Image quality assessment by an efficient correlation-based metric

Image quality can be measured visually. In the human visual system, a compressed image can be judged by the human eye. Image quality may not be perceived to decline in a region with low compression. However, image quality clearly declines in a region with high compression. As image compression increases, image quality gradually transitions from visually lossless to lossy. In this study, we aim to explain this phenomenon. A few images from different datasets were selected and compressed using JJ2000 and Apollo, which are well-known image compression algorithms. Then, error-based and correlation-based metrics were applied to these images. The correlation-based metrics agree with human-vision evaluations in experiments, but the error-based metrics do not. Inspired by the positive result of the correlation-based metrics, a new metric named the simple correlation factor (SCF) was proposed to explain the aforementioned phenomenon. The results of the SCF show good consistency with human-vision results for several datasets. In addition, the computation efficiency of the SCF is better than that of the existing correlation-based metrics.