Abstract
Image processing methods often introduce distortions, which affect the way an image is subjectively perceived by a human observer. To avoid inconvenient subjective tests in cases in which reference images are not available, it is desirable to develop an automatic no-reference image quality assessment (NR-IQA) technique. In this paper, a novel NR-IQA technique is proposed in which the distributions of local gradient orientations in image regions of different sizes are used to characterize an image. To evaluate the objective quality of an image, its luminance and chrominance channels are processed, as well as their high-order derivatives. Finally, statistics of used perceptual features are mapped to subjective scores by the support vector regression (SVR) technique. The extensive experimental evaluation on six popular IQA benchmark datasets reveals that the proposed technique is highly correlated with subjective scores and outperforms related state-of-the-art hand-crafted and deep learning approaches.
Highlights
Image processing methods often introduce distortions, which affect the way an image is subjectively perceived by a human observer
In an FR-image quality assessment (IQA) measure, a distorted image is compared with its reference image, while only some statistics of the distortion-free image are available in the RR-IQA case
The subjective scores obtained in tests with human subjects are denoted as mean opinion scores (MOS) or differential MOS (DMOS)
Summary
A brief review of previous studies closely related to the introduced work is presented. Unlabeled data for learning Gabor features and modeling an image using the soft-assignment coding with the max pooling are employed in [49] In another method that uses a codebook, High Order Statistics Aggregation (HOSA) [28], K-means clustering of normalized image patches and their description with the low and high order statistics are considered. Gradient-based techniques are often employed to provide effective IQA measures [11,24,25,46] These measures use global distributions of gradient magnitude maps [25], relative gradient orientations or magnitude [24,39]. Histograms of gradient magnitude, relative gradient orientation, and relative gradient magnitude maps are used and characterized using the histogram variance These gradient-based measures do not take into account local gradient distributions. Taking into account the referred works, it can be stated that the effectiveness of the HOG in NR image quality prediction remains largely uninvestigated, and a promising application of this descriptor to the NR-IQA is introduced for the first time in this paper
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