1 citations · 2 across the 8 of their papers we have counts for
9 papers
: A Large-Scale Multi-Modality Multi-View Synthesized Benchmark Dataset for Image Restoration and Content Creation
Yuanzhi Li, Lebin Zhou, Nam Ling +3
The gaming and entertainment industry is rapidly evolving, driven by immersive experiences and the integration of generative AI (GAI) technologies. Training such models effectively…
ISCS: Parameter-Guided Feature Pruning for Resource-Constrained Embodied Perception
Jinhao Wang, Nam Ling, Wei Wang +1
Prior studies in embodied AI consistently show that robust perception is critical for human-robot interaction, yet deploying high-fidelity visual models on resource-constrained age…
TVC: Tokenized Video Compression with Ultra-Low Bit Rate
Lebin Zhou, Cihan Ruan, Nam Ling +3
Tokenized visual representations have shown promise in image compression, yet their extension to video remains underexplored due to the challenges posed by complex temporal dynamic…
HDCompression: Hybrid-Diffusion Image Compression for Ultra-Low Bitrates
Lei Lu, Yize Li, Yanzhi Wang +2
Image compression under ultra-low bitrates remains challenging for both conventional learned image compression (LIC) and generative vector-quantized (VQ) modeling. Conventional LIC…
GameIR: A Large-Scale Synthesized Ground-Truth Dataset for Image Restoration over Gaming Content
Lebin Zhou, Kun Han, Nam Ling +2
Image restoration methods like super-resolution and image synthesis have been successfully used in commercial cloud gaming products like NVIDIA's DLSS. However, restoration over ga…
HybridFlow: Infusing Continuity into Masked Codebook for Extreme Low-Bitrate Image Compression
Lei Lu, Yanyue Xie, Wei Jiang +3
This paper investigates the challenging problem of learned image compression (LIC) with extreme low bitrates. Previous LIC methods based on transmitting quantized continuous featur…
Image and Video Compression using Generative Sparse Representation with Fidelity Controls
Wei Jiang, Wei Wang
We propose a framework for learned image and video compression using the generative sparse visual representation (SVR) guided by fidelity-preserving controls. By embedding inputs i…
Online Meta Adaptation for Variable-Rate Learned Image Compression
Wei Jiang, Wei Wang, Songnan Li +1
This work addresses two major issues of end-to-end learned image compression (LIC) based on deep neural networks: variable-rate learning where separate networks are required to gen…
Substitutional Neural Image Compression
Xiao Wang, Wei Jiang, Wei Wang +3
We describe Substitutional Neural Image Compression (SNIC), a general approach for enhancing any neural image compression model, that requires no data or additional tuning of the t…