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.
Highlights
The acquisition of underwater images plays a significant role in the research of underwater rescue and biometric tracking at swimming pools in Fei et al (2012), Alshbatat et al (2020), and Pleština et al (2020)
The swimming pool image database is a largescale image quality evaluation (IQA) database with 1500 images generated from 150 pristine images, having 5 five distortion levels and 1 one distortion type, it is chosen as the testing bed
As a new research field, the swimming pool image research has been more and more people’s attention been gathering increasing attention in recent years, at present there are a lot of swimming pool water to carry on many areas in which to ask research questions, such as swimming pool environment anomaly detection, swimming pool body posture recognition, swimming pool, target tracking, etc., and the image quality is the basis of all vision problems, so the establishment of the swimming pool image database is very necessary
Summary
The acquisition of underwater images plays a significant role in the research of underwater rescue and biometric tracking at swimming pools in Fei et al (2012), Alshbatat et al (2020), and Pleština et al (2020). It is urgent necessary to design an objective evaluation method that can simulate the human visual system (HVS) to automatically measure the image quality These objective IQA approaches can be classified into the following three categories based on the degree of reference to the original image information: full reference (FR) method, reduce reference (RR) method, and no reference (NR) method. Since it is not easy to obtain the original image in some cases, this method has attracted the attention of scholars in recent years (Gu et al, 2015b; Min et al, 2018), and RR method, which is involved in Chen et al (2021), can obtain some information of the image. This method evaluates the image quality by comparing the difference between the extracted reference image and the partial information of the distorted image
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