NR IQA for noise affected images using singular value decomposition
This study presents an efficient no reference image quality assessment (NR IQA) technique to assess the quality of images affected by noise. The proposed technique is based on two characteristics of the human eye (retina) namely the presence of centre surround receptive field and visualisation utilising different spatial frequency channels. In the proposed technique, the authors model centre surround receptive field using difference of Gaussians (DoG) whereas to mimic multiple frequencies in the centre surround receptive field, they compute multiple DoG images of different values of standard deviations generated for different frequencies. Furthermore, the singular value decomposition based features are obtained from the generated DoG images to estimate the image quality. The proposed technique does not require any training, neither based on distorted/original images nor based on subjective human scores, to assess the image quality. The performance of the proposed technique is being analysed on LIVE, TID08, CSIQ and SD IVL databases and it shows that the proposed technique outperforms recently proposed NR and no training/training based IQA techniques. Experimental validation of the proposed technique in the big data scenario of 10,000 noisy images also shows encouraging results.
IET Signal Process.