Compressive sensing-based image decoding method

Abstract

The invention relates to a compressive sensing-based image decoding method. The compressive sensing-based image decoding method comprises the following steps of: performing compressive sensing (CS) reconstruction on an image signal acquired through inverse quantization at a decoding end, wherein the CS reconstruction performed on the image signal can be realized by solving the optimization problem of norm of the following formula; and converting the solved column vector into a matrix to decode the image. In order to improve the quality of the CS reconstruction, the method also comprises the step of performing block combination on the image, namely combining p*p image blocks into one image block, before performing the CS reconstruction on the image signal, wherein the line number / column number of the combined image block is p times that of the image blocks before the combination. In the method, the value of a TV operator on the block edge is further improved; when i is equal to n in the image block matrix In*n, a horizontal operator is defined as Ii-1, j-Iij; and when j is equal to n, a vertical operator is defined as Ii, j-1-Iij. All the improvement of the method is concentrated at the decoding end and an encoding end does not need any alteration, so a better effect can be achieved compared with the conventional image compression standard.

Description

technical field [0001] The invention relates to an image decoding method, in particular to an image decoding method based on compressed sensing. Background technique [0002] In recent years, there are several widely used image compression coding standards, such as JPEG and JPEG2000, which use pixel redundancy to reduce the bit rate, so JPEG and JPEG2000 use transformation and quantization. Correspondingly, their decoders use inverse quantization and inverse transformation to maintain consistency between the encoder and decoder. [0003] Transformation is an operation for a signal to realize the mapping relationship between the time domain and the transformation domain. The essence of transformation is to project the signal on a set of basis functions to obtain a set of projection values, that is, the transformation domain expression of the signal. The purpose of transformation is to analyze what information is useful in the original signal, and to study how to extract thes...

AI Technical Summary

Problems solved by technology

[0004] When the signal is lossless or nearly lossless in the transform domain, the inverse transform method is the optimal method for signal reconstruction, however, when the signal has large noise in the transform domain, the inverse transform method is not necessarily the optimal reconstruction method

At the same time, both JPEG and JPEG2000 use quantization and dequantization, so it will also cause quantization noise and dequantization noise

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