Abstract:Based on the nonlocal similarity and the correlation among inter-frames in video sequences,this paper proposes an algorithm of structural similarity based inter-frame group sparse representation(SSIM-InterF-GSR),which effectively improves the reconstruction performance for compressed video sensing.In SSIM-InterF-GSR,the structural similarity(SSIM)is utilized as block matching criterion to generate the group of similar blocks from the current frame and reference frames.And then,the sparsity of the groups is used as the regularization term to reconstruct the current frame.Meanwhile,the step-decreasing scheme for number of matching blocks is proposed during the iteration process of SSIM-InterF-GSR.Simulation results show that,compared to the state-of-the-art compressed video sensing reconstruction algorithm(Up-Se-AWEN-HHP),the SSIM-InterF-GSR algorithm obtains a better reconstruction quality.The most gap is up to 4~5dB.
和志杰, 杨春玲, 汤瑞东. 视频压缩感知中基于结构相似的帧间组稀疏表示重构算法研究[J]. 电子学报, 2018, 46(3): 544-553.
HE Zhi-jie, YANG Chun-ling, TANG Rui-dong. Research on Structural Similarity Based Inter-Frame Group Sparse Representation for Compressed Video Sensing. Acta Electronica Sinica, 2018, 46(3): 544-553.
[1] DONOHO D L.Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[2] 石光明,刘丹华,高大化,等.压缩感知理论及其研究进展[J].电子学报,2009,37(5):1070-1081. SHI Guang-ming,LIU Dan-hua,GAO Da-hua,et al.Advances in theory and application of compressed sensing[J].Acta Electronica Sinica,2009,37(5):1070-1081.(in Chinese)
[3] Candes E,Romberg J.l1-magic:Recovery of Sparse Signals via Convex Programming[OL].URL:www.acm.caltech.edu/l1magic/downloads/l1magic.pdf,2005-4-14.
[4] WRIGHT S J,NOWAK R D,FIGUEIREDO M A T.Sparse reconstruction by separable approximation[J].IEEE Transactions on Signal Processing,2009,57(7):2479-2493.
[5] FIGUEIREDO M A T,NOWAK R D,WRIGHT S J.Gradient projection for sparse reconstruction:Application to compressed sensing and other inverse problems[J].IEEE Journal on Selected Topics in Signal Processing,2007,1(4):586-597.
[6] TROPP J A,GILBERT A C.Signal recovery from random measurements via orthogonal matching pursuit[J].IEEE Transactions on Information Theory,2007,53(12):4655-4666.
[7] ANDRIOLE S J S J.CoSaMP:Iterative signal recovery from incomplete and inaccurate samples[J].Communications of the ACM,2010,53(12):67-79.
[8] LU GAN.Block compressed sensing of natural images[A].200715th International Conference on Digital Signal Processing[C].Cardiff,UK:IEEE,2007.403-406.
[9] MUN S,FOWLER J E.Block compressed sensing of images using directional transforms[A].I Data Compression Conference(DCC)[C].Snowbird,USA:IEEE,2009.3021-3024.
[10] SUNGKWANG M,FOWLER J E.Residual reconstruction for block-based compressed sensing of video[A].Data Compression Conference(DCC)[C].Snowbird,USA:IEEE,2011.183-192.
[11] Kuo Y,Wu K,Chen J.A scheme for distributed compressed video sensing based on hypothesis set optimization techniques[J].Multidimensional Systems and Signal Processing,2017,28(1):129-148.
[12] 杨春玲,欧伟枫.CVS中基于多参考帧的最优多假设预测算法[J].华南理工大学学报:自然科学版,2016,44(1):1-8. YANG Chun-Ling,OU Wei-Feng.Research on multireference-based optimal multihypothesis prediction in compressed video sensing[J].Journal of South China University of Technology(Natural Science Edition),2016,44(1):1-8.(in Chinese)
[13] 沈燕飞,李锦涛,朱珍民,等.基于非局部相似模型的压缩感知图像恢复算法[J].自动化学报,2015,41(2):261-272. SHEN Yan-Fei,LI Jin-Tao,ZHU Zhen-Min,et al.Image reconstruction algorithm of compressed sensing based on nonlocal similarity model[J].Acta Automatica Sinica,2015,41(2):261-272.(in Chinese)
[14] ZHANG J,ZHAO D,GAO W.Group-based sparse representation for image restoration[J].IEEE Transactions on Image Processing,2014,23(8):3336-3351.
[15] Do T T,Chen Y,Nguyen D T,et al.Distributed compressed video sensing[A].Image Processing(ICIP)[C].Cairo,Egypt:IEEE,2009.1393-1396.
[16] Cossalter M,Valenzise G,Tagliasacchi M,et al.Joint compressive video coding and analysis[J].IEEE Transactions on Multimedia,2010,12(3):168-183.
[17] Chen C,Tramel E W,Fowler J E.Compressed-sensing recovery of images and video using multihypothesis predictions[A].Signals,Systems and Computers(ASILOMAR)[C].Pacific Grove,USA:IEEE,2011.1193-1198.
[18] Gao X,Jiang F,Liu S,et al.Hierarchical frame based spatial-temporal recovery for video compressive sensing coding[J].Neurocomputing,2016,174:404-412.
[19] GOLDSTEIN T,OSHER S.The split bregman method for L1-regularized problems[J].SIAM Journal on Imaging Sciences,2009,2(2):323-343.
[20] WANG Z,BOVIK A C,SHEIKH H R,et al.Image quality assessment:From error visibility to structural similarity[J].IEEE Transactions on Image Processing,2004,13(4):600-612.
[21] TRAMEL E W,FOWLER J E.Video compressed sensing with multihypothesis[A].Data Compression Conference(DCC)[C].Snowbird,USA:IEEE,2011.193-202.
[22] LI S,QI H.A douglas-rachford splitting approach to compressed sensing image recovery using low-rank regularization[J].IEEE Transactions on Image Processing,2015,24(11):4240-4249.
[23] SüHRING K.H.264/AVC Software JM 19.0[OL].http://iphome.hhi.de/suehring/tml/,2015.