aVCSR: Adaptive Video Compressive Sensing Using Region-of-Interest Detection in the Compressed Domain

Abstract

Existing video compressive sensing (CS) techniques with fixed sampling rates can deliver satisfactory reconstructed quality but necessitate large transmission bandwidth. To overcome this challenge, region-of-interest (ROI)-based CS algorithms have been introduced to allocate different coding resources between ROI and non-ROI segments. However, neglecting non-ROI excessively in these algorithms leads to unsatisfactory average quality for the eventual reconstruction. In this article, we integrate the ideas of these methods and propose a novel adaptive video CS approach using a low-complexity ROI detection method in the compressed domain. The ROI is detected and sampled by calculating the measurement variance between the reference frame and the subsequent frames. Conversely, the non-ROI is not transmitted but will be reconstructed by utilizing the reference frame through the corresponding position information. In addition, we present a compact method for adapting the threshold value, which allows each frame of a video to have a unique threshold rather than an artificially predetermined fixed value. Moreover, a reference-frame-updating strategy is developed to improve the versatility of the entire framework. Compared to state-of-the-art counterparts, extensive experimental results have demonstrated that our proposed methods achieve superior performance while tackling diverse scenes and using a lower sampling rate.