  Adaptive video streaming allows for the construction of bitrate ladders that
deliver perceptually optimized visual quality to viewers under bandwidth
constraints. Two common approaches to adaptation are per-title encoding and
per-shot encoding. The former involves encoding each program, movie, or other
content in a manner that is perceptually- and bandwidth-optimized for that
content but is otherwise fixed. The latter is a more granular approach that
optimizes the encoding parameters for each scene or shot (however defined) of a
video content. Per-shot video encoding, as pioneered by Netflix, encodes on a
per-shot basis using the Dynamic Optimizer (DO). Under the control of the VMAF
perceptual video quality prediction engine, the DO delivers high-quality videos
to millions of viewers at considerably reduced bitrates than per-title or fixed
bitrate ladder encoding. A variety of per-title and per-shot encoding
techniques have been recently proposed that seek to reduce computational
overhead and to construct optimal bitrate ladders more efficiently using
low-level features extracted from source videos. Here we develop a perceptually
optimized method of constructing optimal per-shot bitrate and quality ladders,
using an ensemble of low-level features and Visual Information Fidelity (VIF)
features extracted from different scales and subbands. We compare the
performance of our model, which we call VIF-ladder, against other
content-adaptive bitrate ladder prediction methods, counterparts of them that
we designed to construct quality ladders, a fixed bitrate ladder, and bitrate
ladders constructed via exhaustive encoding using Bjontegaard delta metrics.
