Abstract
With the tremendous growth and usage of digital images, no-reference image quality assessment is becoming increasingly important. This paper presents in-depth analysis of Benford’s law inspired first digit distribution feature vectors for no-reference quality assessment of natural, screen-content, and synthetic images in various viewpoints. Benford’s law makes a prediction for the probability distribution of first digits in natural datasets. It has been applied among others for detecting fraudulent income tax returns, detecting scientific fraud, election forensics, and image forensics. In particular, our analysis is based on first digit distributions in multiple domains (wavelet coefficients, DCT coefficients, singular values, etc.) as feature vectors and the extracted features are mapped onto image quality scores. Extensive experiments have been carried out on seven large image quality benchmark databases. It has been demonstrated that first digit distributions are quality-aware features, and it is possible to reach or outperform the state-of-the-art with them.
Highlights
Assurance of acceptable image quality is a crucial task in a very wide range of practical applications, such as video surveillance [1], medical image processing [2], or vision systems of autonomous vehicles [3]
The first group contains a small set of reference, pristine, distortion-free images and a large set of distorted images derived from the reference images using various artificial distortions (Gaussian blur, motion blur, contrast change, etc.) at different levels
We report on median Pearson’s linear correlation coefficient (PLCC), s rankorder correlation coefficient (SROCC), and KROCC values measured over 1000 random train–test splits
Summary
Assurance of acceptable image quality is a crucial task in a very wide range of practical applications, such as video surveillance [1], medical image processing [2], or vision systems of autonomous vehicles [3]. Any kind of image noise or distortion does deteriorate the users’ visual experience, but can lead to tragic consequences. Image quality assessment (IQA) has been in the focus of research for decades [7]. Existing IQA approaches are classified into three groups—full-reference (FR), reducedreference (RR), and no-reference (NR)—depending on the availability of the distortion-free, reference image [8,9]. The reference image is not available in the majority of real-life applications, the development of NR-IQA methods is a very popular research topic in the literature
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