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We propose an image quality assessment metric based on quaternion singular value decomposition that represents a color image as a quaternion matrix, separates image noise information using singular value decomposition and extracts features from both the whole image and its noise information. In the proposed method, the color image and its local variance are represented by using quaternion and then performing singular value decomposition. Later, 75% of singular values are taken as image noise<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2989312">doi:10.1109/access.2020.2989312</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qzxkaqwirraj7mtfar4yntxpiq">fatcat:qzxkaqwirraj7mtfar4yntxpiq</a> </span>
more »... ormation. We extract a luminance comparison, contrast comparison, structure comparison, phase congruency and gradient magnitude from whole color images and extract the peak signal-to-noise ratio from image noise information as features. Finally, these features are used as the input to a kernel extreme learning machine to predict the quality of the tested images. Extensive experiments performed on four benchmark image quality assessment databases demonstrate that the proposed metric achieves high consistency with the subjective evaluations and outperforms state-of-the-art image quality assessment metrics.
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