Continual Learning of No-Reference Image Quality Assessment with Channel Modulation Kernel

No-Reference Image Quality Assessment (NR-IQA), a subset of IQA techniques, is critical in scenarios where reference images are unavailable. With advancements in camera technology and computer vision, IQA datasets have evolved significantly in distortion types, image contents, and domains. This highlights the need for a broad study of NR-IQA continual learning, optimizing on a sequence of tasks, in both in-domain and domain-Transfer settings. In this paper, we introduce the Channel Modulation Kernel (CMKernel) as a solution to enhance NR-IQA continual learning from two perspectives. Firstly, CMKernel encodes channel attention information for both in-domain and domain-Transfer scenarios. By imposing constraints on CMKernels of successive models, the channel attention distillation loss effectively mitigates the divergence between old and new models. Secondly, in the context of the domain-Transfer setting, a significant challenge lies in training a robust and transferable base model from the general domain for subsequent continual learning across specific domains. To tackle this, we introduce CMKernel-based multi-dataset learning to acquire a generative model. By dynamically weighting convolutional channels, the base model learns more equally from mixed datasets, enhancing its performance for subsequent incremental tasks. Comprehensive experiments validate the superiority of CMKernel in both in-domain and domain-Transfer continual learning settings, showcasing its efficacy in addressing the evolving challenges of NR-IQA in diverse image contexts.