Unsupervised blind image quality assessment via joint spatial and transform features

Abstract

A novel unsupervised blind image quality assessment (BIQA) method, which requires no mean opinion scores for model training is presented in this paper. The method employs joint spatial and transform features as quality degradation metrics, specifically, phase congruency, gradient magnitude (GM), and GM and Laplacian of Gaussian response and local normalized coefficient are extracted as spatial features, and Karhunen–Loéve transform coefficient and discrete cosine transform coefficient are modeled as transform features. Both spatial and transform features are well analyzed to remove the redundancy, and then fitted to the multivariate Gaussian model for no-reference image quality assessment. Extensive experiments conducted on seven IQA databases demonstrate the superiority of the proposed method over the state-of-the-art both supervised and unsupervised BIQA methods.

Objective Video Quality Assessment and Ground Truth Coordinates for Automatic License Plate Recognition

“…BRISQUE [25]: The Blind/Reference-less Image Spatial Quality Evaluator, which aims to assess the quality of images without a reference image; 14. NIQE [26]: The Naturalness Image Quality Evaluator evaluates the perceptual quality of an image in a completely blind manner; 15. OG-IQA [27]: Object Geometric-Based Image Quality Assessment focuses on evaluating the image quality based on geometric distortions; 16.…”