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eess.IV2025

A Cross-Framework Study of Temporal Information Buffering Strategies for Learned Video Compression

Kuan-Wei Ho, Yi-Hsin Chen, Martin Benjak +2

Recent advances in learned video codecs have demonstrated remarkable compression efficiency. Two fundamental design aspects are critical: the choice of inter-frame coding framework…

eess.IV2025

MH-LVC: Multi-Hypothesis Temporal Prediction for Learned Conditional Residual Video Coding

Huu-Tai Phung, Zong-Lin Gao, Yi-Chen Yao +5

This work, termed MH-LVC, presents a multi-hypothesis temporal prediction scheme that employs long- and short-term reference frames in a conditional residual video coding framework…

eess.IV2025

HyTIP: Hybrid Temporal Information Propagation for Masked Conditional Residual Video Coding

Yi-Hsin Chen, Yi-Chen Yao, Kuan-Wei Ho +5

Most frame-based learned video codecs can be interpreted as recurrent neural networks (RNNs) propagating reference information along the temporal dimension. This work revisits the…

eess.IV2025

Conditional Residual Coding with Explicit-Implicit Temporal Buffering for Learned Video Compression

Yi-Hsin Chen, Kuan-Wei Ho, Martin Benjak +2

This work proposes a hybrid, explicit-implicit temporal buffering scheme for conditional residual video coding. Recent conditional coding methods propagate implicit temporal inform…

eess.IV2024

On the Rate-Distortion-Complexity Trade-offs of Neural Video Coding

Yi-Hsin Chen, Kuan-Wei Ho, Martin Benjak +2

This paper aims to delve into the rate-distortion-complexity trade-offs of modern neural video coding. Recent years have witnessed much research effort being focused on exploring t…

eess.IV2024

Transformer-based Learned Image Compression for Joint Decoding and Denoising

Yi-Hsin Chen, Kuan-Wei Ho, Shiau-Rung Tsai +4

This work introduces a Transformer-based image compression system. It has the flexibility to switch between the standard image reconstruction and the denoising reconstruction from…