Improved No-reference Noisy Image Quality Assessment Based on Masking Effect and Gradient Information

Heavy computational burden, or complex training procedure and poor universality caused by the manual setting of the fixed thresholds are the main issues associated with most of the noise image quality evaluation algorithms using domain transformation or machine learning. As an attempt for solution, an improved spatial noisy image quality evaluation algorithm based on the masking effect is presented. Firstly, according to the layer-layer progressive rule based on Hosaka principle, an image is divided into sub-blocks with different sizes that match the frequency distribution of its content, and a masking weight is assigned to each sub-block correspondingly. Then the noise in the image is detected through the pixel gradient information extraction, via a two-step strategy. Following that, the preliminary evaluation value is obtained by using the masking weights to weight the noise pollution index of all the sub-blocks. Finally, the correction and normalization are carried out to generate the whole image quality evaluation parameter-i.e. Modified No-Reference Peak Signal to Noise Ratio (MNRPSNR). Such an algorithm is tested on LIVE and TID2008 image quality assessment database, covering a variety of noise types. The results indicate that compared with the current mainstream evaluation algorithms, it has strong competitiveness, and also has the significant effects in improving the traditional algorithm. Moreover, the high degree of consistency to the human subjective feelings and the applicability to multiple noise types are well demonstrated.