Open Access

ARTICLE

Video Compressed Sensing Reconstruction Based on Multi-Dimensional Reference Frame Multi Hypothesis Rediction

by Hua Li^1,*, Yuchen Yue², Jianhua Luo³

1 Army Armored Force Academy, Weapon and Control Department, Beijing, 100072, China
2 Academy of Military Sciences, Beijing, 100091, China
3 Army Armored Force Academy, Drill Training Center, Beijing, 100072, China

* Corresponding Author: Hua Li. Email:

Journal of Information Hiding and Privacy Protection 2022, 4(2), 61-68. https://doi.org/10.32604/jihpp.2022.027692

Received 24 January 2022; Accepted 06 March 2023; Issue published 17 April 2023

Abstract

In this paper, a video compressed sensing reconstruction algorithm based on multidimensional reference frames is proposed using the sparse characteristics of video signals in different sparse representation domains. First, the overall structure of the proposed video compressed sensing algorithm is introduced in this paper. The paper adopts a multi-reference frame bidirectional prediction hypothesis optimization algorithm. Then, the paper proposes a reconstruction method for CS frames at the re-decoding end. In addition to using key frames of each GOP reconstructed in the time domain as reference frames for reconstructing CS frames, half-pixel reference frames and scaled reference frames in the pixel domain are also used as CS frames. Reference frames of CS frames are used to obtain higher quality assumptions. The method of obtaining reference frames in the pixel domain is also discussed in detail in this paper. Finally, the reconstruction algorithm proposed in this paper is compared with video compression algorithms in the literature that have better reconstruction results. Experiments show that the algorithm has better performance than the best multi-reference frame video compression sensing algorithm and can effectively improve the quality of slow motion video reconstruction.

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Keywords

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