Blind image quality assessment based on Multi-resolution Local Structures
Seyed Majid Khorashadizadeh, Mehdi SadeghiBakhi, Fatemeh Seifishahpar, Alimohammad Latif · 2021
Our approach to no-reference image quality assessment (IQA) is grounded on the assumption that natural scenes have certain statistical properties which are altered in the presence of distortion. Therefore, by measuring this statistic one can recognize the distortion affecting the image and provide a no-reference (NR) IQA. In this paper, we extract local entropy and standard deviation from non-overlapping 8 × 8 blocks in the image and its gradient version from 2 scales. Extracting features from gradient images can help to better distinguishing blur and noise effects on images. Having extracted local measures, we aggregate them by finding the parameters of a Gaussian mixture model fitted on these local measures. Finally, our quality metric is achieved via a Neural Network machine learning tool, and its performance is evaluated on a standard LIVE database. The experimental results point out that our proposed features not only can identify the type of distortion but also are consistent with the DMOS in subjective assessment in terms of the correlation coefficient.