  Although image super-resolution (SR) problem has experienced unprecedented
restoration accuracy with deep neural networks, it has yet limited versatile
applications due to the substantial computational costs. Since different input
images for SR face different restoration difficulties, adapting computational
costs based on the input image, referred to as adaptive inference, has emerged
as a promising solution to compress SR networks. Specifically, adapting the
quantization bit-widths has successfully reduced the inference and memory cost
without sacrificing the accuracy. However, despite the benefits of the
resultant adaptive network, existing works rely on time-intensive
quantization-aware training with full access to the original training pairs to
learn the appropriate bit allocation policies, which limits its ubiquitous
usage. To this end, we introduce the first on-the-fly adaptive quantization
framework that accelerates the processing time from hours to seconds. We
formulate the bit allocation problem with only two bit mapping modules: one to
map the input image to the image-wise bit adaptation factor and one to obtain
the layer-wise adaptation factors. These bit mappings are calibrated and
fine-tuned using only a small number of calibration images. We achieve
competitive performance with the previous adaptive quantization methods, while
the processing time is accelerated by x2000. Codes are available at
https://github.com/Cheeun/AdaBM.
