Assessing media retargeting is not a trivial issue. When resizing one image to a particular percentage of its original size, some content has to be removed, which may affect the image's original meaning and/or composition. We examine the impact of the retargeting process on human fixations, by gathering eye-tracking data for a representative benchmark of retargeted images. We compute their derived saliency maps as input to a set of computational image distance metrics. When analyzing the fixations, we found that even strong artifacts may go unnoticed for areas outside the original regions of interest. We also note that the most important alterations in semantics are due to content removal. Since using an eye tracker is not always a feasible option, we additionally show how an existing model of prediction of human fixations also works sufficiently well in a retargeting context.

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@inproceedings{Castillo2011, author = {Susana Castillo, Tilke Judd and Diego Gutierrez}, title = {Using Eye-Tracking to Assess Different Image Retargeting Methods}, booktitle = {Symposium on Applied Perception in Graphics and Visualization (APGV)}, year = {2011}, location = {Toulouse, France}, publisher = {ACM Press}, }

@article {RGSS-10, title = {A Comparative Study of Image Retargeting}, author = {Michael Rubinstein and Diego Gutierrez and Olga Sorkine and Ariel Shamir}, journal = {ACM Transactions on Graphics, Proceedings Siggraph Asia}, volume = {29}, number = {5}, year = {2010} }
@InProceedings{Judd_2009, author = {Tilke Judd and Krista Ehinger and Fr{\'e}do Durand and Antonio Torralba}, title = {Learning to Predict Where Humans Look}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2009} }