Neural Video Compression with Temporal Layer-Adaptive Hierarchical B-frame Coding

TL;DR

This paper introduces a novel neural video compression model that employs hierarchical B-frame coding with temporal layer-adaptive optimization, significantly improving coding efficiency especially for sequences with complex motions.

Contribution

It extends unidirectional NVC models to bidirectional hierarchical B-frame coding with adaptive optimization, achieving substantial BD-rate gains and better handling of complex motion sequences.

Findings

Achieves -39.86% BD-rate gain over baseline.

Handles complex and large motion sequences with up to -49.13% BD-rate improvement.

Enhances overall reconstruction quality by allocating more bits to lower temporal layers.

Abstract

Neural video compression (NVC) is a rapidly evolving video coding research area, with some models achieving superior coding efficiency compared to the latest video coding standard Versatile Video Coding (VVC). In conventional video coding standards, the hierarchical B-frame coding, which utilizes a bidirectional prediction structure for higher compression, had been well-studied and exploited. In NVC, however, limited research has investigated the hierarchical B scheme. In this paper, we propose an NVC model exploiting hierarchical B-frame coding with temporal layer-adaptive optimization. We first extend an existing unidirectional NVC model to a bidirectional model, which achieves -21.13% BD-rate gain over the unidirectional baseline model. However, this model faces challenges when applied to sequences with complex or large motions, leading to performance degradation. To address this, we introduce temporal layer-adaptive optimization, incorporating methods such as temporal layer-adaptive quality scaling (TAQS) and temporal layer-adaptive latent scaling (TALS). The final model with the proposed methods achieves an impressive BD-rate gain of -39.86% against the baseline. It also resolves the challenges in sequences with large or complex motions with up to -49.13% more BD-rate gains than the simple bidirectional extension. This improvement is attributed to the allocation of more bits to lower temporal layers, thereby enhancing overall reconstruction quality with smaller bits. Since our method has little dependency on a specific NVC model architecture, it can serve as a general tool for extending unidirectional NVC models to the ones with hierarchical B-frame coding.