KEYWORDS: Video, Video compression, Compressed sensing, Wavelets, Reconstruction algorithms, Sensors, 3D acquisition, Optical engineering, Signal processing, 3D video compression
We present a compressive sensing video acquisition scheme that relies on the sparsity properties of video in the spatial domain. In this scheme, the video sequence is represented by a reference frame, followed by the difference of measurement results between each pair of neighboring frames. The video signal is reconstructed by first reconstructing the frame differences using 1 minimization algorithm, then adding them sequentially to the reference frame. Simulation results on both simulated and real video sequences show that when the spatial changes between neighboring frames are small, this scheme provides better reconstruction results than existing compressive sensing video acquisition schemes, such as 2-D or 3-D wavelet methods and the minimum total-variance (TV) method. This scheme is suitable for compressive sensing acquisition of video sequences with relatively small spatial changes. A method that estimates the amount of spatial change based on the statistical properties of measurement results is also presented.
KEYWORDS: Video, Compressed sensing, Video surveillance, Video compression, Cameras, Surveillance, Imaging systems, 3D video streaming, Image restoration, Signal to noise ratio
Compressive Sensing (CS) is a recently emerged signal processing method. It shows that when a signal is sparse in a
certain basis, it can be recovered from a small number of random measurements made on it. In this work, we investigate
the possibility of utilizing CS to sample the video stream acquired by a fixed surveillance camera in order to reduce the
amount of data transmitted. For every 15 continuous video frames, we select the first frame in the video stream as the
reference frame. Then for each following frame, we compute the difference between this frame and its preceding frame,
resulting in a difference frame, which can be represented by a small number of measurement samples. By only
transmitting these samples, we greatly reduce the amount of transmitted data. The original video stream can still be
effectively recovered. In our simulations, SPGL1 method is used to recover the original frame. Two different methods,
random measurement and 2D Fourier transform, are used to make the measurements. In our simulations, the Peak
Signal-to-Noise Ratio (PSNR) ranges from 28.0dB to 50.9dB, depending on the measurement method and number of
measurement used, indicating good recovery quality. Besides a good compression rate, the CS technique has the
properties of being robust to noise and easily encrypted which all make CS technique a good candidate for signal
processing in communication.
Compressive Sensing (CS) is a recently emerged signal processing method. It shows that when a signal is sparse in a
certain basis, it can be recovered from a small number of random measurements made on it. In this work, we investigate
the possibility of utilizing CS to sample the video stream acquired by a fixed surveillance camera in order to reduce the
amount of data transmitted. For every 15 continuous video frames, we select the first frame in the video stream as the
reference frame. Then for each following frame, we compute the difference between this frame and its preceding frame,
resulting in a difference frame, which can be represented by a small number of measurement samples. By only
transmitting these samples, we greatly reduce the amount of transmitted data. The original video stream can still be
effectively recovered. In our simulations, SPGL1 method is used to recover the original frame. Two different methods,
random measurement and 2D Fourier transform, are used to make the measurements. In our simulations, the Peak
Signal-to-Noise Ratio (PSNR) ranges from 28.0dB to 50.9dB, depending on the measurement method and number of
measurement used, indicating good recovery quality. Besides a good compression rate, the CS technique has the
properties of being robust to noise and easily encrypted which all make CS technique a good candidate for signal
processing in communication.
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