Adaptive Compressive Sensing of Images Using Spatial Entropy

Compressive Sensing (CS) realizes a low-complex image encoding architecture, which is suitable for resource-constrained wireless sensor networks. However, due to the nonstationary statistics of images, images reconstructed by the CS-based codec have many blocking artifacts and blurs. To overcome these negative effects, we propose an Adaptive Block Compressive Sensing (ABCS) system based on spatial entropy. Spatial entropy measures the amount of information, which is used to allocate measuring resources to various regions. The scheme takes spatial entropy into consideration because rich information means more edges and textures. To reduce the computational complexity of decoding, a linear mode is used to reconstruct each block by the matrix-vector product. Experimental results show that our ABCS coding system provides a better reconstruction quality from both subjective and objective points of view, and it also has a low decoding complexity.

[1]  Samuel Cheng,et al.  Compressive image sampling with side information , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[2]  Nasser Eslahi,et al.  Image/video compressive sensing recovery using joint adaptive sparsity measure , 2016, Neurocomputing.

[3]  Feihong Gu,et al.  Compressive sensing of piezoelectric sensor response signal for phased array structural health monitoring , 2017, Int. J. Sens. Networks.

[4]  Denis Kouame,et al.  New Estimators and Guidelines for Better Use of Fetal Heart Rate Estimators with Doppler Ultrasound Devices , 2014, Comput. Math. Methods Medicine.

[5]  Guangming Shi,et al.  Model-Assisted Adaptive Recovery of Compressed Sensing with Imaging Applications , 2012, IEEE Transactions on Image Processing.

[6]  Yudong Zhang,et al.  Energy Preserved Sampling for Compressed Sensing MRI , 2014, Comput. Math. Methods Medicine.

[7]  G. Marsaglia,et al.  The Ziggurat Method for Generating Random Variables , 2000 .

[8]  Junguo Zhang,et al.  Adaptive compressed sensing for wireless image sensor networks , 2017, Multimedia Tools and Applications.

[9]  James E. Fowler,et al.  Block Compressed Sensing of Images Using Directional Transforms , 2010, 2010 Data Compression Conference.

[10]  Song Li,et al.  Sparse Signals Recovery from Noisy Measurements by Orthogonal Matching Pursuit , 2011, 1105.6177.

[11]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[12]  Guangming Shi,et al.  Distributed Compressive Sensing for Cloud-Based Wireless Image Transmission , 2017, IEEE Transactions on Multimedia.

[13]  Wen Gao,et al.  Group-Based Sparse Representation for Image Restoration , 2014, IEEE Transactions on Image Processing.

[14]  James E. Fowler,et al.  DPCM for quantized block-based compressed sensing of images , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[15]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[16]  Mário A. T. Figueiredo,et al.  Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems , 2007, IEEE Journal of Selected Topics in Signal Processing.

[17]  Marco Righero,et al.  An introduction to compressive sensing , 2009 .

[18]  Wen Gao,et al.  Compressive Sampling-Based Image Coding for Resource-Deficient Visual Communication , 2016, IEEE Transactions on Image Processing.

[19]  Yudong Zhang,et al.  Exponential Wavelet Iterative Shrinkage Thresholding Algorithm for compressed sensing magnetic resonance imaging , 2015, Inf. Sci..

[20]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[21]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[22]  Ying Yu,et al.  Saliency-Based Compressive Sampling for Image Signals , 2010, IEEE Signal Processing Letters.

[23]  Lu Gan.  Block Compressed Sensing of Natural Images , 2007, 2007 15th International Conference on Digital Signal Processing.

[24]  Jian Weng,et al.  Enabling Secure and Fast Indexing for Privacy-Assured Healthcare Monitoring via Compressive Sensing , 2016, IEEE Transactions on Multimedia.