Learning-Based Complexity Reduction and Scaling for HEVC Encoders

Abstract

This article proposes a fast Coding Unit (CU) partition decision for use in HEVC encoders based on Decision Tree classifiers. The trees are employed in a modified low-complexity encoder that implements a fast CU partition decision algorithm. Using the proposed method, an average complexity reduction of 47.8% is achieved with a Bjontegaard Delta bitrate (BD-BR) loss of 0.24% in the Random Access coding configuration, and a 42.8% complexity reduction with a 0.19% BD-BR loss in the Low Delay B configuration. A decision threshold analysis is also presented to assess the rate-distortion-complexity trade-off of the proposed method at different complexity points, varying the complexity reduction from 28% (with a 0.04% loss in BD-BR) up to 60% (with a 3.6% BD-BR loss) using the Random Access configuration. A comparison with related works shows that the proposed method outperforms competing solutions in terms of both rate-distortion efficiency and complexity reduction.