基于运动块检测的自适应压缩感知，动态调控采样率以减少冗余数据。
Block based Adaptive Compressive Sensing with Sampling Rate Control
- 通过检测视频帧间运动块，仅传输运动区域测量值，非运动区复用前帧。
- 动态调整采样率，使平均采样率稳定在目标值，且运动区域占比越大，采样率越高。
- 利用前帧非运动块测量值协同重建，有效降低块效应，提升图像质量。
压缩感知（CS）可在低于奈奎斯特率的情况下采集和重构信号，在图像与视频采集中具有潜力，可利用数据冗余大幅减少采样数据量。为在保持视频质量的前提下进一步降低采样量，本文探索视频压缩感知中的时间冗余，提出一种基于块的自适应压缩感知框架及采样率（SR）控制策略。为避免对静态区域重复压缩，首先引入相邻帧间的运动块检测机制，仅传输运动块的测量值，非运动区域则由前一帧重构。此外，设计块存储系统与动态阈值，根据运动区域面积与目标采样率，自适应分配每帧的采样率，实现平均采样率控制在目标值范围内。最后，通过参考前帧非运动块的测量值，对运动与非运动块进行协同重建，减少块效应并提升重建质量。大量实验表明，该方法可有效控制采样率，并优于现有方法。
原文摘要 · Abstract (English)
Compressive sensing (CS), acquiring and reconstructing signals below the Nyquist rate, has great potential in image and video acquisition to exploit data redundancy and greatly reduce the amount of sampled data. To further reduce the sampled data while keeping the video quality, this paper explores the temporal redundancy in video CS and proposes a block based adaptive compressive sensing framework with a sampling rate (SR) control strategy. To avoid redundant compression of non-moving regions, we first incorporate moving block detection between consecutive frames, and only transmit the measurements of moving blocks. The non-moving regions are reconstructed from the previous frame. In addition, we propose a block storage system and a dynamic threshold to achieve adaptive SR allocation to each frame based on the area of moving regions and target SR for controlling the average SR within the target SR. Finally, to reduce blocking artifacts and improve reconstruction quality, we adopt a cooperative reconstruction of the moving and non-moving blocks by referring to the measurements of the non-moving blocks from the previous frame. Extensive experiments have demonstrated that this work is able to control SR and obtain better performance than existing works.
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