  This paper introduces a framework for super-resolution of scalable video
based on compressive sensing and sparse representation of residual frames in
reconnaissance and surveillance applications. We exploit efficient compressive
sampling and sparse reconstruction algorithms to super-resolve the video
sequence with respect to different compression rates. We use the sparsity of
residual information in residual frames as the key point in devising our
framework. Moreover, a controlling factor as the compressibility threshold to
control the complexity-performance trade-off is defined. Numerical experiments
confirm the efficiency of the proposed framework in terms of the compression
rate as well as the quality of reconstructed video sequence in terms of PSNR
measure. The framework leads to a more efficient compression rate and higher
video quality compared to other state-of-the-art algorithms considering
performance-complexity trade-offs.
