计算机科学 ›› 2017, Vol. 44 ›› Issue (6): 317-321.doi: 10.11896/j.issn.1002-137X.2017.06.056
• 图形图像与模式识别 • 上一篇
赵慧民,裴真真,才争野,王晨,戴青云,魏文国
ZHAO Hui-min, PEI Zhen-zhen, CAI Zheng-ye, WANG Chen, DAI Qing-yun and WEI Wen-guo
摘要： 为了降低计算成本并节约系统功耗,信号处理最新出现的理论-分布式压缩感知(Distributed Compressed Sensing,DCS)成为视频技术的应用焦点。为此,一种基于多假设预测的视频DCS(VDCS)方案被提出。在VDCS的解码端,当前帧的预测来自于以前重构的参考帧(CS帧),而残差作为重构条件用于改善视频的重构质量。实验结果表明,提出的残差-预测VDCS方法重构视频信号的峰值信噪比(PSNR)优于MH-BCS-SPL和传统的JSM-DCS处理方法。
| [1] LLULL P,YUAN X,LIAO X J,et al.Temporal Compressive Sensing for Video[M].Compressed Sensing and its Applications,Applied and Numerical Harmonic Analysis.Springer International Publishing Switzerland,2015. [2] GAO X W,JIANG F,LIU S H,et al.Hierarchical frame based spatial-temporal recovery for video compressive sensing coding[J].Neurocomputing,2016,174(Part A):404-412. [3] ROMBERG J,et al.Imaging via compressive sampling[J].IEEE Signal Processing Magazine,2008,25(2):14-20. [4] HOU,et al.Complex SAR Image Compression Based on Direc-tional Lifting Wavelet Transform With High Clustering Capabi-lity[J].IEEE Trans.on Geoscience and Remote Sensing,2013,51(1):527-538. [5] HOU,et al.SAR image Bayesian compressive sensing exploitingthe interscale and intrascale dependencies in directional lifting wavelet transform domain[J].Neurocomputing,2014,133:358-368. [6] CHEN G,LI D F,ZHANG J S.Iterative gradient projection algorithm for two-dimensional compressed sensing sparse image reconstruction[J].Signal Processing,2014,104:15-26. [7] TAMADA D,et al.Two-dimensional compressed sensing using the cross-sampling approach for low-field MRI systems[J].IEEE Trans.on Medical Imaging,2014,33(9):1905-1912. [8] KANG B,ZHU W P,Yan J.Object detection oriented video reconstruction using compressed sensing[J].EURASIP Journal on Advances in Signal Processing,2015,2015:15. [9] PEREIRA F,TORRES L,GUILLEMO C,et al.Distributed vi-deo coding:selecting the most promising application scenarios[J].Signal Processing:Image Communication,2008,23(5):339-352. [10] FOWLER J E,MUN S,TRAMEL E W.Block-based com-pressed sensing of images and video[J].Found Trends Signal Process,2012,4(4):297-416. [11] KIM J M,LEE O K,YE J C.Noise Robust Joint Sparse Reco-very Using Compressive Subspace Fitting[EB/OL].[2012-10-12].http://arxiv.org/abs/1112.3446. [12] WARNELL G,BHATTACHARYA S,CHELLAPPA R,et al.Adaptive-Rate Compressive Sensing Using Side Information[J].IEEE Trans.on Image Processing,2015,24(11):3846-3857. [13] MUN S,FOWLER J E.Block compressed sensing of imagesusing directional transforms[C]∥Proceedings of the Internatio-nal Conference on Image Processing.Cairo,Egypt,2009:3021-3024. [14] LIU H X,SONG B,QIN H,et al.Dictionary learning based reconstruction for distributed compressed video sensing[J].Journal of Visual Communication and Image Representation,2013,24(8):1232-1242. [15] TRAMEL E W.Distance-weighted regularization forcompressed-sensing video recovery and supervised hyperspectral classification[D].Mississippi State University,2012. [16] TRAMEL E W,FOWLER J E.Video compressed sensing with multihypothesis[C]∥IEEE data compression conference.Snowbird,UT,2011:193-202. [17] GOLDSTEIN T,SETZER S.High-order methods for basis pursuit[J].UCLA CAM Report,2010:10-41. [18] KANG L W,LU C S.Distributed compressive video sensing[C]∥Proceedings of IEEE International Conference on Acoustics,Speech,and Signal Processing.2009:1169-1172. |
| No related articles found! |
|
||
首页