most citedAction-Constrained Reinforcement Learning for Frame-Level Bit Allocation in HEVC/H.265 through Frank-Wolfe Policy Optimization

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eess.IV2022

Learned Video Compression for YUV 4:2:0 Content Using Flow-based Conditional Inter-frame Coding

Yung-Han Ho, Chih-Hsuan Lin, Peng-Yu Chen +4

This paper proposes a learning-based video compression framework for variable-rate coding on YUV 4:2:0 content. Most existing learning-based video compression models adopt the trad…

eess.IV2022

Neural Frank-Wolfe Policy Optimization for Region-of-Interest Intra-Frame Coding with HEVC/H.265

Yung-Han Ho, Chia-Hao Kao, Wen-Hsiao Peng +1

This paper presents a reinforcement learning (RL) framework that utilizes Frank-Wolfe policy optimization to solve Coding-Tree-Unit (CTU) bit allocation for Region-of-Interest (ROI…

eess.IV2022

B-CANF: Adaptive B-frame Coding with Conditional Augmented Normalizing Flows

Mu-Jung Chen, Yi-Hsin Chen, Wen-Hsiao Peng

Over the past few years, learning-based video compression has become an active research area. However, most works focus on P-frame coding. Learned B-frame coding is under-explored…

eess.IV2022★ 1 cited

Action-Constrained Reinforcement Learning for Frame-Level Bit Allocation in HEVC/H.265 through Frank-Wolfe Policy Optimization

Yung-Han Ho, Yun Liang, Chia-Hao Kao +1

This paper presents a reinforcement learning (RL) framework that leverages Frank-Wolfe policy optimization to address frame-level bit allocation for HEVC/H.265. Most previous RL-ba…

eess.IV2021★ 1 cited

ANFIC: Image Compression Using Augmented Normalizing Flows

Yung-Han Ho, Chih-Chun Chan, Wen-Hsiao Peng +2

This paper introduces an end-to-end learned image compression system, termed ANFIC, based on Augmented Normalizing Flows (ANF). ANF is a new type of flow model, which stacks multip…