Peng Qin — accepted AI-conference papers

2026

DynaQuant: Dynamic Mixed-Precision Quantization for Learned Image Compression

Youneng Bao, Yulong Cheng, Yiping Liu, Yichen Yang, Peng Qin, Mu Li +1

AAAI 2026technical

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 perform

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2024

Leveraging Redundancy in Feature for Efficient Learned Image Compression

Peng Qin, Youneng Bao, Fanyang Meng, Wen Tan, Chao Li, Genhong Wang +1

ICASSP 2024accepted

In recent years, with the development of the field of learned image compression, numerous models with excellent rate-distortion performance have emerged. However, the considerable computational complexity inherent in these models poses challenges for their practical deployment. In this paper, we inv…

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