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Side Information Generation Scheme Based on Coefficient Matrix Improvement Model in Transform Domain Distributed Video Coding.
Wang, Wei; Chen, Jianhua.
Afiliação
  • Wang W; School of Information Science and Engineering, Yunnan University, Kunming 650000, China.
  • Chen J; School of Information Science and Engineering, Yunnan University, Kunming 650000, China.
Entropy (Basel) ; 22(12)2020 Dec 17.
Article em En | MEDLINE | ID: mdl-33348883
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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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article