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

In the latest years, videoconferencing has taken a fundamental role in
interpersonal relations, both for personal and business purposes. Lossy video
compression algorithms are the enabling technology for videoconferencing, as
they reduce the bandwidth required for real-time video streaming. However,
lossy video compression decreases the perceived visual quality. Thus, many
techniques for reducing compression artifacts and improving video visual
quality have been proposed in recent years. In this work, we propose a novel
GAN-based method for compression artifacts reduction in videoconferencing.
Given that, in this context, the speaker is typically in front of the camera
and remains the same for the entire duration of the transmission, we can
maintain a set of reference keyframes of the person from the higher-quality
I-frames that are transmitted within the video stream and exploit them to guide
the visual quality improvement; a novel aspect of this approach is the update
policy that maintains and updates a compact and effective set of reference
keyframes. First, we extract multi-scale features from the compressed and
reference frames. Then, our architecture combines these features in a
progressive manner according to facial landmarks. This allows the restoration
of the high-frequency details lost after the video compression. Experiments
show that the proposed approach improves visual quality and generates
photo-realistic results even with high compression rates. Code and pre-trained
networks are publicly available at
this https URL

我们提出了一种基于 GAN 的方法，用于视频会议中的压缩失真减少，通过提取多尺度特征和结合面部标志的方式，恢复视频压缩后丢失的高频细节，实验证明该方法在高压缩速率下提高了视觉质量并生成了逼真的结果。