13 citations · 15 across the 5 of their papers we have counts for
6 papers
CADC: Content Adaptive Diffusion-Based Generative Image Compression
Xihua Sheng, Lingyu Zhu, Tianyu Zhang +3
Diffusion-based generative image compression has demonstrated remarkable potential for achieving realistic reconstruction at ultra-low bitrates. The key to unlocking this potential…
Learning Switchable Priors for Neural Image Compression
Haotian Zhang, Yuqi Li, Li Li +1
Neural image compression (NIC) usually adopts a predefined family of probabilistic distributions as the prior of the latent variables, and meanwhile relies on entropy models to est…
The Gap Between Principle and Practice of Lossy Image Coding
Haotian Zhang, Dong Liu
Lossy image coding is the art of computing that is principally bounded by the image's rate-distortion function. This bound, though never accurately characterized, has been approach…
Generalized Gaussian Model for Learned Image Compression
Haotian Zhang, Li Li, Dong Liu
In learned image compression, probabilistic models play an essential role in characterizing the distribution of latent variables. The Gaussian model with mean and scale parameters…
Few-Shot Domain Adaptation for Learned Image Compression
Tianyu Zhang, Haotian Zhang, Yuqi Li +2
Learned image compression (LIC) has achieved state-of-the-art rate-distortion performance, deemed promising for next-generation image compression techniques. However, pre-trained L…
On Uniform Scalar Quantization for Learned Image Compression
Haotian Zhang, Li Li, Dong Liu
Learned image compression possesses a unique challenge when incorporating non-differentiable quantization into the gradient-based training of the networks. Several quantization sur…