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Despite the considerable effort devoted to high-generalizable blind image quality assessment (BIQA), the generalization performance of the state-of-the-art metrics remains limited when facing new visual scenes. A straightforward way to address the dilemma is labeling a great number of images from the new scene and subsequently training a new model, which is quite labor-intensive and cost-expensive. Hence, there is an urgent need to mitigate the dependency on labeled samples by designing a data-efficient BIQA algorithm. Motivated by the above facts, this paper presents an Active Learning-based IQA (AL-IQA) framework, which reduces the requirement for training samples by selecting representative images from two perspectives, including distortion and content. Specifically, in terms of distortion, we design distortion prompts and adopt Contrastive Language-Image Pre-Training (CLIP) to predict image distortion in a zero-shot manner. Then, we employ curriculum learning-inspired strategy to select samples with gradually increasing difficulty (measured by prediction uncertainty of CLIP), in order to facilitate model training. Meantime, in terms of content, we adopt distribution matching-based dataset distillation to distill unlabeled images into several high-density informative synthetic images. Then, feature distances between unlabeled images and distilled images are compared to identify images with the most representative content. Finally, Borda count is adopted to capture a consensus of both distortion and content through weighted counting, and prompt tuning is utilized for adapting the model to the IQA task. Extensive experiments are conducted on five IQA datasets, and the results demonstrate that the proposed AL-IQA not only effectively reduces the number of training samples but also achieves state-of-the-art prediction accuracy and generalization performance. The source code is available at https://github.com/esnthere/AL-IQA.

Omnidirectional image quality assessment (OIQA) has become a research hotspot in the field of image processing. The existing OIQA metrics have the common problem of incomplete perceptual information representation, which results in performance limitation. To deal with this problem, a perceptual information completion-based Siamese (PICS) OIQA network is proposed. Specifically, the input OI is first reprojected to generate another OI with complementary perceptual information. Both the original input OI and the generated OI are simultaneously fed into the following QA network, forming the Siamese network structure. For each branch of the QA network, the multiresolution local features and the complementary global features are severally extracted by convolution operations and self-attention modules and then are hierarchically aggregated to more fully simulate the visual perception mechanism. Finally, the aggregated features of the output of two branches are further fused to measure the quality of the OI to be tested. Through extensive experiments on two public OIQA datasets and deep analysis of the experimental results, the performance advancement of the PICS method is demonstrated in the OIQA task.