While the design of blind image quality assessment (IQA) algorithms has improved significantly, the distribution shift between the training and testing scenarios often leads to a poor performance of these methods at inference time. This motivates the study of test time adaptation (TTA) techniques to improve their performance at inference time. Existing auxiliary tasks and loss functions used for TTA may not be relevant for quality-aware adaptation of the pre-trained model. In this work, we introduce two novel quality-relevant auxiliary tasks at the batch and sample levels to enable TTA for blind IQA. In particular, we introduce a group contrastive loss at the batch level and a relative rank loss at the sample level to make the model quality aware and adapt to the target data. Our experiments reveal that even using a small batch of images from the test distribution helps achieve significant improvement in performance by updating the batch normalization statistics of the source model.

翻译：尽管盲图像质量评估（IQA）算法的设计已取得显著进步，但训练与测试场景之间的分布偏移常导致这些方法在推理时性能不佳。这促使研究人员探索测试时自适应（TTA）技术以提升其推理性能。现有用于TTA的辅助任务与损失函数可能不适用于预训练模型的质量感知自适应。本文提出两种新颖的质量相关辅助任务——批级别与样本级别方法，以实现盲IQA的测试时自适应。具体而言，我们引入批级别的组对比损失与样本级别的相对排序损失，使模型具备质量感知能力并适应目标数据。实验表明，即使仅使用测试分布中的小批量图像，通过更新源模型的批归一化统计量，模型性能也能获得显著提升。