No-reference image quality assessment based on quality patches in real time

Chuang Zhang,Guiyue Yu,Suting Chen,Xiaoyu Huang,Xianzhao Yang

doi:10.1186/s13640-018-0361-z

Abstract

Image quality assessment methods quantify the quality of an image that is highly correlated with human-perceived image quality. In this paper, the image quality is represented by the image patches that are selected by applying the spatial correlation of pixel-pairs. The quality image patches are selected from the database LIVE2, and quality feature detectors are learned by running the FastICA algorithm on the patches. Then being quality features, the independent component coefficients are found out from each quality image patch. A Hash lookup table is built by the binarization of the independent component coefficients of quality image patches, and the Hash table can be indexed by the binary code of the independent component coefficients of a tested image. The results of proposed approach were compared with results from other methods of image quality assessment and with the subjective image quality evaluated scores. And the experimental results expressed that the proposed metric method of no-reference image quality assessment could accurately measure the distorted degree of images. The Pearson linear correlation coefficient (PCC) achieves a high value 0.949 for the accuracy of evaluating results. The Spearman order correlation (SRC) achieves a high value 0.996 for the monotonicity of evaluating results. And the root mean square error (RMSE) is 5.917 for the subjective consistency of evaluating results. Extra evaluating aerial images, the proposed method obtained the result that the foggy weather led to the worst quality.

Highlights

Higher image quality is required to transmit and acquire images with the rapid development of the digital vision technology [1, 2]
4.1 Comparing with subjective perception Testing its subjective consistence is the main criterion that evaluates whether an image quality assessment method is effective, in other words, whether the evaluated scores are consistent with the human subjective perception
The scatter of the subjective scores and the evaluated results to the database LIVE2 are shown in Fig. 6, where the horizontal axis is the scores calculated by the PIH-IQA method and the vertical axis is the Differential mean opinion score (DMOS) values of the subjective score

Summary

Introduction

Higher image quality is required to transmit and acquire images with the rapid development of the digital vision technology [1, 2]. The natural image quality assessment methods based on NSS have almost obtained the results of being higher consistent with subjective perception. In this paper, modelling the pixel-pair statistics [20] is applied to extract quality patches, which represent the image quality, for speeding up the algorithm and increasing the real-time ability of evaluating quality.

Results

Conclusion