1 citations · 2 across the 17 of their papers we have counts for
18 papers
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…
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…
Application Space and the Rate-Distortion-Complexity Analysis of Neural Video CODECs
Ricardo L. de Queiroz, Diogo C. Garcia, Yi-Hsin Chen +3
We study the decision-making process for choosing video compression systems through a rate-distortion-complexity (RDC) analysis. We discuss the 2D Bjontegaard delta (BD) metric and…
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…
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…
CAT-3DGS: A Context-Adaptive Triplane Approach to Rate-Distortion-Optimized 3DGS Compression
Yu-Ting Zhan, Cheng-Yuan Ho, Hebi Yang +4
3D Gaussian Splatting (3DGS) has recently emerged as a promising 3D representation. Much research has been focused on reducing its storage requirements and memory footprint. Howeve…
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…
Bridging Compressed Image Latents and Multimodal Large Language Models
Chia-Hao Kao, Cheng Chien, Yu-Jen Tseng +5
This paper presents the first-ever study of adapting compressed image latents to suit the needs of downstream vision tasks that adopt Multimodal Large Language Models (MLLMs). MLLM…
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…
LiDAR Depth Map Guided Image Compression Model
Alessandro Gnutti, Stefano Della Fiore, Mattia Savardi +3
The incorporation of LiDAR technology into some high-end smartphones has unlocked numerous possibilities across various applications, including photography, image restoration, augm…
MaskCRT: Masked Conditional Residual Transformer for Learned Video Compression
Yi-Hsin Chen, Hong-Sheng Xie, Cheng-Wei Chen +4
Conditional coding has lately emerged as the mainstream approach to learned video compression. However, a recent study shows that it may perform worse than residual coding when the…
Transformer-based Image Compression with Variable Image Quality Objectives
Chia-Hao Kao, Yi-Hsin Chen, Cheng Chien +2
This paper presents a Transformer-based image compression system that allows for a variable image quality objective according to the user's preference. Optimizing a learned codec f…
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…
TransTIC: Transferring Transformer-based Image Compression from Human Perception to Machine Perception
Yi-Hsin Chen, Ying-Chieh Weng, Chia-Hao Kao +3
This work aims for transferring a Transformer-based image compression codec from human perception to machine perception without fine-tuning the codec. We propose a transferable Tra…
Transformer-based Variable-rate Image Compression with Region-of-interest Control
Chia-Hao Kao, Ying-Chieh Weng, Yi-Hsin Chen +2
This paper proposes a transformer-based learned image compression system. It is capable of achieving variable-rate compression with a single model while supporting the region-of-in…
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…
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…
Video Rescaling Networks with Joint Optimization Strategies for Downscaling and Upscaling
Yan-Cheng Huang, Yi-Hsin Chen, Cheng-You Lu +3
This paper addresses the video rescaling task, which arises from the needs of adapting the video spatial resolution to suit individual viewing devices. We aim to jointly optimize v…