Image compressed sensing method based on Reed-Solomon codes
An image compression and image 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 perception reconstruction
Inactive Publication Date: 2019-01-18
TIANJIN UNIV
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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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[0031] The present invention mainly adopts the mode of simulation experiment to verify the feasibility of this system model, and all steps are all through experimental verification, in order to realize the image compression sensing method based on Reed-Solomon code, concrete implementation steps are as follows:
[0032] Step 1: Sparse Transformation
[0033] According to the standard method of generating discrete cosine transform matrix, the image to be observed ( figure 2 ) to transform a discrete cosine transform matrix of size n×n, denoted as C 1 , a large number of important coefficients are concentrated in the upper left corner of the matrix, and they contain the main information of the image, such as image 3 shown;
[0034] Step 2: Quantization Denoising
[0035] According to the sampling rate of the image, set a reasonable threshold to denoise and quantize the coefficient matrix. At this time, the sparseness distribution of the vector to be observed is as follows: ...
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The invention relates to an image compressed sensing method based on Reed Solomon codes, and the method comprises the steps: sparse transformation; quantization denoising; homogenization: rearrangingdata streams in a quantized sparse matrix by columns, wherein the length of the column should not be equal to the column length of an original matrix and has a large difference with the column lengthof the original matrix, so that non-zero coefficients can be uniformly distributed into each row vector, and then each row vector of the coefficient matrix represents a newly composed vector to be observed; sampling observation; channel coding; channel transmission; decoding and reconstruction: obtaining a reconstructed column vector by a key equation solving module and a Chien search Forney algorithm module, and converting the reconstructed column vector into a coefficient matrix A; inverse homogenization; inverse sparse transform.
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 is a priori cognition that makes full use of signal sparsity in the process of information perception, and realizes signal compression while information perception; through a small amount of observation data, combined with high-efficiency sparse signal restoration algorithms, the signal is undistorted perception. For a signal with good sparse characteristics, sampling the signal at a rate lower than the Nyquist sampling rate can still achieve accurate reconstruction of the signal with limited sampling samples. However, many target signals in the real world are not sparse. Therefore, it is first necessary to use sparse transformation to convert the signal from the pixel domain to the frequency domain to obtain a feature signal with sparse characteristics, and t...
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IPC IPC(8): H04N19/625H04N19/124H04N19/85
CPCH04N19/124H04N19/625H04N19/85
Inventor 梁煜王浩张为
Owner TIANJIN UNIV