of the reconstructed video sequence obtained by decoding, converting the color mode of each frame of image obtained by decoding from YUV (YUV) to RGB (Red, Green and blue) color mode, and storing to obtain a group of reconstructed sub-images;
D. reconstructing a complete medical image
And (3) restoring the plurality of sub-images into a complete high-resolution image according to the scanning rule in the step (2), and finishing the reconstruction of the medical image.
2. The method for compressing ultrahigh-definition medical pathological image according to claim 1, wherein in the step (1), the image format conversion is: the medical pathology image is converted into bmp format.
3. The method for compressing ultra high definition medical pathological image according to claim 1, wherein in the step a, the medical pathological image obtained by the format conversion in the step (1) is divided into 8 × 8, 16 × 16, 32 × 32 or 64 × 64 sub-blocks with equal size.
4. The method for compressing ultra-high definition medical pathological image according to claim 1, wherein in the step a, all pixels in each sub-block are read in a raster scanning manner from left to right and from top to bottom.
5. The method for compressing ultrahigh-definition medical pathological image according to claim 1, wherein in the step a, all pixels in each sub-block are read by zigzag scanning.
6. The method for compressing ultrahigh-definition medical pathological image according to claim 1, wherein in the step a, all pixels in each sub-block are read by an interlaced scanning method.
7. The method for compressing ultrahigh-definition medical pathological image according to claim 1, wherein in the step B, RGB conversion into YUV is realized by formulas (I), (II) and (III):
Y＝0.299R+0.587G+0.114B (I)
U＝-0.169R-0.331G+0.500B+128 (II)
V＝0.500R-0.419G-0.081B+128 (III)
in the formulas (I), (II) and (III), Y is a brightness signal, U and V are color difference signals, and R, G, B respectively refer to the colors of red, green and blue channels.
8. The method for compressing ultrahigh-definition medical pathological image according to claim 1, wherein in the step (3), the quality loss caused by lossy compression is controlled by a coding parameter QP, wherein the QP is 1, 2, 3, 4, 5 or 6.
9. The method for compressing ultrahigh-definition medical pathological image according to any one of claims 1 to 8, wherein in the step C, YUV is converted into RGB by the following formulas (IV), (V) and (VI):
R＝Y+1.403×(V-128) (IV)
G＝Y-0.343×(U-128)-0.714×(V-128) (V)
B＝Y+1.770×(U-128) (VI)。
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