5 citations · 6 across the 4 of their papers we have counts for
5 papers
From Noise to Latent: Generating Gaussian Latents for INR-Based Image Compression
Chaoyi Lin, Yaojun Wu, Yue Li +3
Recent implicit neural representation (INR)-based image compression methods have shown competitive performance by overfitting image-specific latent codes. However, they remain infe…
An Overview of the JPEG AI Learning-Based Image Coding Standard
Semih Esenlik, Yaojun Wu, Zhaobin Zhang +5
JPEG AI is an emerging learning-based image coding standard developed by Joint Photographic Experts Group (JPEG). The scope of the JPEG AI is the creation of a practical learning-b…
EHVC: Efficient Hierarchical Reference and Quality Structure for Neural Video Coding
Junqi Liao, Yaojun Wu, Chaoyi Lin +4
Neural video codecs (NVCs), leveraging the power of end-to-end learning, have demonstrated remarkable coding efficiency improvements over traditional video codecs. Recent research…
Neural Video Compression with In-Loop Contextual Filtering and Out-of-Loop Reconstruction Enhancement
Yaojun Wu, Chaoyi Lin, Yiming Wang +4
This paper explores the application of enhancement filtering techniques in neural video compression. Specifically, we categorize these techniques into in-loop contextual filtering…
Fidelity-preserving Learning-Based Image Compression: Loss Function and Subjective Evaluation Methodology
Shima Mohammadi, Yaojun Wu, João Ascenso
Learning-based image compression methods have emerged as state-of-the-art, showcasing higher performance compared to conventional compression solutions. These data-driven approache…