基于顺序分级与数据偏置修正优化的二阶段超分辨率量化方案OA

Model quantization technology effectively reduces model storage and computational overhead by mapping high-precision floating-point data to low-bit discrete spaces.A core focus of model quantization research is how to rationally account for the characteristics of parameter distributions to construct superior mapping schemes.Existing Post-Training Quantization(PTQ)schemes nearly universally assume that the data distribution of non-activation layers follows a symmetric bell-shaped curve,but overlook the fact that small biases introduced by the model's activation layers and inputs induce distributional asymmetry.Consequently,the resulting quantization mapping is skewed to one side due to this subtle asymmetry,leading to significant approximation loss.This paper investigates quantization schemes for image Super-Resolution(SR)and proposes improvements to the widely recognized two-stage PTQ scheme.First,the max-min-based equal partitioning employed in the pre-search for quantization bounds is modified to a sorting-based non-uniform partitioning approach.Second,a bias term is introduced during the pseudo-quantization process,where a portion of the data and its mean are adaptively shifted to mitigate estimation loss caused by data bias.The improved scheme outperforms the original counterpart across all performance metrics while maintaining comparable high compression ratio and acceleration ratio-compared to the original SwinIR-light model,it reduces the parameter count by more than 60％and accelerates the SR process by more than 3 times.