  No-Reference Image Quality Assessment (IQA) aims at estimating image quality
in accordance with subjective human perception. However, most existing NR-IQA
methods focus on exploring increasingly complex networks or components to
improve the final performance. Such practice imposes great limitations and
complexity on IQA methods, especially when they are applied to high-resolution
(HR) images in the real world. Actually, most images own high spatial
redundancy, especially for those HR data. To further exploit the characteristic
and alleviate the issue above, we propose a new framework for Image Quality
Assessment with compressive Sampling (dubbed S-IQA), which consists of three
components: (1) The Flexible Sampling Module (FSM) samples the image to obtain
measurements at an arbitrary ratio. (2) Vision Transformer with the Adaptive
Embedding Module (AEM) makes measurements of uniform size and extracts deep
features (3) Dual Branch (DB) allocates weight for every patch and predicts the
final quality score. Experiments show that our proposed S-IQA achieves
state-of-the-art result on various datasets with less data usage.
