A Framework for Super-Resolution of Scalable Video via Sparse Reconstruction of Residual Frames

TL;DR

This paper presents a super-resolution framework for scalable video that leverages compressive sensing and sparse residual reconstruction to improve video quality at various compression rates, suitable for surveillance applications.

Contribution

The novel framework combines compressive sensing with sparse residual reconstruction, introducing a compressibility threshold to balance complexity and performance in scalable video super-resolution.

Findings

Achieves higher PSNR compared to state-of-the-art methods.

Provides efficient trade-offs between compression rate and video quality.

Demonstrates effectiveness in surveillance video scenarios.

Abstract

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.