Over the past two decades, the surge in video streaming applications has been
fueled by the increasing accessibility of the internet and the growing demand
for network video. As users with varying internet speeds and devices seek
high-quality video, transcoding becomes essential for service providers. In
this paper, we introduce a parametric rate-distortion (R-D) transcoding model.
Our model excels at predicting transcoding distortion at various rates without
the need for encoding the video. This model serves as a versatile tool that can
be used to achieve visual quality improvement (in terms of PSNR) via
trans-sizing. Moreover, we use our model to identify visually lossless and
near-zero-slope bitrate ranges for an ingest video. Having this information
allows us to adjust the transcoding target bitrate while introducing visually
negligible quality degradations. By utilizing our model in this manner, quality
improvements up to 2 dB and bitrate savings of up to 46% of the original target
bitrate are possible. Experimental results demonstrate the efficacy of our
model in video transcoding rate distortion prediction.

通过我们提出的参数化失真率（R-D）转码模型，我们可以在不需要对视频进行编码的情况下准确预测不同速率的转码失真，并通过转尺寸实现视觉质量的改进。此外，我们还利用该模型确定了摄入视频的近似无损和接近零斜率的比特率范围，从而在引入几乎不可见的质量损失的同时调整转码目标比特率。实验结果证明了我们的模型在视频转码率失真预测方面的有效性。

视频转码的参数率失真模型

A Parametric Rate-Distortion Model for Video Transcoding

The Geometry-based Point Cloud Compression (G-PCC) has been developed by the
Moving Picture Experts Group to compress point clouds. In its lossy mode, the
reconstructed point cloud by G-PCC often suffers from noticeable distortions
due to the na\"{i}ve geometry quantization (i.e., grid downsampling). This
paper proposes a hierarchical prior-based super resolution method for point
cloud geometry compression. The content-dependent hierarchical prior is
constructed at the encoder side, which enables coarse-to-fine super resolution
of the point cloud geometry at the decoder side. A more accurate prior
generally yields improved reconstruction performance, at the cost of increased
bits required to encode this side information. With a proper balance between
prior accuracy and bit consumption, the proposed method demonstrates
substantial Bjontegaard-delta bitrate savings on the MPEG Cat1A dataset,
surpassing the octree-based and trisoup-based G-PCC v14. We provide our
implementations for reproducible research at
this https URL

该研究论文提出了一种基于几何的超分辨率方法，用于点云几何压缩，并通过构建内容相关的分层先验实现了粗粒度到细粒度的点云几何超分辨率，以在编码器和解码器之间实现更准确的先验，以减少位数消耗并获得资源占用等方面的性能改进。