A Multi-Pass Coding Mode Search Framework For AV1 Encoder Optimization

Abstract

The AV1 codec recently released by the Alliance of Open Media provides nearly 30% BDrate reduction over its predecessor VP9. It substantially extends the available coding block sizes and supports a wide range of prediction modes. There are also a large variety of transform kernel types and sizes. The combination provides an extremely wide range of flexible coding options. To translate such flexibility into compression efficiency, the encoder needs to conduct an extensive search over the space of coding modes. Optimization of the encoder complexity and compression efficiency trade-off is critical to productionizing AV1. Many research efforts have been devoted to devising feature space based pruning methods ranging from decision rules based on some simple observations to more complex neural network models. A multi-pass coding mode search framework is proposed in this work to provide a structural approach to reduce the search volume. It decomposes the original high dimensional space search into cascaded stages of lower dimensional space searches. To retain a near optimal search result, the scheme departs from conventional dimension reduction approach in which one retains a single winner at each stage, and uses that winner for the next stage (dimension). Instead, this framework retains a subset of the states that are the most likely winners at each stage, which are then fed into the next stage to find the next subset of winners. The subset size at each stage is determined by the likelihood that the optimal route will be captured in the current stage. Changing this likelihood parameter tunes the encoder for speed and compression performance trade-off. This framework can integrate with most existing feature based methods at its various stages. The framework provides 60% encoding time reduction at the expense of 0.6% compression loss in libaom AV1 encoder.