| Summary: | The importance of Image quality assessment (IQA)
is ever increasing due to the fast paced advances in imaging
technology and computer vision. Among the numerous IQA
methods, Structural SIMilarity (SSIM) index and its variants
are better matched to the perceived quality of the human visual
system. However, SSIM methods are insufficiently sensitive, when
images contain low information, where the important information
only occupies a low proportion of the image while most of
the image is noise-like, which is common in scientific data.
Therefore, we propose two new IQA methods, InTensity Weighted
SSIM index and Low-Information Similarity Index, for such low
information images. In addition, auxiliary indexes are proposed
to assist with the assessment. The application of these new IQA
methods to natural images and field-specific images, such as
radio astronomical images, medical images, and remote sensing
images, are also demonstrated. The results show that our IQA
methods perform better than state-of-the-art SSIM methods
for differences in high-intensity parts of the input images and
have similar performance to that of the original and gradientbased SSIM for differences in low-intensity parts. Different
similarity indexes are suitable for different applications, which
we demonstrate in our results.
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