A Framework for Super-Resolution of Scalable Video via Sparse Reconstruction of Residual Frames
This is an incremental improvement for video super-resolution in surveillance applications, focusing on performance-complexity trade-offs.
The paper tackles super-resolution of scalable video for reconnaissance and surveillance by proposing a framework based on compressive sensing and sparse reconstruction of residual frames, achieving more efficient compression rates and higher video quality (measured by PSNR) compared to state-of-the-art algorithms.
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.