Image representation by compressive sensing for visual sensor networks

Han, Bing; Wu, Feng; Wu, Dapeng

doi:10.1016/j.jvcir.2010.02.007

Cited by 38 publications

(18 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on CS, an image representation scheme for VSN is proposed in [93]. The target image is firstly divided into two components through a wavelet transform: dense and sparse components.…”

Section: Other Scheme: Compressive Sensingmentioning

confidence: 99%

A Survey of Image Compression Algorithms for Visual Sensor Networks

Mammeri

Hadjou

Khoumsi

2012

ISRN Sensor Networks

View full text Add to dashboard Cite

With the advent of visual sensor networks (VSNs), energy-aware compression algorithms have gained wide attention. That is, new strategies and mechanisms for power-efficient image compression algorithms are developed, since the application of the conventional methods is not always energy beneficial. In this paper, we provide a survey of image compression algorithms for visual sensor networks, ranging from the conventional standards such as JPEG and JPEG2000 to a new compression method, for example, compressive sensing. We provide the advantages and shortcomings of the application of these algorithms in VSN, a literature review of their application in VSN, as well as an open research issue for each compression standard/method. Moreover, factors influencing the design of compression algorithms in the context of VSN are presented. We conclude by some guidelines which concern the design of a compression method for VSN.

show abstract

“…Based on CS, an image representation scheme for VSN is proposed in [93]. The target image is firstly divided into two components through a wavelet transform: dense and sparse components.…”

Section: Other Scheme: Compressive Sensingmentioning

confidence: 99%

A Survey of Image Compression Algorithms for Visual Sensor Networks

Mammeri

Hadjou

Khoumsi

2012

ISRN Sensor Networks

View full text Add to dashboard Cite

show abstract

“…It was first proposed in [30], where CS encoding was applied to measure the fine-scale properties of the signal. A similar approach is adopted in [31], where an image is separated into dense and sparse components. The dense component is encoded by linear measurements, while the sparse component is encoded by CS.…”

Section: Review Of Compressed Sensing-based Image Coding Techniquesmentioning

confidence: 99%

Compressed Sensing-Based Distributed Image Compression

Baig

Lai

Punchihewa³

2014

Applied Sciences

View full text Add to dashboard Cite

In this paper, a new distributed block-based image compression method based on the principles of compressed sensing (CS) is introduced. The coding and decoding processes are performed entirely in the CS measurement domain. Image blocks are classified into key and non-key blocks and encoded at different rates. The encoder makes use of a new adaptive block classification scheme that is based on the mean square error of the CS measurements between blocks. At the decoder, a simple, but effective, side information generation method is used for the decoding of the non-key blocks. Experimental results show that our coding scheme achieves better results than existing CS-based image coding methods.

show abstract

“… Hybrid CS : The idea was suggested by Donoho [24] , to use different algorithms for different subbands of wavelet. Han et al [10] proposed an algorithm based on hybrid CS.  Multi Scale DWT based CS : There are a number of schemes proposed under this category where wavelet basis is primarily used for sparse representation.…”

Section: Compressed Sensingmentioning

confidence: 99%

A Survey on Robust Image Coding Techniques

Pavitra¹,

Devi²,

Reddy³

2013

IJCA

View full text Add to dashboard Cite

Packet losses decrease the quality of an image or video for multimedia applications. Robust image coding is crucial to combat packet losses, for transmission of images over non feedback networks. New CS based image coding schemes are robust against packet losses and carries CS samples of nearly equal importance. CS based coding also ensures low costs and complexity for image sensing. Hence CS based image coding techniques have some distinct advantages over traditional Forward Error Correction (FEC) techniques and Multiple

show abstract

Image representation by compressive sensing for visual sensor networks

Cited by 38 publications

References 38 publications

A Survey of Image Compression Algorithms for Visual Sensor Networks

A Survey of Image Compression Algorithms for Visual Sensor Networks

Compressed Sensing-Based Distributed Image Compression

A Survey on Robust Image Coding Techniques

Contact Info

Product

Resources

About