Abstract
This paper focuses on no-reference image quality assessment(NR-IQA)metrics. In the literature, a wide range of algorithms are proposed to automatically estimate the perceived quality of visual data. However, most of them are not able to effectively quantify the various degradations and artifacts that the image may undergo. Thus, merging of diverse metrics operating in different information domains is hoped to yield better performances, which is the main theme of the proposed work. In particular, the metric proposed in this paper is based on three well-known NR-IQA objective metrics that depend on natural scene statistical attributes from three different domains to extract a vector of image features. Then, Singular Value Decomposition (SVD) based dominant eigenvectors method is used to select the most relevant image quality attributes. These latter are used as input to Relevance Vector Machine (RVM) to derive the overall quality index. Validation experiments are divided into two groups; in the first group, learning process (training and test phases) is applied on one single image quality database whereas in the second group of simulations, training and test phases are separated on two distinct datasets. Obtained results demonstrate that the proposed metric performs very well in terms of correlation, monotonicity and accuracy in both the two scenarios.
Highlights
These days, images have become very integral part of our daily lives; they have become an essential means of communication
This paper presents the extended benchmarking experiments on both LIVEand Categorical Subjective Image Quality (CSIQ) image quality databases[14, 15]that provide the ground truth data as well as the test images from which the features vector is computed
The perceived quality evaluation framework presented in this paper is shown in figure 1; the process is composed of the following steps: 1. Features extraction: features are extracted from images coming from image databases (LIVE II and CSIQ) using three NR-IQA (BRISQUE,BIQI,BLIINDS-II)
Summary
These days, images have become very integral part of our daily lives; they have become an essential means of communication. Depending on the availability of the reference image, objective metrics can be categorized into three categories: full reference, reduced reference or no-reference ( called blind or free-reference) image quality metrics. In the latter class, we can distinguish two types of metrics: metrics intended for specific degradation, and general-purpose metrics where the type of degradation is not praying in. The nonlinear regression algorithm of the relevance vector machine (RVM) is applied to generalize prediction of quality scores to out of sample images.
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