This paper addresses the problem of color image quality assessment. The paper presents a novel image/ video quality
metrics, Color-SSIM (color-based structural similarity index), based on CIELAB color model. The main drawback of the
SSIM algorithm in the spatial domain is that it is highly sensitive to the translation, rotation and scaling of images. This
problem can be solved by applying SSIM to the individual channels of a color image in CIELAB color space and by
combining the results from the individual channels into a weighted vector mean. Experimental results show that
proposed quality metric is highly correlated with the experimental data collected through subjective experiments.
The paper presents a new algorithm for efficient compression of sequences of medical images, based on the Inverse
Pyramid Decomposition, called Group coding. The same approach is adapted for the efficient archiving of multispectral
images as well. The algorithm is based on joint processing of all images in a group, representing the same object, and
obtained using sensors of changing light length (multispectral images) or after time intervals. The background of the new
approach is the use of the Inverse Pyramid Decomposition, which performs leveled image representation with increasing
quality of the approximations obtained in the consecutive decomposition levels. The coarsest approximation of one of
the images in the group, selected to be used as a reference one, is used to calculate the next (better) approximations of
the remaining images in the group. As a result, is obtained efficient compression of the processed groups of images,
which is of high importance for their efficient archiving and storage in image databases. Numerous experiences were
performed with satellite and medical images, which proved the method efficiency.
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