Multimedia image compression method based on non-correlation chaos observation matrix

Abstract

The invention discloses a multimedia image compression method based on a non-correlation chaos observation matrix. The method comprises the steps that a video to be detected is read; under the condition that compression ratio is determined, observation frequency is determined; a Logistic mapping system is selected to produce a chaos factor; through the chaos factor, the first row of the observation matrix is determined, and a sequential cycle method is used to acquire vectors of other rows of the observation matrix; a non-correlation factor is introduced to strengthen the column non-correlation of the observation matrix; for each element moving from the tail end to the front end, the element is multiplied by the non-correlation factor; each time the element moves, the element is multiplied by the non-correlation factor in a superposed manner; and QR decomposition is used to process the observation matrix to acquire an orthogonal observation matrix based on non-correlation chaos. Compared with similar algorithms, the method provided by the invention has the advantages that a storage space restriction is made up; sufficient column non-correlation is met; a multimedia image is well compressed; the quality of a reconstructed image is great; the method is robust for different multimedia images; and the method is highly practical.

Description

technical field [0001] The invention belongs to the technical field of multimedia image transmission, and relates to an image compression method based on compressed sensing, in particular to a multimedia image compression method based on a non-correlated chaotic observation matrix. Background technique [0002] Signal transmission is inseparable from signal compression. Traditional compression methods can represent the original signal with a relatively small number of bits, perform large-scale high-speed sampling on the original analog signal to obtain the original digital discrete signal, and discard most of the unimportant information to reduce the final output. The actual data size is convenient for transmission and storage. This method is based on the limitation of the Nyquist sampling theorem, which requires that the sampling frequency must be higher than twice the highest frequency of the signal to ensure that the original signal can be reconstructed without distortion...

AI Technical Summary

Problems solved by technology

[0006] (1) The random observation matrix reconstruction effect based on compressed sensing is good, but the computational complexity is high and the storage space is large, so it is not easy to implement by hardware;

[0007] (2) The deterministic observation matrix based on compressed sensing occupies a small storage space and is easy to implement in hardware, but the reconstruction effect is poor, there is no perfect theoretical basis, and the robustness and universality are poor

[0008] (3) The observation matrix based on chaos theory has excellent pseudo-randomness, but it cannot guarantee sufficient column non-correlation, and the reconstruction effect has not been significantly improved

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