  While most vision tasks are essentially visual in nature (for recognition),
some important tasks, especially in the medical field, also require
quantitative analysis (for quantification) using quantitative images. Unlike in
visual analysis, pixel values in quantitative images correspond to physical
metrics measured by specific devices (e.g., a depth image). However, recent
work has shown that it is sometimes possible to synthesize accurate
quantitative values from visual ones (e.g., depth from visual cues or defocus).
This research aims to improve quantitative image synthesis (QIS) by exploring
pretraining and image resolution scaling. We propose a benchmark for evaluating
pretraining performance using the task of QIS-based bone mineral density (BMD)
estimation from plain X-ray images, where the synthesized quantitative image is
used to derive BMD. Our results show that appropriate pretraining can improve
QIS performance, significantly raising the correlation of BMD estimation from
0.820 to 0.898, while others do not help or even hinder it. Scaling-up the
resolution can further boost the correlation up to 0.923, a significant
enhancement over conventional methods. Future work will include exploring more
pretraining strategies and validating them on other image synthesis tasks.
