We have located links that may give you full text access.
Multiple-Level Feature Based Measure for Retargeted Image Quality.
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society 2017 October 10
Objective image retargeting quality assessment (IRQA) aims to use computational models to predict the retargeted image quality consistent with subjective perception. In this paper we propose a multiple-level feature (MLF) based quality measure to predict the perceptual quality of retargeted images. We first provide an in-depth analysis on the low-level aspect ratio similarity feature, and then propose a mid-level edge group similarity feature, to better address the shape/structure related distortion. Furthermore, a high-level face block similarity feature is designed to deal with sensitive region deformation. The multiple-level features are complementary as they quantify different aspects of quality degradation in the retargeted image and the MLF measure learned by regression is used to predict the perceptual quality of retargeted images. Extensive experimental results performed on two public benchmark databases demonstrate that the proposed MLF measure achieves higher quality prediction accuracy than the existing relevant state-ofthe- art quality measures.
Full text links
Related Resources
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app