Distributed Compressed Video Sensing with Joint Optimization of Dictionary Learning and <i>l</i>₁-Analysis Based Reconstruction

Abstract

Distributed compressed video sensing (DCVS), combining advantages of compressed sensing and distributed video coding, is developed as a novel and powerful system to get an encoder with low complexity. Nevertheless, it is still unclear how to explore the method to achieve an effective video recovery through utilizing realistic signal characteristics as much as possible. Based on this, we present a novel spatiotemporal dictionary learning (DL) based reconstruction method for DCVS, where both the DL model and the l 1-analysis based recovery with correlation constraints are included in the minimization problem to achieve the joint optimization of sparse representation and signal reconstruction. Besides, an alternating direction method with multipliers (ADMM) based numerical algorithm is outlined for solving the underlying optimization problem. Simulation results demonstrate that the proposed method outperforms other methods, with 0.03-4.14 dB increases in PSNR and a 0.13-15.31 dB gain for non-key frames.

Survey on compressed sensing reconstruction method for3Ddata

“…Moreover, there are some jointly optimized methods applied to video reconstruction. Tian et al 29 proposed a restore method with spatiotemporal DL. DL was incorporated into iterative optimization as a single step to achieve joint optimization of sparse representation and signal restore.…”

An Adaptive Distributed Compressed Video Sensing Algorithm Based on Normalized Bhattacharyya Coefficient for Coal Mine Monitoring Video

“…In general application scenarios, reconstruction quality is widely concerned as an important performance indicator. Fang et al [13] presented a dictionary learning based reconstruction method to achieve the joint optimization of sparse representation and signal reconstruction. Tsugumi et al [14] used 1 -norm error instead of 2 -norm to increase the robustness against outliers, and then applied an alternating direction method of multipliers to minimization the cost function.…”