I²NQ: Inter and Intra Nonuniform Quantization for Single Image Super-Resolution

Abstract

Quantizing neural network is an efficient model compression technique that converts weights and activations from floating-point to integer. However, existing model quantization methods are primarily designed for high-level visual tasks. They do not sufficiently consider the unique characteristics of feature distribution in image super-resolution (SR) reconstruction models. On the one hand, the objective of SR is to restore high-frequency and fine-detail information while preserving the overall feature distribution. Therefore, the regularization techniques are removed to maintain the original distribution. However, vanilla quantization methods often employ regularization techniques to normalize the features for stable network training, which destroys the inherent information of the feature distribution. On the other hand, the feature distribution in SR models exhibits a nonuniform bell-shaped form. Common quantization methods adopt a uniform quantization strategy with equal quantization intervals. This fails to effectively capture the nonuniform feature distribution in SR. To address the above issue, we propose a novel method named Inter and Intra Nonuniform Quantization, which takes into account the specific characteristics of the feature distribution in the context of SR reconstruction models. Additionally, we propose a weight adjustment method called flex-scale-weight-adjust (FSWA). It can maintain the diversity of weight information and reduce quantization errors. Extensive experiments demonstrate that our proposed method surpasses other quantization methods in both the evaluation of reconstruction metrics and visual reconstruction performance.