AI RESEARCH
QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks
arXiv CS.CV
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ArXi:2605.22351v1 Announce Type: new Low-bit quantization is widely used to compress super-resolution (SR) models and reduce storage and computation costs for deployment on resource-limited devices. However, when SR models are pushed to ultra-low precision (2-4 bits), performance can drop sharply due to diminished representational capacity and the detail-sensitive nature of SR. To address these issues, we propose QuantSR+, a unified framework that improves quantization operators, network design, and