Blind image quality assessment (BIQA) of user generated content (UGC) suffers from the range effect which indicates that on the overall quality range, mean opinion score (MOS) and predicted MOS (pMOS) are well correlated; focusing on a particular range, the correlation is lower. The reason for the range effect is that the predicted deviations both in a wide range and in a narrow range destroy the uniformity between MOS and pMOS. To tackle this problem, a novel method is proposed from coarse-grained metric to fine-grained prediction. Firstly, we design a rank-and-gradient loss for coarse-grained metric. The loss keeps the order and grad consistency between pMOS and MOS, thereby reducing the predicted deviation in a wide range. Secondly, we propose multi-level tolerance loss to make fine-grained prediction. The loss is constrained by a decreasing threshold to limite the predicted deviation in narrower and narrower ranges. Finally, we design a feedback network to conduct the coarse-to-fine assessment. On the one hand, the network adopts feedback blocks to process multi-scale distortion features iteratively and on the other hand, it fuses non-local context feature to the output of each iteration to acquire more quality-aware feature representation. Experimental results demonstrate that the proposed method can alleviate the range effect compared to the state-of-the-art methods effectively.
翻译:用户生成内容(UGC)的盲人图像质量评估(BIQA)受到范围效应的影响,其范围效应表明,总体质量范围、平均评分(MOS)和预测的MOS(PMOS)是密切相关的;侧重于特定范围,相关程度较低;范围效应的原因是,在广泛和狭小范围内预测的偏差摧毁了MOS和PMOS之间的统一性。为了解决这一问题,我们提出了一个从粗微的测量到细微的预测的新型方法。首先,我们为粗微的测量度设计了等级和等级损失。损失保持了PMOS和MOS之间的秩序和等级一致性,从而降低了预测的偏差。第二,我们提出多层次容忍损失,以作出细微的预测。为了缩小阈限,以限制预测的狭窄和狭小范围的偏差。最后,我们设计了一个反馈网络,以进行粗微的到底线评估。一方面,网络将反馈区段段段与多层次质量模型的对比,从而有效地展示其非地标定的模化方法。