Reconstruction of compressed video via non-convex minimization

Reconstruction of compressed video via non-convex minimization

This paper studies the sparsity prior to compressed video reconstruction algorithms. An effective non-convex 3DTPV regularization (0 < p < 1) is proposed for sparsity promotion. Based on the augmented Lagrangian reconstruction algorithm, this paper analyzes and compares three non-convex proxim...

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Bibliographic Details
Main Authors: Chao Ji, Jinshou Tian, Liang Sheng, Kai He, Liwei Xin, Xin Yan, Yanhua Xue, Minrui Zhang, Ping Chen, Xing Wang
Format: Article
Language:English
Published: AIP Publishing LLC 2020-11-01
Series:AIP Advances
Online Access:http://dx.doi.org/10.1063/5.0022860
Description
Summary:This paper studies the sparsity prior to compressed video reconstruction algorithms. An effective non-convex 3DTPV regularization (0 < p < 1) is proposed for sparsity promotion. Based on the augmented Lagrangian reconstruction algorithm, this paper analyzes and compares three non-convex proximity operators for the ℓp-norm function, and numerous simulation results confirmed that the 3DTPV regularization can gain higher video reconstruction quality than the existing convex regularization and is more competitive than the existing video reconstruction algorithms.
ISSN:2158-3226

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