4 papers
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…
Learned Image Compression with Dictionary-based Entropy Model
Jingbo Lu, Leheng Zhang, Xingyu Zhou +3
Learned image compression methods have attracted great research interest and exhibited superior rate-distortion performance to the best classical image compression standards of the…
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…