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：盲图像质量评估（BIQA）在用户生成内容（UGC）中存在范围效应，即在全质量范围内，平均意见得分（MOS）与预测MOS（pMOS）相关性良好；但聚焦于特定范围时，相关性降低。范围效应的成因是宽窄范围内的预测偏差共同破坏了MOS与pMOS之间的一致性。为解决该问题，本文提出一种从粗粒度度量到细粒度预测的新方法。首先，我们设计一种排序-梯度损失函数用于粗粒度度量。该损失函数维持pMOS与MOS之间的顺序与梯度一致性，从而减少宽范围内的预测偏差。其次，我们提出多级容差损失函数实现细粒度预测，通过递减阈值约束逐级压缩预测偏差范围。最后，我们设计一种反馈网络执行从粗到细的评估：一方面采用反馈模块迭代处理多尺度失真特征；另一方面将非局部上下文特征融合至每次迭代输出，获取更丰富的质量感知特征表示。实验结果表明，相较于现有最优方法，本方法能有效缓解范围效应。