1 citations · 1 across the 8 of their papers we have counts for
9 papers
Locality-Aware Density Control for Efficient Gaussian-based Image Representation
Jiacong Chen, Qingyu Mao, Xiandong Meng +5
2D Gaussian Splatting is an attractive direction for image representation due to its explicit formulation, fast rasterization, and favorable decoding efficiency. The representation…
Variable-Length Finite-Rate CSI Feedback With Generative Priors
Yangxuan Cheng, Fanyang Meng, Jian Zou +4
This letter studies scalable finite-rate CSI feedback for FDD massive MIMO. Existing scalable neural schemes usually obtain rate flexibility by ordering, masking, quantizing, vecto…
STGV: Spatio-Temporal Hash Encoding for Gaussian-based Video Representation
Jierun Lin, Jiacong Chen, Qingyu Mao +4
2D Gaussian Splatting (2DGS) has recently become a promising paradigm for high-quality video representation. However, existing methods employ content-agnostic or spatio-temporal fe…
DynaQuant: Dynamic Mixed-Precision Quantization for Learned Image Compression
Youneng Bao, Yulong Cheng, Yiping Liu +4
Prevailing quantization techniques in Learned Image Compression (LIC) typically employ a static, uniform bit-width across all layers, failing to adapt to the highly diverse data di…
Dataset Distillation as Data Compression: A Rate-Utility Perspective
Youneng Bao, Yiping Liu, Zhuo Chen +3
Driven by the ``scale-is-everything'' paradigm, modern machine learning increasingly demands ever-larger datasets and models, yielding prohibitive computational and storage require…
ShiftLIC: Lightweight Learned Image Compression with Spatial-Channel Shift Operations
Youneng Bao, Wen Tan, Chuanmin Jia +3
Learned Image Compression (LIC) has attracted considerable attention due to their outstanding rate-distortion (R-D) performance and flexibility. However, the substantial computatio…
Enhancing Adversarial Training with Prior Knowledge Distillation for Robust Image Compression
Zhi Cao, Youneng Bao, Fanyang Meng +4
Deep neural network-based image compression (NIC) has achieved excellent performance, but NIC method models have been shown to be susceptible to backdoor attacks. Adversarial train…
Bandwidth-efficient Inference for Neural Image Compression
Shanzhi Yin, Tongda Xu, Yongsheng Liang +4
With neural networks growing deeper and feature maps growing larger, limited communication bandwidth with external memory (or DRAM) and power constraints become a bottleneck in imp…
Universal Efficient Variable-rate Neural Image Compression
Shanzhi Yin, Chao Li, Youneng Bao +1
Recently, Learning-based image compression has reached comparable performance with traditional image codecs(such as JPEG, BPG, WebP). However, computational complexity and rate fle…