分布式视频自适应块压缩感知
Distributed Video Adaptive Block Compressive Sensing
摘要：
视频块压缩感知已经被研究用于资源受限的场景，例如无线传感器网络，但是该方法仍然遭受低性能和长重构时间。受经典分布式视频编码的启发，我们设计了一个轻量级的编码器，在解码器上执行计算密集型操作，如视频帧插值。偏离最近训练端到端神经网络的趋势，我们提出了两种算法，利用卷积神经网络组件来重建视频，大大减少了重建时间。在编码器中，我们利用帧之间的时间相关性，并基于先前帧的压缩测量值部署自适应技术。在解码端，我们利用时间相关性，使用视频帧内插和时间差分脉冲编码调制。仿真结果表明，我们提出的两种算法，VAL-VFI和VAL-IDA-VFI重建更高质量的视频，达到最先进的性能。
英文摘要：
Video block compressive sensing has been studied for use in resource constrained scenarios, such as wireless sensor networks, but the approach still suffers from low performance and long reconstruction time. Inspired by classical distributed video coding, we design a lightweight encoder with computationally intensive operations, such as video frame interpolation, performed at the decoder. Straying from recent trends in training end-to-end neural networks, we propose two algorithms that leverage convolutional neural network components to reconstruct video with greatly reduced reconstruction time. At the encoder, we leverage temporal correlation between frames and deploy adaptive techniques based on compressive measurements from previous frames. At the decoder, we exploit temporal correlation by using video frame interpolation and temporal differential pulse code modulation. Simulations show that our two proposed algorithms, VAL-VFI and VAL-IDA-VFI reconstruct higher quality video, achieving state-of-the-art performance.
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