Blind Omnidirectional Image Quality Assessment Based on Structure and Natural Features

Abstract

Omnidirectional image quality assessment (OIQA) is a hot research topic in image processing. Besides, IQA is also very important in the field of instrumentation and measurement. In this article, we propose a new no-reference OIQA model by analyzing the relationship between image features and perceived quality. A gradient weighted local phrase quantization map is built to complement the gradient map to model the structure degradation of omnidirectional images caused by various distortions. Considering the large amount of information contained in the omnidirectional image, we extract the luminance features, global entropy instead of local entropy, and color features, which are ignored in most previous works to estimate the naturalness quality of the omnidirectional image. Finally, support vector regression is utilized to train and test our model based on the image features and human subjective quality assessment scores. The experiments on two databases show that our model outperforms the state-of-the-art IQA models and correlates well with subjective scores in the two databases.