Iterative Reweighted Tikhonov-Regularized Multihypothesis Prediction Scheme for Distributed Compressive Video Sensing

Chen, Can; Zhou, Chao; Liu, Pengyuan; Zhang, Dengyin

doi:10.1109/tcsvt.2018.2886310

Cited by 32 publications

(19 citation statements)

References 27 publications

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“…Li et al [8] presented a multihypothesis-based residual reconstruction scheme (MR-MHRR) that generates hypothesis blocks in the residual domain and calculates the linear prediction weights in measurement-domain. Chen et al [9] presented an iterative reweighted Tikhonov-regularized scheme for MH prediction reconstruction and a Bhattacharyya coefficient-based stopping criterion to avoid over-iteration.…”

Section: Introductionmentioning

confidence: 99%

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

Xue

Hua

et al. 2020

IEEE Access

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Compared with traditional video surveillance systems, wireless video sensor systems are more suitable for emergency application scenarios, such as underground coal mine disaster rescue, due to their low power consumption and rapid deployment. Considering the limited computing power and transmission bandwidth of video sensor nodes, we propose an adaptive compression and hybrid multiple hypothesis based residual reconstruction algorithm based on normalized Bhattacharyya coefficient (NBCAC-MHRR) to solve the high efficiency video coding (HEVC) problem in underground coal mines. First, a low-complexity adaptive sampling rate allocation method is performed on the encoding side. Second, by integrating the background subtraction idea, we combine the high-quality reconstruction of the foreground with the multihypothesis residual reconstruction of the background to improve the overall reconstruction effect of the video sequence. Simulation results show that the proposed algorithm can achieve higher reconstruction quality and efficiency than previous methods, especially in underground coal mine application scenarios.

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Section: Introductionmentioning

confidence: 99%

“…References [5]- [9] mainly use the pixel domain /measurement domain ME/MC method to generate the hypothesis as accurately as possible, thereby improving the performance of residual reconstruction.…”

Section: Introductionmentioning

confidence: 99%

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

Xue

Hua

et al. 2020

IEEE Access

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“…Zhao [14] proposed a reweighted residual sparsity (RRS) model which not only takes full advantage of spatial correlation of videos to produce good initial recoveries, but also utilizes temporal correlation between frames to further enhance the reconstruction quality. To enhance the robustness of MH prediction, Chen [15] proposed a reweighted Tikhonov regularization which considers the impact of each hypothesis. Although these methods can yield competitive reconstruction quality, they are time-consuming and do not easily meet the requirements of real-time applications.…”

Section: Introductionmentioning

confidence: 99%

JsrNet: A Joint Sampling–Reconstruction Framework for Distributed Compressive Video Sensing

Chen

Zhou

et al. 2019

Sensors

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Huge video data has posed great challenges on computing power and storage space, triggering the emergence of distributed compressive video sensing (DCVS). Hardware-friendly characteristics of this technique have consolidated its position as one of the most powerful architectures in source-limited scenarios, namely, wireless video sensor networks (WVSNs). Recently, deep convolutional neural networks (DCNNs) are successfully applied in DCVS because traditional optimization-based methods are computationally elaborate and hard to meet the requirements of real-time applications. In this paper, we propose a joint sampling–reconstruction framework for DCVS, named “JsrNet”. JsrNet utilizes the whole group of frames as the reference to reconstruct each frame, regardless of key frames and non-key frames, while the existing frameworks only utilize key frames as the reference to reconstruct non-key frames. Moreover, different from the existing frameworks which only focus on exploiting complementary information between frames in joint reconstruction, JsrNet also applies this conception in joint sampling by adopting learnable convolutions to sample multiple frames jointly and simultaneously in an encoder. JsrNet fully exploits spatial–temporal correlation in both sampling and reconstruction, and achieves a competitive performance in both the quality of reconstruction and computational complexity, making it a promising candidate in source-limited, real-time scenarios.

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“…Exploiting this temporal redundancies plays a key role in video reconstruction. In the literature of compressed video sensing (CVS), multi-hypothesis (MH) prediction technique [8]- [12] took full advantage of temporal correlation among video sequences and showed a good performance in both reconstruction quality and time complexity. The core idea of MH model is to predict the current frame with a linear combination of hypotheses from reconstructed reference frames.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the MH model, Zhao et al [11] designed a reweighted residual sparsity for residual reconstruction, leading to an improved performance in reconstruction quality. In [12], the impact of each hypothesis was considered by a reweighted Tikhonov regularization, enhancing the robustness of MH prediction. Among these MH models, MH-BCS-SPL [8] presents a competitive reconstruction quality with a low time complexity, yielding the state-of-the-art recovery performance in traditional CVS methods.…”

Section: Introductionmentioning

confidence: 99%

MH-Net: A Learnable Multi-Hypothesis Network for Compressed Video Sensing

et al. 2019

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This paper establishes a novel recovery network called MH-Net-a framework for compressed video sensing (CVS) based on recently emerging deep neural networks (DNNs) techniques. MH-Net exploits temporal correlation between frames in the form of multi-hypothesis (MH) prediction, and learns a highdimensional domain which is more robust for prediction generation. After the MH prediction, a special residual network is used in MH-Net to reconstruct the residuals between the MH prediction and the desired frame from their measurements. The final reconstruction is derived by adding the reconstructed residuals to the MH prediction. Unlike the block-wise reconstruction in existing DNN-based CS architecture, MH-Net builds a mapping from block measurements to a complete frame reconstruction, leading to better reconstruction quality. Benefitting from the DNN's nature, the forward propagation of MH-Net is extremely fast, making it suitable for real-time applications. Experimental results show that MH-Net presents a better recovery performance compared with existing DNN-based recovery methods and traditional iterative recovery algorithms. INDEX TERMS Compressed sensing (CS), compressed video sensing (CVS), multi-hypothesis (MH) prediction, neural networks (NNs), signal reconstruction, video signal processing.

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Iterative Reweighted Tikhonov-Regularized Multihypothesis Prediction Scheme for Distributed Compressive Video Sensing

Cited by 32 publications

References 27 publications

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

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

JsrNet: A Joint Sampling–Reconstruction Framework for Distributed Compressive Video Sensing

MH-Net: A Learnable Multi-Hypothesis Network for Compressed Video Sensing

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