6 papers
Can LLMs Design Video Coding Tools? A Case Study on Planar Mode
Yingwen Zhang, Meng Wang, Liqiang He +1
This paper explores whether large language models (LLMs) can design video coding tools, a highly challenging task due to the intricate algorithmic coupling of tool modifications. I…
Evo-RAD: Navigating Rare Retinal Disease Diagnosis via Self-Evolving Agentic Retrieval
Wangding Xia, Ye Du, Jiashi Lin +3
Large-scale pretrained foundation models have revolutionized general medical screening, but often falter on rare diseases because such conditions are underrepresented in real-world…
LLM-Driven Heuristic Frame-Level Quantization Parameter Adaptation for VVenC
Liqiang He, Yingwen Zhang, Riyu Lu +2
Optimal frame-level quantization parameter (QP) allocation remains a persistent challenge in modern video encoders. The fixed-QP scheme widely adopted in practical systems is inher…
Fine-Grained Motion Compression and Selective Temporal Fusion for Neural B-Frame Video Coding
Xihua Sheng, Peilin Chen, Meng Wang +3
With the remarkable progress in neural P-frame video coding, neural B-frame coding has recently emerged as a critical research direction. However, most existing neural B-frame code…
Compact Visual Data Representation for Green Multimedia -- A Human Visual System Perspective
Peilin Chen, Xiaohan Fang, Meng Wang +2
The Human Visual System (HVS), with its intricate sophistication, is capable of achieving ultra-compact information compression for visual signals. This remarkable ability is coupl…
An Information-Theoretic Regularizer for Lossy Neural Image Compression
Yingwen Zhang, Meng Wang, Xihua Sheng +4
Lossy image compression networks aim to minimize the latent entropy of images while adhering to specific distortion constraints. However, optimizing the neural network can be chall…