Compressive sensing reconstruction of video data based on DCT and gradient-descent method

The reconstruction of video with missing data using a gradient-based compressive sensing algorithm is presented in this paper. The video is assumed to have missing data randomly spread over the frames. Also, it has been assumed that the video frames are sparse in the DCT domain. The value of the gradient is calculated in the sparsity domain and then used to update the pixel values. The efficiency of the video reconstruction using the gradient-descent algorithm is analyzed in terms of the number of available samples and sparsity level, showing better reconstruction performance comparing to the greedy algorithm. Also, it has been shown that the gradient-based algorithm provides high quality reconstruction for real video sequences.