We present a new video compression framework (ViSTRA2) which exploits adaptation of spatial resolution and effective bit depth, down-sampling these parameters at the encoder based on perceptual criteria, and up-sampling at the decoder using a deep convolution neural network. ViSTRA2 has been integrated with the reference software of both the HEVC (HM 16.20) and VVC (VTM 4.0.1), and evaluated under the Joint Video Exploration Team Common Test Conditions using the Random Access configuration. Our results show consistent and significant compression gains against HM and VVC based on Bjønegaard Delta measurements, with average BD-rate savings of 12.6% (PSNR) and 19.5% (VMAF) over HM and 5.5% (PSNR) and 8.6% (VMAF) over VTM.

我们提出了一种新的视频压缩框架 (ViSTRA2)，它利用空间分辨率和有效位深度的适应性，根据感知标准在编码器上对这些参数进行下采样，并在解码器上使用深度卷积神经网络进行上采样。ViSTRA2 已与 HEVC (HM 16.20) 和 VVC (VTM 4.0.1) 的参考软件集成，并在联合视频探索团队通用测试条件下使用随机访问配置进行评估。我们的结果显示基于 Bjønegaard Delta 测量的 HM 和 VVC 具有一致且显着的压缩增益，与 HM 相比，平均 BD 速率节省 12.6% (PSNR) 和 19.5% (VMAF)，5.5% (PSNR) 和 8.6% (VMAF)通过 VTM。