Abstract
In recent years, block compressed sampling (BCS) has emerged as a considerable attractive sampling technology for image acquisition. However, the general BCS approaches ignore the information distribution in the same image sub-block, and may lead to unfair allocation of sampling resources. In this paper, we propose a novel compressed sampling scheme by employing the idea of adaptive partition. In the proposed scheme, images are adaptively partitioned based on their saliency information through clustering, and pixels with similar saliency are gathered in the same sub-blocks. Sampling rates for those blocks, in turn, are computed on the basis of their saliency values, respectively. Therefore the sampling resources are allocated with fairer and more equitable sharing by all sub-blocks. Experimental results show that the proposed scheme has better visual effect and obtains higher image reconstruction accuracy than existing ones.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Cands E, Tao T (2006) Near-optimal signal recovery from random projections: Universal encoding strategies?. IEEE Trans Inf Theory 52(12):5406–5425
Chen C, Tramel EW, Fowler JE (2011) Compressed-sensing recovery of images and video using multihypothesis predictions. In: Proceeding of IEEE asilomar conference on signals, systems and computers, ASILOMAR
Chen Z, Zhang W, Deng Bin, Xie H, Gu Xiaoyan Name-Face Association with Web Facial Image Supervision. Multimedia Systems, in press
Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306
Donoho DL, Tsaig Y, Drori I, Starck JL (2012) Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit. IEEE Trans Inf Theory 58(2):1094–1121
Duda RO, Hart PE, Stork DG (2000) Pattern classification. Wiley, New York
Fowler JE, Mun S, Tramel EW (2010) Block-based compressed sensing of images and video. Found Trends Signal Process 4(4):297–416
Fowler JE, Mun S, Tramel EW (2011) Multiscale block compressed sensing with smoothed projected landweber reconstruction. In: Proceeding of IEEE European signal processing conference
Gao X, Gu Z, Kayaalp M, Pendarakis D, Wang H (2017) ContainerLeaks: emerging security threats of information leakages in container clouds. In: Proceeding of IEEE dependable systems and networks (DSN)
Gong M, Liang Y, Shi J, Ma W, Ma J (2013) Fuzzy C-Means clustering with local information and kernel metric for image segmentation. IEEE Trans Image Process 22(2):573–584
Hou Y, Zhang Y (2014) Effective image block compressed sensing. In: Proceeding of international conference on pattern recognition (ICPR)
Hu K, Gao X, Li F (2011) Detection of suspicious lesions by adaptive thresholding based on multiresolution analysis in mammograms. IEEE Trans Instrum Meas 60(2):462–472
Hu K, Yang W, Gao X (2017) Microcalcification diagnosis in digital mammography using extreme learning machine based on hidden Markov tree model of dual-tree complex wavelet transform. Expert Systs Appl 86:135–144
Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20(11):1254–1259
Jiang Y-G, Wu Z, Wang Jun, Xue X, Chang S-F (2017) Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks. IEEE Trans Pattern Anal Mach Intell (TPAMI) 6(1):1–14. https://doi.org/10.1109/TPAMI.2017.2670560
Li Z, Tang J (2017) Weakly Supervised Deep Matrix Factorization for Social Image Understanding. IEEE Trans Image Process 26(1):276–288
Liu W, Mei T, Zhang Y (2014) Instant mobile video search with layered audio-video indexing and progressive transmission. IEEE Trans Multimed 16(8):2242–2255
Liu W, Mei T, Zhang Y, Che C, Luo J (2015) Multi-task deep visual-semantic embedding for video thumbnail selection. CVPR:3707–3715
Liu N, Yu X, Wang C, Li C, Ma L, Lei J (2017) An Energy Sharing Model with Price-based Demand Response for Microgrids of Peer-to-Peer Prosumers. IEEE Trans Power Syst 32(5):3569–3583
Lu G (2007) Block compressed sensing of natural images. Proceedings of the 15th. In: International conference on digital signal processing. wales: institute of electrical and electrical engineering computer society, pp 403–406
Mun S, Fowler JE (2009) Block compressed sensing of images using directional transforms. In: Proceedings of the international conference on image processing, Cairo, pp 3021–3024
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 20(8):888–905
Wang Z, Bovik A, Sheikh H, Simoncelli E (2004) Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612
Wang R, Jiao L, Lu F, Yang S (2013) Block-based adaptive compressed sensing of image using texture information. Acta Electron Sin 41(8):1506–1514
Wang M, Xu W, Mallada E, Tang A (2015) Sparse recovery with graph constraints. IEEE Trans Inf Theory 61(2):1028–1044
Yu Y, Wang B, Zhang L (2010) Saliency-based compressive sampling for image signals. IEEE Signal Process Lett 17(11):973–976
Zhang J, Xiang Q, Yin Y, Chen C, Luo X (2016) Adaptive compressed sensing for wireless image sensor networks. Multimed Tools Appl 76:4227–4242
Acknowledgment
This work is supported by CERNET Innovation Project of China under Grant Number NGII20160323.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhou, S., Chen, Z., Zhong, Q. et al. Block compressed sampling of image signals by saliency based adaptive partitioning. Multimed Tools Appl 78, 537–553 (2019). https://doi.org/10.1007/s11042-017-5249-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-017-5249-x