A Feature-Enriched Completely Blind Image Quality Evaluator
Existing blind image quality assessment (BIQA) methods are mostly opinion-aware. They learn regression models from coaching pictures with associated human subjective scores to predict the perceptual quality of check images. Such opinion-aware ways, but, need a giant quantity of coaching samples with associated human subjective scores and of a variety of distortion types. The BIQA models learned by opinion-aware ways usually have weak generalization capability, hereby limiting their usability in follow. By comparison, opinion-unaware ways do not would like human subjective scores for training, and so have greater potential for good generalization capability. Unfortunately, thus so much no opinion-unaware BIQA methodology has shown consistently better quality prediction accuracy than the opinion-aware ways. Here, we have a tendency to aim to develop an opinion-unaware BIQA method that can compete with, and maybe outperform, the prevailing opinion-aware strategies. By integrating the options of natural image statistics derived from multiple cues, we have a tendency to learn a multivariate Gaussian model of image patches from a assortment of pristine natural images. Using the learned multivariate Gaussian model, a Bhattacharyya-like distance is used to measure the quality of every image patch, and then an overall quality score is obtained by average pooling. The proposed BIQA methodology will not want any distorted sample images nor subjective quality scores for coaching, yet in depth experiments demonstrate its superior quality-prediction performance to the state-of-the-art opinion-aware BIQA ways. The MATLAB supply code of our algorithm is publicly accessible at www.comp.polyu.edu.hk/ cslzhang/IQA/ILNIQE/ILNIQE.htm.
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