  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.
