A compressive sensing approach to object-based surveillance video coding

This paper studies the feasibility and investigates various choices in the application of compressive sensing (CS) to object-based surveillance video coding. The residual object error of a video frame is a sparse signal and CS, which aims to represent information of a sparse signal by random measurements, is considered for coding of object error. This work proposes several techniques using two approaches- direct CS and transform-based CS. The techniques are studied and analyzed by varying the different trade-off parameters such as the measurement index, quantization levels etc. Finally we recommend an optimal scheme for a range of bitrates. Experimental results with comparative bitrates-vs-PSNR graphs for the different techniques are presented.