Deep Learning-Based Intra Mode Derivation for Versatile Video Coding

TL;DR

This paper introduces a deep learning-based method for intra mode derivation in Versatile Video Coding, reducing bits and improving compression efficiency by skipping intra mode signaling through a multi-class classification approach.

Contribution

It proposes a novel deep learning framework that predicts intra modes, eliminating the need for signaling and achieving significant bit rate reductions in VVC.

Findings

Achieves 2.28% bit rate reduction for Y component

Outperforms existing deep learning methods in intra mode derivation

Handles various block sizes and quantization parameters with a single model

Abstract

In intra coding, Rate Distortion Optimization (RDO) is performed to achieve the optimal intra mode from a pre-defined candidate list. The optimal intra mode is also required to be encoded and transmitted to the decoder side besides the residual signal, where lots of coding bits are consumed. To further improve the performance of intra coding in Versatile Video Coding (VVC), an intelligent intra mode derivation method is proposed in this paper, termed as Deep Learning based Intra Mode Derivation (DLIMD). In specific, the process of intra mode derivation is formulated as a multi-class classification task, which aims to skip the module of intra mode signaling for coding bits reduction. The architecture of DLIMD is developed to adapt to different quantization parameter settings and variable coding blocks including non-square ones, which are handled by one single trained model. Different from the existing deep learning based classification problems, the hand-crafted features are also fed into the intra mode derivation network besides the learned features from feature learning network. To compete with traditional method, one additional binary flag is utilized in the video codec to indicate the selected scheme with RDO. Extensive experimental results reveal that the proposed method can achieve 2.28%, 1.74%, and 2.18% bit rate reduction on average for Y, U, and V components on the platform of VVC test model, which outperforms the state-of-the-art works.