This paper introduces Distribution-Flexible Subset Quantization (DFSQ), a
post-training quantization method for super-resolution networks. Our motivation
for developing DFSQ is based on the distinctive activation distributions of
current super-resolution models, which exhibit significant variance across
samples and channels. To address this issue, DFSQ conducts channel-wise
normalization of the activations and applies distribution-flexible subset
quantization (SQ), wherein the quantization points are selected from a
universal set consisting of multi-word additive log-scale values. To expedite
the selection of quantization points in SQ, we propose a fast quantization
points selection strategy that uses K-means clustering to select the
quantization points closest to the centroids. Compared to the common iterative
exhaustive search algorithm, our strategy avoids the enumeration of all
possible combinations in the universal set, reducing the time complexity from
exponential to linear. Consequently, the constraint of time costs on the size
of the universal set is greatly relaxed. Extensive evaluations of various
super-resolution models show that DFSQ effectively retains performance even
without fine-tuning. For example, when quantizing EDSRx2 on the Urban
benchmark, DFSQ achieves comparable performance to full-precision counterparts
on 6- and 8-bit quantization, and incurs only a 0.1 dB PSNR drop on 4-bit
quantization.

该论文介绍了一种名为 DFSQ 的后训练量化方法，以优化超分辨率网络，通过对激活进行通道归一化并应用分布灵活的子集量化（SQ）策略来缩短量化点的选择时间，并提出了一种快速的 K-means 聚类量化点选择策略。DFSQ 在保留各种超分辨率模型的性能的同时，大大放宽了通用集合大小的时间成本约束。