Sparsity Properties of Compressive Video Sampling Generated by Coefficient Thresholding
Abstract: We study the
compressive sampling (CS) and its application in video encoding framework. The
video input is firstly transformed into suitable domain in order to achieve
sparser configuration of coefficients. Then, we apply coefficient thresholding
to classify which frames to be sampled compressively or conventionally. For
frames chosen to undergo compressive sampling, the coefficient vectors will be
projected into smaller vectors using random measurement matrix. As CS requires
two main conditions, i.e. sparsity and matrix incoherence, this research is
emphasized on the enhancement of 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.
Keywords: compressive
sampling, video coding, sparse representation, signal sparsity, motion compensation
Author: Ida Wahidah Hamzah,
Tati Latifah R. Mengko, Andriyan B. Suksmono, Hendrawan Hendrawan
Journal Code: jptkomputergg140094
