Abstract
In this article, we present a new no-reference (NR) objective image quality metric based on image classification. We also propose a new blocking metric and a new blur metric. Both metrics are NR metrics since they need no information from the original image. The blocking metric was computed by considering that the visibility of horizontal and vertical blocking artifacts can change depending on background luminance levels. When computing the blur metric, we took into account the fact that blurring in edge regions is generally more sensitive to the human visual system. Since different compression standards usually produce different compression artifacts, we classified images into two classes using the proposed blocking metric: one class that contained blocking artifacts and another class that did not contain blocking artifacts. Then, we used different quality metrics based on the classification results. Experimental results show that each metric correlated well with subjective ratings, and the proposed NR image quality metric consistently provided good performance with various types of content and distortions.
Highlights
There has been considerable interest in developing image quality metrics that predict perceptual image quality
We propose a new NR blocking metric and a new NR blur metric based on human visual sensitivity, and we propose a NR metric based on image classification
In this article, we proposed a new NR image quality metric based on image classification
Summary
There has been considerable interest in developing image quality metrics that predict perceptual image quality. In [7,8], blocking metrics were developed to measure the blockiness between adjacent block edge boundaries These methods do not consider that the visibility can be changed depending on background luminance levels. Researchers have tried to combine blur and blocking metrics to compute NR image quality metrics [16,17]. In [17], Jeong et al proposed a NR image quality metric that first computed the blur and blocking metrics and combined them for global optimization. The proposed NR blocking metric, NR blur metric, and NR image quality metric based on image classification were evaluated using three image sets (i.e. JPEG-, JPEG2000-compressed, and Gaussian-blurred images). II, the proposed blocking and blur metrics are explained, and the image quality metric based on image classification is presented.
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