Video compression sampling method for high-speed and low-power wireless sensor network

Abstract

The invention belongs to the technical field of wireless sensor network video compression sampling and aims to provide a low-complexity low-power-consumption encoding end so as to guarantee long-term stable operation, and provide a novel video compression sampling frame and an encoding / decoding strategy based on compressed sensing for the storage of a great quantity of data and complex decoding computation of a receiving end. Therefore, according to the adopted technical scheme, a high-speed low-power-consumption wireless sensor network video compression sampling method comprises the following steps: controlling a compression sampling rate and a frame rate of a video signal according to the time of receiving a sampling video signal and the bit error probability requirement, performing compressed sensing-based video differential encoding so as to finish compression encoding on the video signal; restoring a video image frame by adopting a decoding strategy which corresponds to the compressed sensing-based video differential encoding at a video decoding end, and remotely controlling the time of receiving the sampling video signal and the bit error probability requirement. The method is mainly applied to wireless sensor network video transmission processing.

Description

technical field [0001] The invention belongs to the technical field of wireless sensor network video compression sampling, and in particular relates to video coding and decoding technology based on compressed sensing, and specifically relates to a high-speed and low-power wireless sensor network video compression sampling strategy. Background technique [0002] Traditional video coding methods, whether it is H.26X series or MPEG series traditional video coding technology, all adopt a system structure with complex encoding end and simple decoding end, that is, the encoding end eliminates Temporal and spatial redundancy to obtain high compression efficiency, which makes the entire system have very high requirements on the computing power and memory capacity of the encoder, much higher than that of the decoder. [0003] At present, the video coding method based on Compressed Sensing (CS) is also widely known and applied. Compressed Sensing (CS) is an emerging theory in the fiel...

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Problems solved by technology

[0004] However, the existing video coding method based on Compressed Sensing (CS) lacks feedback control, and its stability and robustness are poor. To achieve further video signal compression, the complexity of the reconstruction algorithm and the sparsity of the video signal itself will be greatly reduced. Double the improvement

Improved types such as distributed compressive sensing video coding (DVCS), joint sparse based compressive sensing video coding (JSMCS), joint sparse based distributed compressive sensing video coding (JSM-DVCS), etc. have largely become It improves the compression rate, stability and robustness of video, but it is difficult to meet the computational complexity requirements of these encoding methods for resource-constrained embedded wireless sensor networks

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