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Sparsity Properties of Compressive Video Sampling Generated by Coefficient Thresholding

Journal from gdlhub / 2016-11-15 02:21:54
Oleh : Ida Wahidah Hamzah, Tati Latifah R. Mengko, Andriyan B. Suksmono, Hendrawan Hendrawan, Telkomnika
Dibuat : 2014-12-01, dengan 1 file

Keyword : compressive sampling, video coding, sparse representation, signal sparsity, motion compensation
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/296

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.

Deskripsi Alternatif :

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.

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