Symbol positions‐based Slepian–Wolf coding with application to distributed video coding

Benierbah, Said; Khamadja, Mohammed

doi:10.1049/iet-ipr.2018.5942

Cited by 3 publications

(12 citation statements)

References 37 publications

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“…4.2, we will present the RD performance results of DVCDL and compare them to those of to the DVC system of Ref. 7, the DISCOVER codec, 17 and H.264/AVC 18 …”

Section: Resultsmentioning

confidence: 99%

“…The closest SI will have fewer errors to correct (low rate) and will help to reconstruct the best possible quality. For this, many techniques have been proposed to predict a good SI, 19 , 20 but also for improving rate control, 21 channel modeling, 22 better SW coding, 7 quantization, 8 or a better reconstruction of the missing information 9 . Improving SI is the most important way to improve the RD performance because this means finding Y that has the smallest H(X/Y).…”

Section: Previous Workmentioning

confidence: 99%

“…In addition, we also have tested the DVC codec proposed in Ref. 7. This system is similar to that of Ref.…”

Section: Dl-improved Dvc Systemmentioning

confidence: 99%

“…Most of the operations are also the same as those of Refs. 7 and 17, except for SI creation. For this later, we have used a DL-based model to create the SI frames.…”

Section: Dl-improved Dvc Systemmentioning

confidence: 99%

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Improving distributed video coding with deep learning

2023

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In the current state-of-the-art distributed video coding (DVC) solutions, side information (SI) frames are created by motion-compensated interpolation. This is the same technique used by frame rate upconversion operations. Recently, these operations are accomplished using deep learning (DL), with significantly improved outcomes. DL is also used to improve the decoded images of numerous compression methods. Consequently, it is natural to wonder what are the impacts of these enhancements on DVC. In this regard, we employed DL networks to create SI and enhance the quality of the DVC decoded frames. We show how these two operations were applied to DVC and their effects on rate-distortion (RD) performance. The comparison with state-of-the-art DVC systems shows an improvement in the quality of the SI frames and that of the decoded frames, and thus an improvement of the RD performance of DVC.

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“…4.2, we will present the RD performance results of DVCDL and compare them to those of to the DVC system of Ref. 7, the DISCOVER codec, 17 and H.264/AVC 18 …”

Section: Resultsmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

“…In addition, we also have tested the DVC codec proposed in Ref. 7. This system is similar to that of Ref.…”

Section: Dl-improved Dvc Systemmentioning

confidence: 99%

“…Most of the operations are also the same as those of Refs. 7 and 17, except for SI creation. For this later, we have used a DL-based model to create the SI frames.…”

Section: Dl-improved Dvc Systemmentioning

confidence: 99%

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Improving distributed video coding with deep learning

2023

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“…In order to further improve the performance of DVC, researchers have proposed various schemes. A new technique to realize Slepian–Wolf coding was presented in [ 8 ], and it is used for DVC. The authors showed that coding the positions of the symbols, instead of their values, can be a good way to implement efficient Slepian–Wolf coding and can reduce the complexity of both the encoder and the decoder.…”

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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Side information hybrid generation based on improved motion vector field

Wang

Hong

et al. 2021

Multimed Tools Appl

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Symbol positions‐based Slepian–Wolf coding with application to distributed video coding

Cited by 3 publications

References 37 publications

Improving distributed video coding with deep learning

Improving distributed video coding with deep learning

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

Side information hybrid generation based on improved motion vector field

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