Robust Image Coding Based Upon Compressive Sensing

Deng, Chenwei; Wang, Lin; Lee, Bu-Sung; Lau, Chiew Tong

doi:10.1109/tmm.2011.2181491

Cited by 72 publications

(39 citation statements)

References 33 publications

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“…A representative work was presented by Deng et al in [5], in which the compressive measurements can be viewed as a number of descriptions mainly because of their democracy properties. We recommend the readers to see [5] for more details. Secondly, CS-based image coding is very promising method in encrypted image compression applications.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…It has been suggested in [6] that CS framework leads to an encryption scheme, where the sensing matrix can be considered as an encryption key. Therefore, the CS-based image compression methods like proposed in this paper is useful in contexts where robust coding [5] and encrypted compression [6] are very important, but the subsequent storage and communication of quantized samples is less constricted.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Let P 1 = [e 1 , e 8 , e 5 , e 4 , e 3 , e 6 , e 7 , e 2 ] T and P 2 = [e 1 , e 8 , e 3 , e 6 , e 5 , e 4 , e 7 , e 2 ] be the corresponding row permutation matrix and column permutation matrix, respectively. Then, the above permutation operations can be rewritten into the following matrix form…”

Section: A Motivated Examplementioning

confidence: 99%

“…However, it has been shown that traditional CS combined with ordinary quantization is not a good compression technique in view of compression efficiency [4]. Nonetheless, the CS-based image compression has become a hot topic in recent years, because it has been demonstrated very efficiency in some special image compression applications, such as robust image coding [5] and encrypted image compression [6].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal Permutation Based Block Compressed Sensing for Image Compression Applications

Cao

Gong

Zhang³

et al. 2018

IEICE Trans. Inf. & Syst.

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SUMMARYBlock compressed sensing (CS) with optimal permutation is a promising method to improve sampling efficiency in CS-based image compression. However, the existing optimal permutation scheme brings a large amount of extra data to encode the permutation information because it needs to know the permutation information to accomplish signal reconstruction. When the extra data is taken into consideration, the improvement in sampling efficiency of this method is limited. In order to solve this problem, a new optimal permutation strategy for block CS (BCS) is proposed. Based on the proposed permutation strategy, an improved optimal permutation based BCS method called BCS-NOP (BCS with new optimal permutation) is proposed in this paper. Simulation results show that the proposed approach reduces the amount of extra data to encode the permutation information significantly and thereby improves the sampling efficiency compared with the existing optimal permutation based BCS approach.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: A Motivated Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Permutation Based Block Compressed Sensing for Image Compression Applications

Cao

Gong

Zhang³

et al. 2018

IEICE Trans. Inf. & Syst.

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show abstract

“…He at el [11] developed a Bayesian compressed sensing method, where a spike-and-slab prior [13] and a zero-mean Gaussian model were imposed for low-band and high-bands, respectively. Chenwei Deng et al [6] proposed a method that uses different measurement matrices for low and high frequency subbands and uses different recovery schemes.  CS on other bases : Other popular bases like Fourier and DCT are also used for compressed sensing.…”

Section: Compressed Sensingmentioning

confidence: 99%

A Survey on Robust Image Coding Techniques

Pavitra¹,

Devi²,

Reddy³

2013

IJCA

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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

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Layered multiple description video coding using dual-tree discrete wavelet transform and H.264/AVC

Chen

Cai

et al. 2015

Multimed Tools Appl

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Robust Image Coding Based Upon Compressive Sensing

Cited by 72 publications

References 33 publications

Optimal Permutation Based Block Compressed Sensing for Image Compression Applications

Optimal Permutation Based Block Compressed Sensing for Image Compression Applications

A Survey on Robust Image Coding Techniques

Layered multiple description video coding using dual-tree discrete wavelet transform and H.264/AVC

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