  Providing high-quality video with efficient bitrate is a main challenge in
video industry. The traditional one-size-fits-all scheme for bitrate ladders is
inefficient and reaching the best content-aware decision computationally
impractical due to extensive encodings required. To mitigate this, we propose a
bitrate and complexity efficient bitrate ladder prediction method using
transfer learning and spatio-temporal features. We propose: (1) using feature
maps from well-known pre-trained DNNs to predict rate-quality behavior with
limited training data; and (2) improving highest quality rung efficiency by
predicting minimum bitrate for top quality and using it for the top rung. The
method tested on 102 video scenes demonstrates 94.1% reduction in complexity
versus brute-force at 1.71% BD-Rate expense. Additionally, transfer learning
was thoroughly studied through four networks and ablation studies.
