  Unlike video coding for professional content, the delivery pipeline of User
Generated Content (UGC) involves transcoding where unpristine reference content
needs to be compressed repeatedly. In this work, we observe that existing
full-/no-reference quality metrics fail to accurately predict the perceptual
quality difference between transcoded UGC content and the corresponding
unpristine references. Therefore, they are unsuited for guiding the
rate-distortion optimisation process in the transcoding process. In this
context, we propose a bespoke full-reference deep video quality metric for UGC
transcoding. The proposed method features a transcoding-specific weakly
supervised training strategy employing a quality ranking-based Siamese
structure. The proposed method is evaluated on the YouTube-UGC VP9 subset and
the LIVE-Wild database, demonstrating state-of-the-art performance compared to
existing VQA methods.
