Image lossless compression method and system

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: 2020-05-01
HEFEI I TEK OPTOELECTRONICS CO LTD
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  • Summary
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  • 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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Examples

Experimental program
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example 1

[0094] Example 1: Use Huffman to encode the following text symbols:

[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 16, 32, 33, 34 respectively. First, any two of the symbols with the smallest probability form a node, such as Figure 6 shown. The new node A formed, the number of times P=1+1=2 of its symbol occurrence; This node is regarded as a new symbol and added in the text symbol table again; Select 33,34 in the text symbol table again to form a new Node B.

[0101] The text symbol table at this time becomes 12 8s, 8 2s, 6 4s, 4 8s and 2 A and B respectively. Select A and B with the smallest number of occurrences of symbols again to form a new node C, and C consists of A and B to form its cotyledons, such as Figure 7 shown.

[0102] Select the smallest symbol in turn, and so on, until all symbols are allocated, forming a Huffman tree s...

example 2

[0118] Example 2: Using the above method, re-solve the Huffman encoding of the text symbol in Example 1 as follows:

[0119] Let the top node be the 0th layer, then count the number of leaf nodes, which are 3 leaf nodes in the second layer, 1 leaf node in the third layer, and 4 leaf nodes in the fifth layer. Therefore perRankNum={0,0,3,1,0,4}. The obtained perRankVal={0,2,1,1,2,0}. Since the layer where the symbols 0, 2, and 4 are located is the second layer, their self-increment value is 1, that is, 0 corresponds to 01, 2 corresponds to 10, and 4 corresponds to 11. The layer where 8 is located is the third layer, so 8 corresponds to 01. 16, 32, 33, and 34 correspond to the fifth layer, and their codes are 00, 01, 10, and 11 respectively.

[0120] Use decimal instead of binary representation, and its encoding symbols are shown in Table 3 below:

[0121] table 3

[0122] symbol 0 2 4 8 16 32 33 34 encoded value 1 2 3 1 0 1 2 3 length 2 ...

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Abstract

The invention discloses an image lossless compression method and system, and belongs to the technical field of image processing. The method comprises: obtaining an original image; calculating the residual error of the original image by adopting an arithmetic coding method, counting the occurrence frequency of each symbol in the residual error, and storing the occurrence frequency into an array counts []; circularly establishing a Huffman tree according to the array counts [], and encoding each leaf node of the Huffman tree by using a length limiting method to obtain a Huffman encoding table; and compressing the residual error of the original image by using the Huffman coding table to obtain coded data for decoding processing. According to the lossless compression method integrating arithmetic coding and the general text compression method, the decoding speed of the general text method is achieved while the compression rate is guaranteed.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image lossless compression method and system. Background technique [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 rate, but the decoding speed is relatively slow, and the universal text scheme has a fast decoding speed, but a low compression rate. [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 l...

Claims

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Application Information

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Patent Type & Authority Applications(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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