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A Method of Video Sequence Reconstruction Based on Compressive Sensing

A video sequence and compressed sensing technology, which is applied in digital video signal modification, image communication, electrical components, etc., can solve problems such as increasing the complexity of codecs and wasting sampling resources, achieving good video quality, shortening reconstruction time, The effect of small amount of data

Active Publication Date: 2020-04-10
FUZHOU UNIV
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

This not only caused a serious waste of sampling resources, but also greatly increased the complexity of the codec

Method used

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  • A Method of Video Sequence Reconstruction Based on Compressive Sensing
  • A Method of Video Sequence Reconstruction Based on Compressive Sensing
  • A Method of Video Sequence Reconstruction Based on Compressive Sensing

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Embodiment Construction

[0028] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0029] Such as figure 1 As shown, the present invention provides a method for reconstructing video sequences based on compressed sensing, comprising the steps of:

[0030] (1) Measurement process

[0031] Step A1: grouping the original video sequence into groups of image groups GOP, and dividing them into n image groups;

[0032] Step A2: Set the image group size to m, and the first frame in each image group is the key frame , the second frame to the mth frame are non-key frames , ,…, , i is the serial number of the image group, i=1,2, ...,n;

[0033] Step A3: Sampling the key frames of each image group in blocks at a high sampling rate, corresponding to , i=1,2,...,n, perform sub-block sampling on the non-key frames of each image group at a low sampling rate, corresponding to get , ,…, , i=1,2,...,n;

[0034] (2) Refactoring process ...

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Abstract

The invention discloses a video sequence reconstruction method based on compressed sensing. An encoding end separates an original video sequence into two types of frames, i.e., key frames and non-key frames, and measurement is performed respectively by use of different sampling rates. At a decoding end, different video sequences are subjected to performance judgement of two different algorithms, i.e., motion estimation / compensation prediction and optical flow method prediction, and then through combination with a comparison result, all the non-key frames are subjected to good-performance algorithm reconstruction by use of inter-frame correlation . When a video is processed, the amount of data needing to be sampled is small and the quality of the recovered video is good. Compared with a traditional algorithm of video independent reconstruction, the peak signal-to-noise ratio at a low sampling rate is improved and the reconstruction time is shortened.

Description

technical field [0001] The invention relates to a video sequence reconstruction method based on compressed sensing. Background technique [0002] In the traditional hybrid coding technology architecture, the video signal is usually fully sampled first, and then compressed, a large amount of redundant data is discarded, leaving only a small amount of important data. This not only causes a serious waste of sampling resources, but also greatly increases the complexity of the codec. Compressed sensing is a new type of information processing theory, which enables the signal to be sampled at a rate much lower than the Nyquist sampling rate, and the original signal can still be reconstructed with a high probability at the decoding end, which can be realized based on a small number of observations Accurate reconstruction of the signal. [0003] In the compressed sensing framework, the core problem is to use the sparsity of the signal to measure and reconstruct the sparse signal cl...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04N19/172H04N19/177H04N19/42H04N19/51
CPCH04N19/172H04N19/177H04N19/42H04N19/51
Inventor 陈建兰诚栋苏忆艳陈淡
Owner FUZHOU UNIV
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