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Image compression perception method fused with information source channel decoding

A channel decoding and image compression technology, applied in the field of image processing, can solve the problems of low data throughput rate and low reconstruction accuracy, and achieve the effect of high throughput rate and accurate image compression sensing reconstruction

Inactive Publication Date: 2019-01-11
TIANJIN UNIV
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Problems solved by technology

The traditional image compression sensing method has low reconstruction accuracy and low data throughput rate. In order to comply with the development trend of the big data era and meet the needs of high-speed data acquisition and transmission and massive image and video data storage, it is urgent to propose a new image compression sensing method.

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  • Image compression perception method fused with information source channel decoding
  • Image compression perception method fused with information source channel decoding

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Embodiment Construction

[0029] The present invention mainly adopts the mode of simulation experiment to verify the feasibility of the system model, and all the steps have been verified by experiments. In order to realize the image compression sensing method of fusion source channel decoding, the specific implementation steps are as follows:

[0030] Step 1: Sparse Transformation

[0031] According to the standard method of generating discrete wavelet transform matrix, a discrete wavelet transform matrix of size n×n is generated, denoted as W 1 ,Such as figure 2 As shown, the wavelet subband HH 1 , LH 1 , HL 1 , HH 2 , LH 2 , HL 2 ,…,LL n The probabilities of the coefficients taking non-zero values ​​are denoted as η 1 , η 2 , η 3 , η 4 , η 5 , η 6 ,...,η 3n+1 ;

[0032] Step 2: Homogenize

[0033] Order the wavelet subbands according to the size of the sparsity (η 1 2 = η 3 4 5 = η 6 3n+1 ) is arranged by row into a sparse matrix W with fixed row values 2 , the N data in each col...

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Abstract

The present invention relates to an image compression perception method fused with information source channel decoding. The method comprises the steps of: sparse transformation; homogenization: wavelet subbands are arranged in order according to the sizes of the sparsity to a sparse matrix W2 with a fixed line value, and N data in each column expresses one vector to be observed; sampling observation; channel coding: the signals obtained through sampling are coded through adoption of RS codes; channel transmission; decoding reconstruction: the received codeword information is demodulated, and then the codeword information is subjected to calculation through a syndrome, solution through a key equation, chien search and a Forney algorithm module to obtain correct codeword information, and finally, a reconstructed column vector is obtained through a key equation solution module and a chien-search Forney algorithm module, and the reconstructed column vector is converted to a coefficient matrix A; inverse homogenization; and inverse sparse transformation.

Description

technical field [0001] The invention belongs to the field of image processing, and mainly relates to an image compression sensing method based on channel coding theory. Background technique [0002] Compressed sensing theory points out that for signals with sparse characteristics, the signal can still be accurately reconstructed using limited sampling samples when the signal is observed at a rate much lower than the Nyquist sampling rate. Since the natural image signal does not have sparse characteristics in the pixel domain, it is first necessary to use discrete wavelet transform or discrete cosine transform to realize the conversion of the signal from the pixel domain to the frequency domain, and obtain the characteristic signal with sparse characteristics composed of transform domain coefficients, and then The feature signal is subjected to compressed sensing sampling. Since the number of samples after sampling is much lower than the number of transform domain coefficien...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04N19/63H04N19/44H04N19/132G06T9/00
CPCG06T9/00H04N19/132H04N19/45H04N19/63
Inventor 梁煜王浩张为
Owner TIANJIN UNIV