基于显著性信息的压缩感知图像可分级编码
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摘要
多媒体信号在相对不稳定和带宽有限的无线网络环境下,易出现丢包等传输问题,同时码率受限,影响接收端多媒体信号感官质量。图像压缩感知作为一种结合采样和压缩的信号处理理论,具有编码简单,解码复杂的结构特性,适用于能耗受限的传感器无线网络中的多媒体信号采集与传输问题。但是,现有图像压缩感知技术尚存在压缩效率有限,恢复质量不高等问题,同样易受无线传输环境丢包影响。文中针对图像压缩感知压缩效率和无线传输环境下传输质量受限问题,通过运用图像视觉显著性信息判决技术和路径分集技术,对图像关键区域少量的可分级信息冗余增加,进行非对称的信道保护策略,来保障图像中视觉关注较高区域恢复质量。文中提出的基于显著性信息的压缩感知图像可分级编码方法,在无差错情况下,率失真性能优于传统无显著性信息方法,在丢包网络环境下,优于CS-MDC算法。
In the relatively unstable and bandwidth limited wireless network environment,multimedia signal is susceptible to transmission problems like packet loss and limited rate,affecting perceptual quality of received signal. As a signal processing theory combining sampling and compression,compressive sensing for image is suitable for multimedia signal acquisition and transmission in energy constrained wireless sensor network,due to its structure of simple encoding and complicated decoding. However,the existing image compressive sensing technology is still limited in compression efficiency and recovery quality,and also vulnerable to the packet loss in wireless transmission environment. Aiming at compressive sensing problems of compression efficiency and transmission quality in wireless environment,increasing a little scalable redundancy information in the key region of image can guarantee the recovery quality in the region with high visual intension,which is an asymmetric channel protection strategy. Saliency-based scalable compressive sensing for image is proposed and its rate distortion performance is better than that of the traditional method without considering saliency information in error-free case,and better than that of the CSMDC algorithm in packet loss case.
In the relatively unstable and bandwidth limited wireless network environment,multimedia signal is susceptible to transmission problems like packet loss and limited rate,affecting perceptual quality of received signal. As a signal processing theory combining sampling and compression,compressive sensing for image is suitable for multimedia signal acquisition and transmission in energy constrained wireless sensor network,due to its structure of simple encoding and complicated decoding. However,the existing image compressive sensing technology is still limited in compression efficiency and recovery quality,and also vulnerable to the packet loss in wireless transmission environment. Aiming at compressive sensing problems of compression efficiency and transmission quality in wireless environment,increasing a little scalable redundancy information in the key region of image can guarantee the recovery quality in the region with high visual intension,which is an asymmetric channel protection strategy. Saliency-based scalable compressive sensing for image is proposed and its rate distortion performance is better than that of the traditional method without considering saliency information in error-free case,and better than that of the CSMDC algorithm in packet loss case.
引文
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[6]赵春晖,刘巍.基于交织抽取与分块压缩感知策略的图像多描述编码方法[J].电子与信息学报,2011,33(2):461-465.ZHAO Chunhui,LIU Wei.Image multiple description coding method based on interleaving extraction and block compressive sensing strategy[J].Journal of Electronics&Information Technology,2011,33(2):461-465.(in Chinese)
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[12]HOU X,ZHANG L.Saliency detection:A spectral residual approach[C]∥IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2007:1-8.
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[15]YU Y,WANG B,ZHANG L.Hebbian-based neural networks for bottom-up visual attention and its applications to ship detection in SAR images[J].Neurocomputing,2011,74(11):2008-2017.
[16]BARANIUK R G.Compressive sensing[J].IEEE Signal Processing Magazine,2007,24(4):118-121.
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[23]ITU-T.Subjective video quality assessment methods for multimedia applications[S].1999.