Abstract
Blind Image Quality Assessment (BIQA) techniques evaluate the perceptual quality of a distorted image without access to its distortion-free version. In this paper, a novel BIQA measure is proposed in which interest points drawn by visually attractive regions in a grayscale image are characterized using a binary descriptor. Then, a regression technique maps the feature space to subjective opinion scores to provide the quality prediction. In this method, an additional image filtering step prior the feature extraction is used. The filtering is obtained as a solution to a problem of finding a correlation between the image quality and the keypoint detection results. The attention of the human visual system (HVS) in presence of distortions, mimicked by an interest point detector, is enhanced using the proposed quality-aware image filtering. A keypoint descriptor which mimics retinal photoreceptors in the HVS is also applied to the filtered images. In this paper, it is shown that the proposed BIQA method provides a highly competitive performance to the state-of-the-art measures on popular large-scale IQA benchmarks. It is also demonstrated that a simple application of the developed quality-aware filtering can improve the results of BIQA measures.
Highlights
The need to provide the high quality of experience for customers of a variety of multimedia applications has stimulated the growth of methods which can replace expensive, and difficult to apply in real-world techniques, image quality evaluation performed by human subjects
In this paper, a quality-aware filtering and a blind objective image quality assessment (IQA) (BIQA) technique based on such filtering are introduced
Apart from the presented filtering, the attempt to incorporate a complex binary descriptor to characterize visually attractive regions in filtered images is among the contributions of this work to BIQA
Summary
The need to provide the high quality of experience for customers of a variety of multimedia applications has stimulated the growth of methods which can replace expensive, and difficult to apply in real-world techniques, image quality evaluation performed by human subjects. It is shown that the proposed BIQA method provides the highly competitive performance to the related state-ofthe-art measures on popular IQA benchmarks It is investigated whether a filtering which makes distortions easier to capture by the introduced measure can be beneficial to many NR methods based on learning quality-aware image characteristics. The introduction of a novel NR method in which interest points detected in filtered images are described with the HVS-based technique and further characterized by statistics used by the SVR for the quality prediction.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
