High-Performance Distributed Compressive Video Sensing: Jointly Exploiting the HEVC Motion Estimation and the ℓ<sub>1</sub> – ℓ<sub>1</sub> Reconstruction
The distributed compressive video sensing (DCVS) system combines the advantages of compressed sensing (CS) and distributed video coding (DVC), suitable for the limited-resource video sensing and transmission environment. In this paper, we propose a comprehensive high performance DCVS system. First,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8995521/ |
| _version_ | 1818428137752494080 |
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| author | Ruifeng Zhang Shaohua Wu Ye Wang Jian Jiao |
| author_facet | Ruifeng Zhang Shaohua Wu Ye Wang Jian Jiao |
| author_sort | Ruifeng Zhang |
| collection | DOAJ |
| description | The distributed compressive video sensing (DCVS) system combines the advantages of compressed sensing (CS) and distributed video coding (DVC), suitable for the limited-resource video sensing and transmission environment. In this paper, we propose a comprehensive high performance DCVS system. First, we introduce the BM3D-AMP algorithm reconstruct key (K) frames. Second, we propose a new high efficiency video coding (HEVC) motion estimation (ME) algorithm with motion vector (MV) prediction method. By integrating the segmentation idea and motion estimation, this algorithm can gets more accurate side information (SI). Finally, we propose the ℓ<sub>1</sub> - ℓ<sub>1</sub> minimization model to achieve non-key (NK) frames joint high-quality reconstruction. We utilize the alternating direction method of multipliers (ADMM) algorithm to solve it. With the idea of dividing and conquering, the general problem is decomposed into several smaller pieces. Experimental results demonstrate that the proposed system has significant improvement over its counterparts. |
| first_indexed | 2024-12-14T14:56:51Z |
| format | Article |
| id | doaj.art-ee568d44ec0641ae9e3209abe4f6caf5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T14:56:51Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ee568d44ec0641ae9e3209abe4f6caf52022-12-21T22:56:58ZengIEEEIEEE Access2169-35362020-01-018313063131610.1109/ACCESS.2020.29733928995521High-Performance Distributed Compressive Video Sensing: Jointly Exploiting the HEVC Motion Estimation and the ℓ<sub>1</sub> – ℓ<sub>1</sub> ReconstructionRuifeng Zhang0https://orcid.org/0000-0003-0733-8293Shaohua Wu1https://orcid.org/0000-0002-6950-0594Ye Wang2https://orcid.org/0000-0002-0464-9136Jian Jiao3https://orcid.org/0000-0002-7988-9820Harbin Institute of Technology at Shenzhen, Shenzhen, ChinaHarbin Institute of Technology at Shenzhen, Shenzhen, ChinaHarbin Institute of Technology at Shenzhen, Shenzhen, ChinaHarbin Institute of Technology at Shenzhen, Shenzhen, ChinaThe distributed compressive video sensing (DCVS) system combines the advantages of compressed sensing (CS) and distributed video coding (DVC), suitable for the limited-resource video sensing and transmission environment. In this paper, we propose a comprehensive high performance DCVS system. First, we introduce the BM3D-AMP algorithm reconstruct key (K) frames. Second, we propose a new high efficiency video coding (HEVC) motion estimation (ME) algorithm with motion vector (MV) prediction method. By integrating the segmentation idea and motion estimation, this algorithm can gets more accurate side information (SI). Finally, we propose the ℓ<sub>1</sub> - ℓ<sub>1</sub> minimization model to achieve non-key (NK) frames joint high-quality reconstruction. We utilize the alternating direction method of multipliers (ADMM) algorithm to solve it. With the idea of dividing and conquering, the general problem is decomposed into several smaller pieces. Experimental results demonstrate that the proposed system has significant improvement over its counterparts.https://ieeexplore.ieee.org/document/8995521/Distributed compressive video sensingside informationHEVC motion estimationℓ₁–ℓ₁ minimization |
| spellingShingle | Ruifeng Zhang Shaohua Wu Ye Wang Jian Jiao High-Performance Distributed Compressive Video Sensing: Jointly Exploiting the HEVC Motion Estimation and the ℓ<sub>1</sub> – ℓ<sub>1</sub> Reconstruction IEEE Access Distributed compressive video sensing side information HEVC motion estimation ℓ₁–ℓ₁ minimization |
| title | High-Performance Distributed Compressive Video Sensing: Jointly Exploiting the HEVC Motion Estimation and the ℓ<sub>1</sub> – ℓ<sub>1</sub> Reconstruction |
| title_full | High-Performance Distributed Compressive Video Sensing: Jointly Exploiting the HEVC Motion Estimation and the ℓ<sub>1</sub> – ℓ<sub>1</sub> Reconstruction |
| title_fullStr | High-Performance Distributed Compressive Video Sensing: Jointly Exploiting the HEVC Motion Estimation and the ℓ<sub>1</sub> – ℓ<sub>1</sub> Reconstruction |
| title_full_unstemmed | High-Performance Distributed Compressive Video Sensing: Jointly Exploiting the HEVC Motion Estimation and the ℓ<sub>1</sub> – ℓ<sub>1</sub> Reconstruction |
| title_short | High-Performance Distributed Compressive Video Sensing: Jointly Exploiting the HEVC Motion Estimation and the ℓ<sub>1</sub> – ℓ<sub>1</sub> Reconstruction |
| title_sort | high performance distributed compressive video sensing jointly exploiting the hevc motion estimation and the x2113 sub 1 sub x2013 x2113 sub 1 sub reconstruction |
| topic | Distributed compressive video sensing side information HEVC motion estimation ℓ₁–ℓ₁ minimization |
| url | https://ieeexplore.ieee.org/document/8995521/ |
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