Abstract
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.
Abstract (translated)
压缩感知（CS），即在低于奈奎斯特率的情况下获取和重建信号，具有极大的潜力通过利用数据冗余来大幅度减少采样数据量，在图像和视频采集方面尤为突出。为了进一步减少采样数据同时保持视频质量，本文探索了视频CS中的时间冗余，并提出了一种基于块的自适应压缩感知框架，以及一种采样率（SR）控制策略。为了避免对非移动区域进行重复压缩，我们首先在连续帧之间加入了移动块检测功能，仅传输移动块的测量值。非移动区域则从前一帧中重建出来。此外，我们提出了一种块存储系统和动态阈值方法，根据移动区域的面积以及目标SR来实现对每帧自适应分配采样率，从而控制整体平均SR维持在目标SR水平内。最后，为了减少阻塞效应并提高重建质量，我们采用了通过参考前一帧中非移动块测量值的合作式移动和非移动区块重建方法。大量的实验表明，这项工作能够有效控制采样率，并且相比现有的研究取得了更好的性能表现。
URL
https://arxiv.org/abs/2411.10200
