Image Super-Resolution (SR) techniques improve visual quality by enhancing the spatial resolution of images. Quality evaluation metrics play a critical role in comparing and optimizing SR algorithms, but current metrics achieve only limited success, largely due to the lack of large-scale quality databases, which are essential for learning accurate and robust SR quality metrics. In this work, we first build a large-scale SR image database using a novel semi-automatic labeling approach, which allows us to label a large number of images with manageable human workload. The resulting SR Image quality database with Semi-Automatic Ratings (SISAR), so far the largest of SR-IQA database, contains 8,400 images of 100 natural scenes. We train an end-to-end Deep Image SR Quality (DISQ) model by employing two-stream Deep Neural Networks (DNNs) for feature extraction, followed by a feature fusion network for quality prediction. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics and achieves promising generalization performance in cross-database tests. The SISAR database and DISQ model will be made publicly available to facilitate reproducible research.

翻译：图像超分辨率(SR)技术通过提高图像的空间分辨率提高图像的空间分辨率来提高视觉质量。质量评价指标在比较和优化斯洛伐克算法方面发挥着关键作用,但目前衡量标准只取得了有限的成功,这主要是由于缺乏大规模质量数据库,而数据库对于学习准确和稳健的SR质量度量至关重要。在这项工作中,我们首先使用一种新型的半自动标签方法来建立一个大型的SR图像数据库,这使我们能够给大量图像贴上可控的人类工作量的标签。由此形成的具有半自动评级(SISAR)的斯洛伐克图像质量数据库,迄今为止是SR-IQA最大的数据库,包含100个自然场景的8 400个图像。我们通过使用两流深层神经网络进行地貌提取,然后是用于质量预测的特征聚合网络,来培训一个端到端深图像SR质量(DISQ)模型。实验结果显示,拟议的方法优于最新水平的计量标准,并在跨数据库中实现有希望的通用性通用性测试。我们培训了两流深层图像SRRSAR数据库和DISQ模型,以便进行公开研究。