Abstract
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.
Abstract (translated)
No-Reference Image Quality Assessment (IQA) aims to estimate image quality based on subjective human perception. However, most existing NR-IQA methods focus on exploring increasingly complex networks or components to improve final performance. This 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 have 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.
URL
https://arxiv.org/abs/2404.17170
