Decision Trees for Complexity Reduction in Video Compression

TL;DR

This paper introduces decision tree classifiers to reduce complexity in HEVC video encoding by skipping unnecessary coding unit tests, significantly decreasing encoding time with minimal quality loss.

Contribution

It presents a novel manual pruning method for decision trees and demonstrates a 42.1% reduction in encoding time with minimal bitrate increase.

Findings

42.1% average encoding time reduction

0.7% bitrate increase with skip criteria

Effective decision tree models for complexity reduction

Abstract

This paper proposes a method for complexity reduction in practical video encoders using multiple decision tree classifiers. The method is demonstrated for the fast implementation of the 'High Efficiency Video Coding' (HEVC) standard, chosen because of its high bit rate reduction capability but large complexity overhead. Optimal partitioning of each video frame into coding units (CUs) is the main source of complexity as a vast number of combinations are tested. The decision tree models were trained to identify when the CU testing process, a time-consuming Lagrangian optimisation, can be skipped i.e a high probability that the CU can remain whole. A novel approach to finding the simplest and most effective decision tree model called 'manual pruning' is described. Implementing the skip criteria reduced the average encoding time by 42.1% for a Bj{\o}ntegaard Delta rate detriment of 0.7%, for 17 standard test sequences in a range of resolutions and quantisation parameters.