Open Access

ARTICLE

A High-Quality Adaptive Video Reconstruction Optimization Method Based on Compressed Sensing

Yanjun Zhang¹, Yongqiang He², Jingbo Zhang¹, Yaru Zhao³, Zhihua Cui^1,*, Wensheng Zhang⁴

1 School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, 030024, China
2 Department of Big Data and Intelligent Engineering, Shanxi Institute of Technology, Yangquan, 045000, China
3 School of Computer Science, Beijing University of Technology, Beijing, 100124, China
4 The Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100049, China

* Corresponding Author: Zhihua Cui. Email:

(This article belongs to this Special Issue: Bio-inspired Computer Modelling: Theories and Applications in Engineering and Sciences)

Computer Modeling in Engineering & Sciences 2023, 137(1), 363-383. https://doi.org/10.32604/cmes.2023.025832

Received 01 August 2022; Accepted 21 December 2022; Issue published 23 April 2023

Abstract

The video compression sensing method based on multi hypothesis has attracted extensive attention in the research of video codec with limited resources. However, the formation of high-quality prediction blocks in the multi hypothesis prediction stage is a challenging task. To resolve this problem, this paper constructs a novel compressed sensing-based high-quality adaptive video reconstruction optimization method. It mainly includes the optimization of prediction blocks (OPBS), the selection of search windows and the use of neighborhood information. Specifically, the OPBS consists of two parts: the selection of blocks and the optimization of prediction blocks. We combine the high-quality optimization reconstruction of foreground block with the residual reconstruction of the background block to improve the overall reconstruction effect of the video sequence. In addition, most of the existing methods based on predictive residual reconstruction ignore the impact of search windows and reference frames on performance. Therefore, Block-level search window (BSW) is constructed to cover the position of the optimal hypothesis block as much as possible. To maximize the availability of reference frames, Nearby reference frame information (NRFI) is designed to reconstruct the current block. The proposed method effectively suppresses the influence of the fluctuation of the prediction block on reconstruction and improves the reconstruction performance. Experimental results show that the proposed compressed sensing-based high-quality adaptive video reconstruction optimization method significantly improves the reconstruction performance in both objective and supervisor quality.

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Keywords

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