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A method and system for image lossless compression

A lossless compression and image technology, applied in the field of image processing, can solve the problems of reduced compression rate, slow decoding speed, low compression rate, etc., to achieve the effect of guaranteed compression rate and convenient transplantation

Active Publication Date: 2022-05-31
HEFEI I TEK OPTOELECTRONICS CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] Image lossless compression means that after data compression, the information is not lost and can be completely restored to the original state before compression. The current image lossless compression schemes generally include arithmetic coding-based schemes and general-purpose text-based schemes, both of which have their own advantages and disadvantages , the arithmetic coding scheme has a good compression ratio, but the decoding speed is relatively slow, and the universal text scheme has a fast decoding speed, but the compression ratio is low
[0003] If the image lossless compression algorithm is transplanted to the FPGA, when compressing the image, it can only be compressed line by line; if the general text method is used, the compression rate will be reduced to between 1.0 and 1.2 due to too little compressed data line by line

Method used

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  • A method and system for image lossless compression
  • A method and system for image lossless compression
  • A method and system for image lossless compression

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0095] {0,0,0,0,0,0,0,0,0,0,0,0,

[0096] 2,2,2,2,2,2,2,2,2,

[0097] 4,4,4,4,4,4,

[0098] 8,8,8,8,

[0099] 16, 32, 33, 34}

[0100] It consists of 12 0s, 8 2s, 6 4s, 4 8s, and 1 16, 32, 33, 34 respectively. First put the symbol with the smallest probability

[0101] The text symbol table at this time becomes 12 8s, 8 2s, 6 4s, 4 8s, and 2 A and B respectively. selector again

[0103] If the left branch of the Huffman tree is represented by bit=1, and the right branch is represented by bit=0, then the final obtained

[0105]

[0107] Input: treeLenth: the height of the tree, that is, the number of layers

[0109] Count the number of leaf nodes of each layer, to the array perRankNum[]

[0110] Starting from the bottom of the Huffman tree, calculate the initial value of the self-increment, and save it to the array perRankVal[]

[0111] startValue=0;

[0112] for n=treeLenth: 1;

[0113] per RankVal[n]=startValue;

[0114] startValue=startValue+perRankNum[n]

[0115] startValue=st...

example 2

[0119] Let the top node be the 0th layer, then count the number of leaf nodes, when they are the second layer, 3 leaf nodes and 1 in the third layer

[0122]

[0124] 1) Define a cost value, and the tree keeps changing structure in the direction of decreasing cost value.

[0126]

[0130]

[0134] S43, decode using the compressed encoding table and encoded data.

[0139]

[0143]

[0144] ZSTD uses the above weights to describe the encoding table, and reconstructs the original Huffman table at the decoding end. it uses

[0147]

[0148]

[0151] {0...0,1,0,0,0,0,0,0,1,0,0,2,1,0,1,0,3,0,5,0 ,1,6,0,2,3,0,1,0,1,1,

[0153] {(41,0),(1,1),(6,0),(1,1),(2,0),(1,2),(1,1),(1,0 ),1,0,3,0,5,0,1,6,0,

[0154] A total of 34 bytes, plus the encoded value of 32 bytes, a total of 66 bytes. Compared with FSE, it is compressed to 48 words

[0155] It should be noted that the following will prove the effectiveness of the fusion algorithm of the present invention by means of experimental verificat...

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Abstract

The invention discloses an image lossless compression method and system, belonging to the technical field of image processing, comprising: obtaining an original image; calculating the residual of the original image by using an arithmetic coding method, counting the number of occurrences of each symbol in the residual, and storing Enter the array counts[]; according to the array counts[], loop to form a Huffman tree, and use the method of limiting the length to encode each leaf node of the Huffman tree to obtain the Huffman encoding table; use Huffman encoding The table compresses the residual of the original image to obtain coded data for decoding. The invention proposes a lossless compression method which combines arithmetic coding and general text compression method, and realizes the decoding speed of the general text method while ensuring the compression rate.

Description

A kind of image lossless compression method and system technical field [0001] The present invention relates to the technical field of image processing, and in particular, to a method and system for lossless image compression. Background technique Image lossless compression means that the information is not lost after the data is compressed, and it can be completely restored to the original image before compression. Similarly, current image lossless compression schemes generally include arithmetic coding-based schemes and general text-based schemes. Each has its own advantages and disadvantages. The arithmetic coding scheme has a good compression rate, but the decoding speed is slow. The general text scheme decoding Fast, but with low compression. [0003] If the image lossless compression algorithm is transplanted to the FPGA, the compression can only be performed line by line when compressing the image. Compression; if the general text method is used, the compressio...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04N19/91H04N19/42G06T9/00G06T9/40H03M7/40
CPCH04N19/91H04N19/42G06T9/005G06T9/40H03M7/40
Inventor 祖慈邵云峰李博川
Owner HEFEI I TEK OPTOELECTRONICS CO LTD
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