In VP9, a 64 × 64 superblock can be recursively decomposed all the way to blocks of size 4 × 4. The encoder performs the encoding process for each possible partitioning and the optimal one is selected by minimizing the rate and distortion cost. This scheme ensures the encoding quality, but also brings in large computational complexity and substantial CPU resources. In this paper, to speed up the partition search without sacrificing the quality, we propose a multi-level machine learning-based early termination scheme. One weighted Support Vector Machine classifier is trained for each block size. The binary classifiers are used to determine that provided a block, whether it is necessary to continue the search down to smaller blocks, or to perform the early termination and take the current block size as the final one. Moreover, the classifiers are trained with varying error-tolerance for different block sizes, i.e., a stricter error-tolerance is adopted for larger block size compared with the smaller ones to control the encoder performance drop. Extensive experimental results demonstrate that for HD and 4K videos, the proposed framework accomplishes remarkable speed-up (20-25%) with less than 0.03% performance drop measured in the Bjøntegaard delta bit rate (BDBR) compared with current VP9 codebase 1 .
A multi-level SVM based early termination scheme for VP9 block partitioning was adopted in REF , which reduced encoding time by 20-25% against less than a 0.03% increase in BD-rate in the inter mode.
Multi-Level Machine Learning-based Early Termination in VP9 Partition Search
Visual Information Processing and Communication
Computer Science