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20232026
most citedGeneralized Gaussian Model for Learned Image Compression

13 citations · 19 across the 32 of their papers we have counts for

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12 papers · 1 filter

eess.IV2026

Accelerating Merge with Motion Vector Difference via Filter Difference Analysis for VVenC

Xinmin Feng, Shengyang Xu, Jianhua Chen +3

Merge with Motion Vector Difference (MMVD) is a key coding tool in Versatile Video Coding for improving motion prediction accuracy. However, its exhaustive search strategy imposes…

eess.IV2026

Recent Advances of End-to-End Video Coding Technologies for AVS Standard Development

Xihua Sheng, Xiongzhuang Liang, Chuanbo Tang +8

Video coding standards are essential to enable the interoperability and widespread adoption of efficient video compression technologies. In pursuit of greater video compression eff…

eess.IV2025

In-Loop Filtering Using Learned Look-Up Tables for Video Coding

Zhuoyuan Li, Jiacheng Li, Yao Li +4

In-loop filtering (ILF) is a key technology in video coding standards to reduce artifacts and enhance visual quality. Recently, neural network-based ILF schemes have achieved remar…

eess.IV2025

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…

eess.IV2025

Learned Image Compression with Hierarchical Progressive Context Modeling

Yuqi Li, Haotian Zhang, Li Li +1

Context modeling is essential in learned image compression for accurately estimating the distribution of latents. While recent advanced methods have expanded context modeling capac…

eess.IV20251 cited

StableCodec: Taming One-Step Diffusion for Extreme Image Compression

Tianyu Zhang, Xin Luo, Li Li +1

Diffusion-based image compression has shown remarkable potential for achieving ultra-low bitrate coding (less than 0.05 bits per pixel) with high realism, by leveraging the generat…