Sparsity properties of compressive video sampling generated by coefficient thresholding

Abstract

We study the compressive sampling (CS) and its application in a video encoding framework. The video input is firstly transformed into a suitable domain in order to achieve sparser configuration of coefficients. Then, we apply coefficient thresholding to classify which frames are to be sampled compressively or conventionally. For frames chosen to undergo compressive sampling, the coefficient vectors will be projected into smaller vectors using a random measurement matrix. As CS requires two main conditions, i.e. sparsity and matrix incoherence, this research is focused on the enhancement of the sparsity property of the input signal. It was empirically proven that the sparsity enhancement could be reached by applying motion compensation and thresholding to the non-significant coefficient count. At the decoder side, the reconstruction algorithm can employ basis pursuit or L1 minimization algorithm.