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。