Dataset of Subjective Assessment for Visually Near-Lossless Image Coding based on Just Noticeable Difference
Soichiro Honda, Yoshihiro Maeda, Norishige Fukushima · 2023
Image compression is essential in image processing, and image quality assessment (IQA) is important in determining the image compression level. This study aims to construct a dataset for evaluating image quality at low compression in this coding degradation, i.e., for high-quality images. Typical IQA databases are selected for general-purpose image degradation, not high-quality images. If one tries to evaluate high-quality images, multi-level evaluations are difficult to construct successfully. In addition, the evaluated encoder is not a de facto standard encoding algorithm. Therefore, this study constructs a dataset for subjective evaluation of visually near-lossless level image compression quality based on binary level evaluation of the just noticeable difference (JND). Experimental results showed that the new dataset was validated for correlation by various IQAs. It was also shown that more than the compression quality covered by the conventional dataset is needed for the binary evaluation of high-quality images. The dataset is available at: https://norishigefukushima.github.io/iqanearlossless/.