Recent years have witnessed the great success of blind image quality assessment (BIQA) in various task-specific scenarios, which present invariable distortion types and evaluation criteria. However, due to the rigid structure and learning framework, they cannot apply to the cross-task BIQA scenario, where the distortion types and evaluation criteria keep changing in practical applications. This paper proposes a scalable incremental learning framework (SILF) that could sequentially conduct BIQA across multiple evaluation tasks with limited memory capacity. More specifically, we develop a dynamic parameter isolation strategy to sequentially update the task-specific parameter subsets, which are non-overlapped with each other. Each parameter subset is temporarily settled to Remember one evaluation preference toward its corresponding task, and the previously settled parameter subsets can be adaptively reused in the following BIQA to achieve better performance based on the task relevance. To suppress the unrestrained expansion of memory capacity in sequential tasks learning, we develop a scalable memory unit by gradually and selectively pruning unimportant neurons from previously settled parameter subsets, which enable us to Forget part of previous experiences and free the limited memory capacity for adapting to the emerging new tasks. Extensive experiments on eleven IQA datasets demonstrate that our proposed method significantly outperforms the other state-of-the-art methods in cross-task BIQA. The source code of the proposed method is available at https://github.com/maruiperfect/SILF.

翻译：近年来，盲图像质量评估（BIQA）在各类特定任务场景中取得了巨大成功，这些场景常呈现不变的失真类型与评估准则。然而，由于僵硬的结构和学习框架，它们无法适用于跨任务BIQA场景——该场景中失真类型与评估准则随实际应用持续变化。本文提出一种可扩展增量学习框架（SILF），能够在有限内存容量下跨多个评估任务顺序执行BIQA。具体而言，我们开发了一种动态参数隔离策略，顺序更新彼此不重叠的任务特定参数子集。每个参数子集被临时固定，以铭记对应任务的单一评估偏好；基于任务相关性，先前固定的参数子集可在后续BIQA中自适应复用，以获得更优性能。为抑制序列任务学习中内存容量的无限制扩张，我们通过逐步选择性剪枝先前固定参数子集中的非重要神经元，构建可扩展内存单元，从而遗忘部分历史经验并释放有限内存容量以适应新任务。在十一个IQA数据集上的大量实验表明，所提方法在跨任务BIQA中显著优于其他最先进方法。所提方法的源代码已开源至https://github.com/maruiperfect/SILF。