Fast inter-frame prediction in multi-view video coding based on perceptual distortion threshold model

Jiang, Gangyi; Du, Baozhen; Fang, Shuqing; Yu, Mei; Shao, Feng; Peng, Zongju; Chen, Fen

doi:10.1016/j.image.2018.10.002

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…Unlike the traditional RD model, Li et al [18] proposed a multi-view bit allocation method based on the exact target bit relationship between base view and dependent view. To reduce coding complexity, Jiang et al [13] put forward inter-frame prediction method that reveals the relationship between mode selection and coding distortion threshold by a perceptual distortion threshold model. Li et al [19] proposed an RD optimization for the dependent viewpoint based on inter-viewpoint dependencies, and greatly improved its performance in MVC.…”

Section: Multi-view Video Codingmentioning

confidence: 99%

Multi-View Video Coding with GAN Latent Learning

Lan¹,

Luo²,

Yan³

et al. 2022

Preprint

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The introduction of multiple viewpoints inevitably increases the bitrates to store and transmit video scenes. To reduce the compressed bitrates, researchers have developed to skip intermediate viewpoints during compression and delivery, and finally reconstruct them with Side Information (SI). Generally, the depth maps can be utilized to construct SI; however, it shows inferior performance with inaccurate reconstruction or high bitrates. In this paper, we propose a multi-view video coding based on SI of Generative Adversarial Network (GAN). At the encoder, we construct a spatio-temporal Epipolar Plane Image (EPI) and further utilize convolutional network to extract the latent code of GAN as SI; while at the decoder side, we combine the SI and adjacent viewpoints to reconstruct intermediate views by the generator of GAN. In particular, we set a joint encoder constraint of reconstruction cost and SI entropy, in order to achieve an optimal tradeoff between reconstruction quality and bitrate overhead. Experiments show a significantly improved Rate-Distortion (RD) performance compared with the state-of-the-art methods.

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Section: Multi-view Video Codingmentioning

confidence: 99%