  For large-scale still image coding tasks, the processing platform needs to
ensure that the coded images meet the quality requirement. Therefore, the
quality control algorithms that generate adaptive QP towards a target quality
level for image coding are of significant research value. However, the existing
quality control methods are limited by low accuracy, excessive computational
cost, or temporal information dependence. In this paper, we propose a concise
{\lambda} domain linear distortion model and an accurate model parameters
estimation method based on the original data. Since the model parameters are
obtained from the original data, the proposed method is decoupled from the RDO
process and can be applied to different image encoders. Experiments show that
the proposed quality control algorithm achieves the highest control accuracy
and the lowest delay in the literature at the same time. The application of
Alibaba's e-commerce platform also shows that the proposed algorithm can
significantly reduce the overall bitrate while greatly reducing the bad case
ratio.
