Quality Assessment for Retargeted Images: A Review
Paper ID : 1002-MVIP2020
maryam karimi *, Erfan Entezami
گروه علوم کامپیوتر، دانشکده علوم ریاضی، دانشگاه شهرکرد، شهرکرد، ایران
Transition, saving and many processing methods causes different damage in images. Traditional quality metrics have low correlations with human scores. The key problem is to evaluate the distorted images like human subjects do. Subjective quality assessment is more reliable but is cumbersome and time consuming, so it is impossible to embed it in online applications. Therefore, many objective perceptual Image Quality Assessment (IQA) models have been developed till now. Content aware retargeting methods aim to adapt source images to target display devices with different sizes and aspect ratios so that salient areas will be less distorted. The size mismatch and the completely different distortions caused by retargeting have made common IQA methods useless in this area. Therefore, retargeted Image Quality Assessment (RIQA) methods are designed for this purpose. The quality of retargeted images is different depending to image content and retargeting algorithm. This paper provides a literature review and a new categorization on the current subjective and objective retargeted image quality measures. In addition, we intend to compare and analyze the performance of these measures in this article.
Image retargeting, Image quality assessment, Geometrical distortions, Image resizing, Retargeted image quality assessment
Status : Paper Accepted (Oral Presentation)