To further compress measurements, the output of block-based compressed sensing, this work presents a measurement coding framework using measurement-domain intra prediction. In the framework, a deterministic measurement matrix based on the correlation of adjacent pixels (APMM) is proposed to embed the pixel-domain boundary information of each block to the measurement domain. By adopting APMM, the pixeldomain information can be efficiently used for measurementdomain intra prediction. To avoid the interference of pixels that are far apart and achieve a high prediction accuracy, we employ boundary measurements of neighboring blocks as reference for prediction. Finally, the residuals between measurements and predictions are processed by quantization and Huffman coding to generate a coded bit sequence for transmitting. Compared to the state-of-the-art, this work achieves a 24% decrease in bitrate and a 1.68dB increase in PSNR on average.

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Most deep network methods for compressive sensing reconstruction suffer from the black-box characteristic of DNN. In this paper, a deep neural network with interpretable motion estimation named CSMCNet is proposed. The network is able to realize high-quality reconstruction of video compressive sensing by unfolding the iterative steps of optimization based algorithms. A DNN based, multi-hypothesis motion estimation module is designed to improve the reconstruction quality, and a residual module is employed to further narrow down the gap between reconstruction results and original signal in our proposed method. Besides, we propose an interpolation module with corresponding training strategy to realize scalable CS reconstruction, which is capable of using the same model to decode various compression ratios. Experiments show that a PSNR of 29.34dB can be achieved at 2% CS ratio (compressed by 98%), which is superior than other state-of-the-art methods. Moreover, the interpolation module is proved to be effective, with significant cost saving and acceptable performance losses.

In this paper, a deep neural network with interpretable motion compensation called CS-MCNet is proposed to realize high-quality and real-time decoding of video compressive sensing. Firstly, explicit multi-hypothesis motion compensation is applied in our network to extract correlation information of adjacent frames(as shown in Fig. 1), which improves the recover performance. And then, a residual module further narrows down the gap between reconstruction result and original signal. The overall architecture is interpretable by using algorithm unrolling, which brings the benefits of being able to transfer prior knowledge about the conventional algorithms. As a result, a PSNR of 22dB can be achieved at 64x compression ratio, which is about 4% to 9% better than state-of-the-art methods. In addition, due to the feed-forward architecture, the reconstruction can be processed by our network in real time and up to three orders of magnitude faster than traditional iterative methods.