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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…
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…
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…