4 citations · 5 across the 5 of their papers we have counts for
5 papers
Causal Context Adjustment Loss for Learned Image Compression
Minghao Han, Shiyin Jiang, Shengxi Li +4
In recent years, learned image compression (LIC) technologies have surpassed conventional methods notably in terms of rate-distortion (RD) performance. Most present learned techniq…
CF-GO-Net: A Universal Distribution Learner via Characteristic Function Networks with Graph Optimizers
Zeyang Yu, Shengxi Li, Danilo Mandic
Generative models aim to learn the distribution of datasets, such as images, so as to be able to generate samples that statistically resemble real data. However, learning the under…
QVD: Post-training Quantization for Video Diffusion Models
Shilong Tian, Hong Chen, Chengtao Lv +6
Recently, video diffusion models (VDMs) have garnered significant attention due to their notable advancements in generating coherent and realistic video content. However, processin…
A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution
Zhixiong Yang, Jingyuan Xia, Shengxi Li +5
Deep learning-based methods have achieved significant successes on solving the blind super-resolution (BSR) problem. However, most of them request supervised pre-training on labell…
Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain
Qunliang Xing, Mai Xu, Shengxi Li +4
Existing quality enhancement methods for compressed images focus on aligning the enhancement domain with the raw domain to yield realistic images. However, these methods exhibit a…