| 引用本文: | 陈根龙,刘浩,周健,黄荣.视频压缩感知采样率自适应的帧间片匹配重构[J].哈尔滨工业大学学报,2022,54(5):146.DOI:10.11918/202105063 |
| CHEN Genlong,LIU Hao,ZHOU Jian,HUANG Rong.Subrate-adaptive interframe patch matching for video compressive sensing reconstruction[J].Journal of Harbin Institute of Technology,2022,54(5):146.DOI:10.11918/202105063 |
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| 摘要: |
| 相比于香农采样需要大规模样本,压缩采样在视频信号的高能效表征方面具有独特优势。当关键帧采样率与非关键帧采样率不一致时,现有的帧间片匹配算法在多种联合采样率下呈现出不稳定的重建质量。为了充分利用帧间的相关性进行视频压缩感知重构,本文提出了一种采样率自适应的帧间片匹配重构算法,根据关键帧采样率与非关键帧采样率的相对变化执行差异化的帧间片匹配,以更好地适配不同联合采样率生成的视频码流。所提算法首先对各帧的观测值分别进行帧内重构,然后按照关键帧采样率较非关键帧采样率的增长率情形进行自适应的帧间重构:若增长率较低,当前帧直接选取最近的关键帧和同向的邻近非关键帧的重构结果,执行基于同向双参考帧的片匹配重构；若增长率较高,当前帧逐步地挑选多个参考帧的重构结果,完成更为精细的多阶段片匹配重构。相较于现有重构算法,所提算法在视频压缩感知的典型架构中能够获得更为稳定的重构性能,视频序列的重建质量得到了一定程度的提升。 |
| 关键词: 视频压缩感知 采样率 帧间重构 片匹配 参考帧 |
| DOI：10.11918/202105063 |
| 分类号:TN919.8 |
| 文献标识码:A |
| 基金项目:中央高校基本科研业务费专项(2232021G-09)； 国家自然科学基金(62001099) |
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| Subrate-adaptive interframe patch matching for video compressive sensing reconstruction |
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CHEN Genlong1,LIU Hao1,2,ZHOU Jian1,HUANG Rong1,2
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(1.College of Information Science and Technology, Donghua University, Shanghai 201620, China; 2.Engineering Research Center of Digitized Textile & Apparel Technology, Ministry of Education, Donghua University, Shanghai 201620, China)
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| Abstract: |
| In contrast with the Shannon sampling which requires large-scale samples, the compressive sampling has unique advantages in energy-efficient representation of video signals. When the sampling subrates of a keyframe and a non-keyframe are inconsistent, the existing interframe patch matching algorithms show unstable recovery quality under different subrate combinations. In order to fully utilize the temporal correlation between frames, a subrate-adaptive interframe patch matching algorithm was proposed for video compressive sensing reconstruction, where the differential interframe matching mechanism was performed according to the relative change between keyframe sampling subrate and non-keyframe sampling subrate for better adaptation of video bitstream generated from different subrate combinations. Firstly, the measurements of each frame were reconstructed to obtain the corresponding intraframe results respectively. Then, the adaptive interframe reconstruction was implemented according to the growth ratio of the keyframe sampling subrate to the non-keyframe sampling subrate. In the case of low growth ratio, the current frame selected the nearest keyframe and the non-keyframe in the same direction as its co-directional double reference frames for patch matching reconstruction. In the case of high growth ratio, the current frame gradually selected the reconstruction results of multiple reference frames for multi-stage patch matching reconstruction. Compared with the existing reconstruction algorithms, the proposed algorithm could achieve more stable reconstruction performance during the typical framework of video compressive sensing, and thus the recovery quality of video sequence was consistently improved. |
| Key words: video compressive sensing sampling subrate interframe reconstruction patch matching reference frame |
