  Single image super-resolution (SISR) aims to reconstruct a high-resolution
image from its low-resolution observation. Recent deep learning-based SISR
models show high performance at the expense of increased computational costs,
limiting their use in resource-constrained environments. As a promising
solution for computationally efficient network design, network quantization has
been extensively studied. However, existing quantization methods developed for
SISR have yet to effectively exploit image self-similarity, which is a new
direction for exploration in this study. We introduce a novel method called
reference-based quantization for image super-resolution (RefQSR) that applies
high-bit quantization to several representative patches and uses them as
references for low-bit quantization of the rest of the patches in an image. To
this end, we design dedicated patch clustering and reference-based quantization
modules and integrate them into existing SISR network quantization methods. The
experimental results demonstrate the effectiveness of RefQSR on various SISR
networks and quantization methods.
