70 citations · 76 across the 18 of their papers we have counts for
5 papers · 1 filter
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…
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…
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo Simulation
Jingyuan Xia, Zhixiong Yang, Shengxi Li +4
Learning-based approaches have witnessed great successes in blind single image super-resolution (SISR) tasks, however, handcrafted kernel priors and learning based kernel priors ar…
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…