  With the rising popularity of intelligent mobile devices, it is of great
practical significance to develop accurate, realtime and energy-efficient image
Super-Resolution (SR) inference methods. A prevailing method for improving the
inference efficiency is model quantization, which allows for replacing the
expensive floating-point operations with efficient fixed-point or bitwise
arithmetic. To date, it is still challenging for quantized SR frameworks to
deliver feasible accuracy-efficiency trade-off. Here, we propose a Fully
Quantized image Super-Resolution framework (FQSR) to jointly optimize
efficiency and accuracy. In particular, we target on obtaining end-to-end
quantized models for all layers, especially including skip connections, which
was rarely addressed in the literature. We further identify training obstacles
faced by low-bit SR networks and propose two novel methods accordingly. The two
difficulites are caused by 1) activation and weight distributions being vastly
distinctive in different layers; 2) the inaccurate approximation of the
quantization. We apply our quantization scheme on multiple mainstream
super-resolution architectures, including SRResNet, SRGAN and EDSR.
Experimental results show that our FQSR using low bits quantization can achieve
on par performance compared with the full-precision counterparts on five
benchmark datasets and surpass state-of-the-art quantized SR methods with
significantly reduced computational cost and memory consumption.
