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Image compression sensing method based on perceptual and random displacement

A random permutation and image compression technology, applied in the field of image processing, can solve the problem of not considering human visual characteristics, and achieve the effect of removing visual redundancy, increasing sparsity, and improving sparsity.

Inactive Publication Date: 2017-03-22
SHANGHAI UNIV
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  • Abstract
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AI Technical Summary

Problems solved by technology

However, the compressive sensing coding method does not consider human visual characteristics at the beginning, i.e. there is still room for further improvement

Method used

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  • Image compression sensing method based on perceptual and random displacement
  • Image compression sensing method based on perceptual and random displacement
  • Image compression sensing method based on perceptual and random displacement

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Embodiment Construction

[0025] Preferred embodiments of the present invention are described in detail as follows:

[0026] In this example, see Figures 1 to 4 , this embodiment utilizes the visual perception characteristic and the image compression method of the compressed sensing technology, and specifically adopts the following technical scheme, and the steps are as follows: figure 1 shown in the flowchart. The method of the present invention is implemented by programming on the matlab2014a platform. First, the image is subjected to block DCT transformation, and then the JND model of the DCT domain is established, and the DCT coefficients are preprocessed by using this model, and then the processed data are randomly scrambled, and finally Complete random measurements. At the decoding end, the restoration and reconstruction of the image is achieved through the inverse process.

[0027] In this example, see Figures 1 to 4 , an image compressed sensing method based on perception and random permu...

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Abstract

The invention discloses an image compression sensing method based on perceptual and random displacement. The method comprises the following steps: segmenting an image into nXn image blocks; conducting DCT transformation to each image block; utilizing a JND module to calculate the JND value in the DCT domain; utilizing the JND value to process the original DCT coefficient wherein the DCT value smaller than the JND value is reset as zero and the DCT value greater than the JND value stays unchanged; after that, conducting random replacement to the processed data; using a random matrix to observer for a measurement value of compression sensing; and finally, restoring the received data. With the method of the invention, the complexity of the coding end can be reduced and the coding end is enabled to have the ability to prevent data loss, making it applied in areas where the coding end has a poor computing ability but a strong decoding end and remarkably improving the compression efficiency.

Description

technical field [0001] The invention relates to an image processing method, in particular to an image and video compression method, which is applied to data management and data transmission technology utilization. Background technique [0002] The amount of image data is generally large, and if it is directly transmitted without compressing it, it will cause a great burden on the existing network resources. Although there are many more efficient image compression algorithms, the most common ones are JPEG, H.261, MPEG and so on. However, these algorithms often require complex calculations on the signal, and remove redundant information in the image through operations. Therefore, these compression methods have high requirements on the performance of the computer, and are not suitable for occasions with high power consumption restrictions on the encoding end, such as wireless sensor networks. [0003] Compressed sensing technology, which has been gradually developed in recent...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04N19/154H04N19/176H04N19/625H04N19/90
CPCH04N19/154H04N19/176H04N19/625H04N19/90
Inventor 王永芳吴健朱康华朱芸
Owner SHANGHAI UNIV
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