Subjective and Objective Quality Assessment of Swimming Pool Images

Abstract

As the research basis of image processing and computer vision research, image quality evaluation (IQA) has been widely used in different visual task fields. As far as we know, limited efforts have been made to date to gather swimming pool image databases and benchmark reliable objective quality models, so far. To filled this gap, in this paper we reported a new database of underwater swimming pool images for the first time, which is composed of 1500 images and associated subjective ratings recorded by 16 inexperienced observers. In addition, we proposed a main target area extraction and multi-feature fusion image quality assessment (MM-IQA) for a swimming pool environment, which performs pixel-level fusion for multiple features of the image on the premise of highlighting important detection objects. Meanwhile, a variety of well-established full-reference (FR) quality evaluation methods and partial no-reference (NR) quality evaluation algorithms are selected to verify the database we created. Extensive experimental results show that the proposed algorithm is superior to the most advanced image quality models in performance evaluation and the outcomes of subjective and objective quality assessment of most methods involved in the comparison have good correlation and consistency, which further indicating indicates that the establishment of a large-scale pool image quality assessment database is of wide applicability and importance.

G2NPAN: GAN-guided nuance perceptual attention network for multimodal medical fusion image quality assessment

“…Although this subjective evaluation method can give a score consistent with the human visual system (HVS), but, the quality score of the fused image is influenced by the environment, and cannot be directly analyzed quantitatively due to the direct human involvement. More importantly, subjective assessment is a time-consuming and labor-intensive process ( Lei et al, 2022 ; Liu et al, 2022 ). This would not be permissible in an already rushed clinical setting.…”

Medical image fusion quality assessment based on conditional generative adversarial network

“… Kang et al (2014) used deep learning techniques to accurately predict the quality of images without reference images, and their method greatly improved the performance and robustness of the algorithm. On the premise of highlighting the important detection objects, Lei et al (2022) fuses multiple features of the images at the pixel level and designed an IQA method of main target region extraction and multi-feature fusion. However, among these IQA methods, they are proposed for general use in the field of image fusion, not specifically for MMIF.…”