Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Imran, Noreen; Seet, Boon‐Chong; Fong, A.C.M.

doi:10.1186/s40064-015-1300-4

Cited by 11 publications

(4 citation statements)

References 58 publications

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“…Other video coding approaches may also be exploited for optimized cryptography. The Distributed Video Coding (DVC) employs less complex encoders, leaving most of the complexity to the decoders [57]. In other words, most of the computational and energy costs are shifted to the destination side (sink), which is usually expected to be resource-full.…”

Section: Video Cryptographymentioning

confidence: 99%

Cryptography in Wireless Multimedia Sensor Networks: A Survey and Research Directions

2017

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Wireless multimedia sensor networks will play a central role in the Internet of Things world, providing content-rich information for an uncountable number of monitoring and control scenarios. As more applications rely on multimedia data, security concerns gain attention, and new approaches arise to provide security for such networks. However, the usual resource constraints of processing, memory and the energy of multimedia-based sensors have brought different challenges for data encryption, which have driven the development of different security approaches. In this context, this article presents the state-of-the-art of cryptography in wireless multimedia sensor networks, surveying innovative works in this area and discussing promising research directions.

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Section: Video Cryptographymentioning

confidence: 99%

Cryptography in Wireless Multimedia Sensor Networks: A Survey and Research Directions

2017

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“…Given that no decoded bitplanes are used as the condition, CBP p(b k = 0|Y ) is computed in (9), which is used to compute the LLRs. The aforementioned deduction suggests that the integral intervals represented with Gray code are quite different from those with NBC, which will yield different CBPs and LLRs.…”

Section: ) Llr Computation In Para_pdwzmentioning

confidence: 99%

“…Girod et al at Stanford proposed a DVC solution characterized by framebased coding with feedback channel including pixel-domain Wyner-Ziv (PDWZ) [7] and transform-domain Wyner-Ziv (TDWZ) [8] video coding. For various reasons, the research community mostly adopted the Stanford Wyner-Ziv video coding architecture which has significantly evolved in the last years [9]. Therefore this Stanford Wyner-Ziv coding architecture is also adopted in this paper to evaluate the effect of Gray code.…”

Section: Introductionmentioning

confidence: 99%

Can Gray Code Improve the Performance of Distributed Video Coding?

et al. 2016

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Gray code application in distributed video coding (DVC) has been studied in the literature and it was claimed that DVC performance could be improved by Gray code because of stronger bitplane correlation presented. However, two factors are ignored in the literature, which renders the aforementioned claim untenable. These factors are log-likelihood ratio (LLR) computation and robustness to wrongly decoded bits, which may influence the DVC performance when different bit representations are used. This paper comprehensively evaluates the performances of Gray code in different DVC schemes, such as feedback channel-based transform domain Wyner-Ziv (FC_TDWZ) video coding, parallel pixel domain Wyner-Ziv video coding, and encoder rate control-based TDWZ (ERC_TDWZ). Experimental results suggest that the Gray code does not always improve the DVC performance, although stronger bitplane correlation is presented. In particular, the performance of FC_TDWZ with Gray code is exactly the same as that with natural bit code, because the same magnitudes of LLRs are obtained. Moreover, the reconstruction quality of the WZ frames in ERC_TDWZ is improved by Gray code not because of stronger bitplane correlation but because Gray code is more robust to wrongly decoded bits induced by rate underestimation. However, these RD gains are quite limited when the rate is controlled accurately at the encoder.INDEX TERMS Distributed video coding, Gray code, performance evaluation, log-likelihood ratios, robustness to wrongly decoded bits.

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“…This kind of encoding architecture makes the computational complexity of the encoder far higher than that of the decoder, which is more suitable for application scenarios where encoding once and decoding multiple times, such as digital TV and DVD playback and other video services. However, with the popularization and development of wireless low-energy video sensor networks, wireless video surveillance systems [ 4 ], and handheld mobile video terminal devices, users have put forward new requirements for video coding. Since most of these wireless devices are battery-powered, their energy supply and computing power are very limited, the traditional video coding framework is challenged in terms of the computational complexity of the encoder.…”

Section: Introductionmentioning

confidence: 99%

Side Information Generation Scheme Based on Coefficient Matrix Improvement Model in Transform Domain Distributed Video Coding

Wang

Chen

2020

Entropy

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In order to effectively improve the quality of side information in distributed video coding, we propose a side information generation scheme based on a coefficient matrix improvement model. The discrete cosine transform coefficient bands of the Wyner–Ziv frame at the encoder side are divided into entropy coding coefficient bands and distributed video coding coefficient bands, and then the coefficients of entropy coding coefficient bands are sampled, which are divided into sampled coefficients and unsampled coefficients. For sampled coefficients, an adaptive arithmetic encoder is used for lossless compression. For unsampled coefficients and the coefficients of distributed video coding coefficient bands, the low density parity check accumulate encoder is used to calculate the parity bits, which are stored in the buffer and transmitted in small amount upon decoder request. At the decoder side, the optical flow method is used to generate the initial side information, and the initial side information is improved according to the sampled coefficients by using the coefficient matrix improvement model. The experimental results demonstrate that the proposed side information generation scheme based on the coefficient matrix improvement model can effectively improve the quality of side information, and the quality of the generated side information is improved by about 0.2–0.4 dB, thereby improving the overall performance of the distributed video coding system.

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Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Cited by 11 publications

References 58 publications

Cryptography in Wireless Multimedia Sensor Networks: A Survey and Research Directions

Cryptography in Wireless Multimedia Sensor Networks: A Survey and Research Directions

Can Gray Code Improve the Performance of Distributed Video Coding?

Side Information Generation Scheme Based on Coefficient Matrix Improvement Model in Transform Domain Distributed Video Coding

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