On SSIM-bit rate comparison of HEVC encoders

Zhao, Tiesong; Wang, Zhou; Chen, Chang Wen

doi:10.1109/apsipa.2015.7415514

Cited by 2 publications

(1 citation statement)

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“…The popular video coding criteria are employed to evaluate and compare all the above four methods. These criteria include the PSNR-aware measurements (rate-PSNR curves, the BDPSNR and BDBR [1]) and the SSIM-aware measurements (rate-SSIM curves, the ADSSIM and ADBR [40]). Four Quantization parameters (Qps), 27, 32, 37 and 42, are employed in original encoder to formulate the curves.…”

Section: Methodsmentioning

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: Methodsmentioning

confidence: 99%