NDLI: Nonlocally Adaptive Pattern Classification Based Compressed Sensing for Video Recovery

Content Provider	Springer Nature Link
Author	Shi, Wenxuan Chen, Xi Li, Jie
Copyright Year	2013
Abstract	Traditional video coding algorithms usually undergo several coding steps for each frame before transmission, which reduces the efficiency at the encoder. The compressed sensing (CS) as an innovative method in signal processing can make the encoder much easier than ever before, with which each frame only needs to multiply a projection matrix at the encoder, if the frame is sparse in a transform domain. Frames in a video usually exhibit sparsity in different parts on different bases; however, existing compressed sensing reconstruction methods usually recover a frame in a fixed set of bases for the entirety of the frame. Therefore, the frames cannot be recovered faithfully by the conventional CS reconstruction methods from a small number of measurements. In this paper, in order to rectify the flaw, we construct an initial estimation frame by motion estimation from neighboring frames and through the observation of the current frame. Then, nonlocally adaptive sparse signal presentation facilitation by a 2D piecewise autoregressive (AR) model is integrated into the reconstruction. The piecewise AR model is generated from the pattern classification of subimages of the initial estimation frame and its neighboring frames. An iterative procedure is proposed to recover a new estimated frame and its AR model alternatively, until the termination threshold is satisfied. The experimental results demonstrating the capabilities of the proposed method are presented.
Starting Page	241
Ending Page	256
Page Count	16
File Format	PDF
ISSN	0278081X
Journal	Circuits, Systems, and Signal Processing
Volume Number	33
Issue Number	1
e-ISSN	15315878
Language	English
Publisher	Springer US
Publisher Date	2013-07-17
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Compressed sensing Video coding AR model Pattern classification Circuits and Systems Electrical Engineering Signal, Image and Speech Processing Electronics and Microelectronics, Instrumentation
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Signal Processing

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