  The assessment of the perceptual quality of digital images is becoming
increasingly important as a result of the widespread use of digital multimedia
devices. Smartphones and high-speed internet are just two examples of
technologies that have multiplied the amount of multimedia content available.
Thus, obtaining a representative dataset, which is required for objective
quality assessment training, is a significant challenge. The Blind Image
Quality Assessment Database, BIQ2021, is presented in this article. By
selecting images with naturally occurring distortions and reliable labeling,
the dataset addresses the challenge of obtaining representative images for
no-reference image quality assessment. The dataset consists of three sets of
images: those taken without the intention of using them for image quality
assessment, those taken with intentionally introduced natural distortions, and
those taken from an open-source image-sharing platform. It is attempted to
maintain a diverse collection of images from various devices, containing a
variety of different types of objects and varying degrees of foreground and
background information. To obtain reliable scores, these images are
subjectively scored in a laboratory environment using a single stimulus method.
The database contains information about subjective scoring, human subject
statistics, and the standard deviation of each image. The dataset's Mean
Opinion Scores (MOS) make it useful for assessing visual quality. Additionally,
the proposed database is used to evaluate existing blind image quality
assessment approaches, and the scores are analyzed using Pearson and Spearman's
correlation coefficients. The image database and MOS are freely available for
use and benchmarking.
