Screen content images (SCIs) usually comprise various content types with sharp edges, in which artifacts or distortions can be effectively sensed by a vanilla structure similarity measurement in a full-reference manner. Nonetheless, almost all of the current state-of-the-art (SOTA) structure similarity metrics are “locally” formulated in a single-level manner, while the true human visual system (HVS) follows the multilevel manner; such mismatch could eventually prevent these metrics from achieving reliable quality assessment. To ameliorate this issue, this article advocates a novel solution to measure structure similarity “globally” from the perspective of sparse representation. To perform multilevel quality assessment in accordance with the real HVS, the abovementioned 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 scores than the current leading works. Both the source code and data are also publicly available to gain widespread acceptance and facilitate new advancement and validation.

中文翻译：

---

融合多级结构相似度的全参考屏幕内容图像质量评估

屏幕内容图像（SCIs）通常包括具有锐利边缘的各种内容类型，其中伪影或失真可以通过完全参考方式的普通结构相似性测量来有效地感知。尽管如此，几乎所有当前最先进的（SOTA）结构相似度度量都是以单级方式“局部”制定的，而真正的人类视觉系统（HVS）遵循多级方式；这种不匹配最终可能会阻止这些指标实现可靠的质量评估。为了改善这个问题，本文提倡一种新颖的解决方案，从稀疏表示的角度“全局”测量结构相似度。按照真实的HVS进行多层次的质量评估，通过采用新设计的选择性深度融合网络，上述全局度量将与传统的局部度量相结合。为了验证其有效性和有效性，我们在两个广泛使用的大规模公共 SCI 数据集上将我们的方法与 12 种 SOTA 方法进行了比较，定量结果表明，我们的方法与目前的领先作品相比，与主观质量得分的一致性明显更高。源代码和数据也都是公开的，以获得广泛的接受并促进新的进步和验证。定量结果表明，与目前的领先作品相比，我们的方法与主观质量得分的一致性明显更高。源代码和数据也都是公开的，以获得广泛的接受并促进新的进步和验证。定量结果表明，与目前的领先作品相比，我们的方法与主观质量得分的一致性明显更高。源代码和数据也都是公开的，以获得广泛的接受并促进新的进步和验证。