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Compressive Sampling-Based Image Coding for Resource-Deficient Visual Communication
Liu X.4; Zhai D.4; Zhou J.3; Zhang X.1; Zhao D.4; Gao W.2
2016-06-01
Source PublicationIEEE Transactions on Image Processing
ISSN10577149
Volume25Issue:6Pages:2844-2855
Abstract

In this paper, a new compressive sampling-based image coding scheme is developed to achieve competitive coding efficiency at lower encoder computational complexity, while supporting error resilience. This technique is particularly suitable for visual communication with resource-deficient devices. At the encoder, compact image representation is produced, which is a polyphase down-sampled version of the input image; but the conventional low-pass filter prior to down-sampling is replaced by a local random binary convolution kernel. The pixels of the resulting down-sampled pre-filtered image are local random measurements and placed in the original spatial configuration. The advantages of the local random measurements are two folds: 1) preserve high-frequency image features that are otherwise discarded by low-pass filtering and 2) remain a conventional image and can therefore be coded by any standardized codec to remove the statistical redundancy of larger scales. Moreover, measurements generated by different kernels can be considered as the multiple descriptions of the original image and therefore the proposed scheme has the advantage of multiple description coding. At the decoder, a unified sparsity-based soft-decoding technique is developed to recover the original image from received measurements in a framework of compressive sensing. Experimental results demonstrate that the proposed scheme is competitive compared with existing methods, with a unique strength of recovering fine details and sharp edges at low bit-rates.

KeywordCompressive Sensing Local Random Sampling Low Bit-rates Image Coding Multiple Description Coding
DOI10.1109/TIP.2016.2554320
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000375303000008
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Cited Times [WOS]:13   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Nanyang Technological University
2.Peking University
3.Universidade de Macau
4.Harbin Institute of Technology
Recommended Citation
GB/T 7714
Liu X.,Zhai D.,Zhou J.,et al. Compressive Sampling-Based Image Coding for Resource-Deficient Visual Communication[J]. IEEE Transactions on Image Processing,2016,25(6):2844-2855.
APA Liu X.,Zhai D.,Zhou J.,Zhang X.,Zhao D.,&Gao W..(2016).Compressive Sampling-Based Image Coding for Resource-Deficient Visual Communication.IEEE Transactions on Image Processing,25(6),2844-2855.
MLA Liu X.,et al."Compressive Sampling-Based Image Coding for Resource-Deficient Visual Communication".IEEE Transactions on Image Processing 25.6(2016):2844-2855.
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