Blind Quality Assessment of Camera Images Based on Low-Level and High-Level Statistical Features

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

2 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)135-146
Journal / PublicationIEEE Transactions on Multimedia
Volume21
Issue number1
Early online date25 Jun 2018
Publication statusPublished - Jan 2019

Abstract

Camera images in reality are easily affected by various distortions, such as blur, noise, blockiness, etc., which damage the quality of the images. The complexity of the distortions in camera images raises great challenge for precisely predicting their perceptual quality. In this paper, we present an image quality assessment (IQA) approach which aims to solve this challenging problem to some extent. In the proposed method, we firstly extract the low-level and high-level statistical features which can capture the quality degradations effectively. On one hand, the first kind of statistical features are extracted from the locally mean subtracted and contrast normalized (MSCN) coefficients, which denote the low-level features in the early human vision. On the other hand, recently proposed brain theory and neuroscience, especially the free-energy principle reveals that the human brain tries to explain its encountered visual scenes through an inner creative model, with which the brain can produce the projection for the image. Then the quality of perceptions can be reflected by the divergence between the image and its brain projection. Based on this, we extract the second type of features from the brain perception mechanism which represent the high-level features. The low-level and high-level statistical features can play a complementary role in quality prediction. After feature extraction, we design a neural network to integrate all the features and convert them to the final quality score. Extensive tests performed on two real camera image datasets prove the validity of our method and its advantageous predicting ability over the competitive IQA models.

Research Area(s)

  • Cameras, Computer science, Degradation, Distortion, Estimation, Feature extraction, free-energy principle, Image quality, Image quality assessment (IQA), natural image statistics, neural network, no-reference (NR)/blind