BIQ2021: a large-scale blind image quality assessment database
Nisar Ahmed, Shahzad Asif
Abstract
Perceptual quality assessment of digital images is becoming increasingly important due to widespread use of digital multimedia devices. Smartphones and high-speed internet are among the technologies that have increased the amount of multimedia content by several folds. Availability of a representative dataset, required for objective quality assessment training, is therefore an important challenge. We present a blind image quality assessment database (BIQ2021). The dataset addresses the challenge of representative images for no-reference image quality assessment by selecting images with naturally occurring distortions and reliable labeling. The dataset contains three set of images: images captured without intention of their use in image quality assessment, images obtained with intentional introduced natural distortions, and images collected from an open-source image sharing platform. Ensuring that the database contains a mix of images from different devices, containing different type of objects, and having varying degree of foreground and background information has been tried. The subjective scoring of these images is carried out in a laboratory environment through single-stimulus method to obtain reliable scores. The database provides details of subjective scoring, statistics of the human subjects, and the standard deviation of each image. The mean opinion scores (MOSs) provided with the dataset make it useful for assessment of visual quality. Moreover, existing blind image quality assessment approaches are tested on the proposed database, and the scores are analyzed using Pearson and Spearman’s correlation coefficients. The image database and the MOS along with relevant statistics are freely available for use and benchmarking.