BIQ2021: A Large-Scale Blind Image Quality Assessment Database

TL;DR

The paper introduces BIQ2021, a comprehensive large-scale dataset of images with natural distortions and subjective quality scores, aimed at advancing no-reference image quality assessment methods.

Contribution

It provides a diverse, well-labeled image database with subjective scores, facilitating the development and benchmarking of blind image quality assessment algorithms.

Findings

The dataset includes images from various devices and sources.

Subjective scores are reliably obtained through laboratory testing.

Existing quality assessment methods are evaluated using the dataset.

Abstract

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.