Distributed Compressive Video Sensing with Mixed Multihypothesis Prediction

Chao Zhou,Dengyin Zhang,Fei Ding,Can Chen

doi:10.1155/2018/7020828

Abstract

Traditional video acquisition systems require complex data compression at the encoder, which makes them unacceptable for resource-limited applications such as wireless multimedia sensor networks (WMSNs). To address this problem, distributed compressive video sensing (DCVS) represents a novel sensing approach with a simple encoder. This method shifts the computational burden from the encoder to the decoder and needs a robust reconstruction algorithm. In this paper, a mixed measurement-based multihypothesis (MH) reconstruction algorithm (mixed-MH) is proposed for DCVS to improve the reconstruction quality at low sampling rates. Considering the inaccuracy of MH prediction when measurements are insufficient, the available side information (SI) is resampled to obtain the artificial measurements, which are then integrated into real measurements via regularization. Furthermore, to avoid the negative effect of SI at high sampling rates, an adaptive regularization parameter is designed to balance the contributions of real and artificial measurements at different sampling rates. The experimental results demonstrate that the proposed mixed-MH prediction scheme outperforms other state-of-the-art algorithms in the reconstruction quality at the same low sampling rate.

Highlights

In traditional video acquisition systems, the Shannon-Nyquist sampling theorem requires a high sampling rate to obtain the sampled video signals without any loss of information
The proposed mixed-MH prediction scheme is schematically illustrated in Figure 2 and the area denoted by dotted lines highlights the innovations of this paper
The recovery performance of the mixedMH prediction model is evaluated via extensive experiments

Summary

Introduction

In traditional video acquisition systems, the Shannon-Nyquist sampling theorem requires a high sampling rate to obtain the sampled video signals without any loss of information. The reconstruction quality of the algorithms suggested in [7, 8] is improved, they are both computationally expensive compared with the original MH prediction algorithm [6] These three MH-based recovery methods were all established based on the Johnson-Lindenstrauss (JL) lemma, which enables these methods to obtain the MH prediction in the measurement domain. Chen et al [9] integrated the measurements obtained from the SI into original measurements to enhance the reconstruction quality at low sampling rates This method is influenced by the inaccuracy of the SI; only few measurements of the SI were involved in the prediction, and the original MH prediction model [6] was used to obtain a backup prediction.

Background

The Proposed DCVS Scheme with Mixed-MH Prediction

Experimental Results

Conclusion