87 citations · 131 across the 22 of their papers we have counts for
8 papers · 1 filter
MoTIF: Learning Motion Trajectories with Local Implicit Neural Functions for Continuous Space-Time Video Super-Resolution
Yi-Hsin Chen, Si-Cun Chen, Yen-Yu Lin +1
This work addresses continuous space-time video super-resolution (C-STVSR) that aims to up-scale an input video both spatially and temporally by any scaling factors. One key challe…
Content-Adaptive Motion Rate Adaption for Learned Video Compression
Chih-Hsuan Lin, Yi-Hsin Chen, Wen-Hsiao Peng
This paper introduces an online motion rate adaptation scheme for learned video compression, with the aim of achieving content-adaptive coding on individual test sequences to mitig…
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…
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…
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…
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…
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…
Learned Image Compression with Soft Bit-based Rate-Distortion Optimization
David Alexandre, Chih-Peng Chang, Wen-Hsiao Peng +1
This paper introduces the notion of soft bits to address the rate-distortion optimization for learning-based image compression. Recent methods for such compression train an autoenc…