No-reference Image Semantic Quality Approach using Neural Network

Abstract

Assessment for image quality traditionally needs its original image as a reference but the most of time it is not the case. So, No-Reference (NR) Image Quality Assessment (IQA) seeks to assign quality scores that are consistent with human perception but without an explicit comparison with the reference image. Unfortunately, the field of NR IQA has been largely unexplored. This paper presents a new NR Image Semantic Quality Approach (NR-ISQA) that employs adaptive Neural Networks (NN) to assess the semantic quality of image color. This NN measures the quality of an image by predicting the mean opinion score (MOS) of human observer, using a set of proposed key features especially to describe color. This challenging issues aim at emulating judgment and replacing very complex and time-consuming subjective quality assessment. Two variants of our approach are proposed: the direct and the progressive of the overall quality image. The results show the performances of the proposed approach compared with the human performances.