Constructing Per-Shot Bitrate Ladders using Visual Information Fidelity

Abstract: 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.