  As recent advances in mobile camera technology have enabled the capability to
capture high-resolution images, such as 4K images, the demand for an efficient
deblurring model handling large motion has increased. In this paper, we
discover that the image residual errors, i.e., blur-sharp pixel differences,
can be grouped into some categories according to their motion blur type and how
complex their neighboring pixels are. Inspired by this, we decompose the
deblurring (regression) task into blur pixel discretization (pixel-level blur
classification) and discrete-to-continuous conversion (regression with blur
class map) tasks. Specifically, we generate the discretized image residual
errors by identifying the blur pixels and then transform them to a continuous
form, which is computationally more efficient than naively solving the original
regression problem with continuous values. Here, we found that the
discretization result, i.e., blur segmentation map, remarkably exhibits visual
similarity with the image residual errors. As a result, our efficient model
shows comparable performance to state-of-the-art methods in realistic
benchmarks, while our method is up to 10 times computationally more efficient.
