DynaQuant: Dynamic Mixed-Precision Quantization for Learned Image Compression
Youneng Bao, Yulong Cheng, Yiping Liu, Yichen Yang, Peng Qin, Mu Li, Yongsheng Liang
Abstract
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 distributions and sensitivity characteristics inherent in LIC models. This leads to a suboptimal trade-off between performance and efficiency. In this paper, we introduce DynaQuant, a novel framework for dynamic mixed-precision quantization that operates on two complementary levels. First, we propose content-aware quantization, where learnable scaling and offset parameters dynamically adapt to the statistical variations of latent features. This fine-grained adaptation is trained end-to-end using a novel Distance-aware Gradient Modulator (DGM), which provides a more informative learning signal than the standard Straight-Through Estimator. Second, we introduce a data-driven, dynamic bit-width selector that learns to assign an optimal bit precision to each layer, dynamically reconfiguring the network
BibTeX
@inproceedings{aaai2026_dynaquantdynamic,
title = {DynaQuant: Dynamic Mixed-Precision Quantization for Learned Image Compression},
author = {Youneng Bao and Yulong Cheng and Yiping Liu and Yichen Yang and Peng Qin and Mu Li and Yongsheng Liang},
booktitle = {AAAI 2026},
year = {2026}
}