BibTeX:

@article{IJIRSTV3I5073,
     title={Image Quality Assessment using the Versatile Covariance Features: A Review},
     author={Ishu Arora and Naresh Kumar Garg},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={3},
     number={5},
     pages={203--208},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV3I5073.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Image quality assessment is a process of undoing or recovering an image from degraded stage. Knowledge of degradation is needed for successful restoration. The image restoration techniques are used to make the corrupted image as compatible to the original image. Hyper spectral image are alter or corrupted by the mixture of diverse kinds of noise in the acquisition process, which may include stripes, impulse noise, Gaussian noise, deadlines, , and so on. This paper introduces the new image quality assessment methods for the quality semantic gap evaluation for restoration of the image data, which can simultaneously removes the Gaussian noise, impulse noise, deadlines, and stripes. But there is no spatial constraint applied on neighboring pixels that originates large areas of missing pixels. To handle the issue of missing pixels non-reference regularization algorithm has been proposed. The proposed model is projected to improve the overall quality of the image quality assessment methods by correctly analyzing the quality of the image matrix.

image quality assessment; adaptive quality measurement; HVS; SSIM; ESSIM; MS-SSIM