Abstract
Most of no-reference image sharpness metrics suffer interference of tiny textures and the contrast around edges. Based on large-scale structure, this paper proposes a novel metric to avoid this problem. Firstly prominent edges are extracted by a weighted least-squares filter. Then, we calculate the statistics proportion of edges with different width and the mean value of the maximum gradients from the 51th to 250th as the features of the image. Finally, the support vector regression is used to get the relationship model between the features and the subjective assessment result. The metric can reduce the influence of textures, contrast and the content of images effectively, accordingly enhancing the assessment accuracy.
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.
