No-reference perceptual image quality metric using gradient profiles for JPEG2000

No-reference measurement of perceptual image quality is a crucial and challenging issue in modern image processing applications. One of the major difficulties is that some inherent features of natural images and artifacts are possibly rather ambiguous. In this paper, we tackle this problem using statistical information on image gradient profiles and propose a novel quality metric for JPEG2000 images. The key part of the metric is a histogram representing the sharpness distribution of the gradient profiles, from which a blur metric that is insensitive to inherently blurred structures in the natural image is established. Then a ringing metric is built based on ringing visibilities of regions associated with the gradient profiles. Finally, a combination model optimized through plenty of experiments is developed to predict the perceived image quality. The proposed metric achieves performance competitive with the state-of-the-art no-reference metrics on public datasets and is robust to various image contents.

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