A Compressed Sensing Image Coding and Transmission Method

Abstract

The invention provides a compressed sensing image encoding transmission method. The method comprises the following steps: 1) performing random scrambling rearrangement on pixels of an input image at asending end; 2) dividing the image with scrambled pixels into image blocks which have the same size and are not overlapped with each other; 3) performing separable measurement on each image block byusing a partial Hadamard matrix; 4) quantizing and stratifying random measurement values; 5) quantizing bit transmission; 6) receiving information to obtain quantized bits; 7) performing inverse quantization on the quantized bits; 8) obtaining an initial reconstruction image by using a reconstruction method; 9) estimating the random measurement value of an enhancement layer through the initial reconstruction image; 10) updating the random measurement value of the enhancement layer by using a maximum posterior probability estimation algorithm to obtain an updated value of the random measurementvalue of the enhancement layer; and 11) obtaining a final reconstruction image by using the reconstruction method. The compressed sensing image encoding transmission method has the advantages of lowencoder complexity, high packet loss resistance and high rate distortion performance.

Description

technical field [0001] The invention belongs to the field of image transmission, in particular to a compression sensing image coding transmission method. Background technique [0002] In the wireless video sensor network, a large number of video sensor nodes are arranged in the application scene in a self-organized or limited controlled manner. These sensor nodes continuously obtain the image or video data of the application scene, and transmit it back to the terminal platform through wireless means. The terminal platform analyzes and makes decisions on the returned node data. There is a lot of redundancy in image and video data in wireless video sensor networks, and it is necessary to compress the image data on the sensor; considering the unreliability of wireless channel transmission, it is necessary to perform channel coding on the compressed data on the sensor to enhance Its robustness against packet loss. However, hardware modules such as on-chip microprocessors, memo...

AI Technical Summary

Problems solved by technology

However, hardware modules such as on-chip microprocessors, memories, wireless modules, and sensors are insufficient in energy supply and limited in computing performance. Although the traditional joint source channel coding standards have achieved great success, these transmission standards are facing coding challenges. Complicated, unable to adapt to channel changes and other issues, it is not suitable for wireless video sensor networks

There are huge challenges in image coding and transmission in wireless sensor networks. Existing image transmission solutions cannot meet the requirements of simple encoder, strong anti-packet loss, and high rate-distortion performance at the same time.

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