  The screen content images (SCIs) usually comprise various content types with
sharp edges, in which the artifacts or distortions can be well sensed by the
vanilla structure similarity measurement in a full reference manner.
Nonetheless, almost all of the current SOTA structure similarity metrics are
"locally" formulated in a single-level manner, while the true human visual
system (HVS) follows the multi-level manner, and such mismatch could eventually
prevent these metrics from achieving trustworthy quality assessment. To
ameliorate, this paper advocates a novel solution to measure structure
similarity "globally" from the perspective of sparse representation. To perform
multi-level quality assessment in accordance with the real HVS, the
above-mentioned global metric will be integrated with the conventional local
ones by resorting to the newly devised selective deep fusion network. To
validate its efficacy and effectiveness, we have compared our method with 12
SOTA methods over two widely-used large-scale public SCI datasets, and the
quantitative results indicate that our method yields significantly higher
consistency with subjective quality score than the currently leading works.
Both the source code and data are also publicly available to gain widespread
acceptance and facilitate new advancement and its validation.
