1 citations · 1 across the 7 of their papers we have counts for
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End-to-End Learned Image Coding: From Rate-Distortion Optimization to Practical Deployment
Ming Lu, Junqi Shi, Zhan Ma
End-to-end learned image coding (LIC) has achieved substantial improvements in rate--distortion (R--D) performance, yet practical deployment requires addressing a broader set of sy…
Hadamard-Domain Model Quantization for Learned Image Coding
Junqi Shi, Chongzhi Wang, Yiwen He +2
Uniform INT8 quantization is attractive for deploying learned image coding (LIC), but its rate--distortion (R--D) performance is often limited by heavy-tailed tensors and large int…
DiT-IC: Aligned Diffusion Transformer for Efficient Image Compression
Junqi Shi, Ming Lu, Xingchen Li +3
Diffusion-based image compression has recently shown outstanding perceptual fidelity, yet its practicality is hindered by prohibitive sampling overhead and high memory usage. Most…
On Quantizing Neural Representation for Variable-Rate Video Coding
Junqi Shi, Zhujia Chen, Hanfei Li +4
This work introduces NeuroQuant, a novel post-training quantization (PTQ) approach tailored to non-generalized Implicit Neural Representations for variable-rate Video Coding (INR-V…
HINER: Neural Representation for Hyperspectral Image
Junqi Shi, Mingyi Jiang, Ming Lu +3
This paper introduces {HINER}, a novel neural representation for compressing HSI and ensuring high-quality downstream tasks on compressed HSI. HINER fully exploits inter-spectral c…
TinyLIC-High efficiency lossy image compression method
Gaocheng Ma, Yinfeng Chai, Tianhao Jiang +2
Image compression has been the subject of extensive research for several decades, resulting in the development of well-known standards such as JPEG, JPEG2000, and H.264/AVC. Howeve…