Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Design of High-Level Features for Photo Quality Assessment
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
A crowdsourceable QoE evaluation framework for multimedia content
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Photo assessment based on computational visual attention model
MM '09 Proceedings of the 17th ACM international conference on Multimedia
ACQUINE: aesthetic quality inference engine - real-time automatic rating of photo aesthetics
Proceedings of the international conference on Multimedia information retrieval
Saliency-enhanced image aesthetics class prediction
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Classification of digital photos taken by photographers or home users
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
High level describable attributes for predicting aesthetics and interestingness
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Learning to predict the perceived visual quality of photos
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Content-based photo quality assessment
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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There is no doubt that an image's content determines how people assess the image aesthetically. Previous works have shown that image contrast, saliency features, and the composition of objects may jointly determine whether or not an image is perceived as aesthetically pleasing. In addition to an image's content, the way the image is presented may affect how much viewers appreciate it. For example, it may be assumed that a picture will always look better when it is displayed in a larger size. Is this "the-bigger-the-better" rule always valid? If not, in what situations is it invalid? In this paper, we investigate how an image's resolution (pixels) and physical dimensions (inches) affect viewers' appreciation of it. Based on a large-scale aesthetic assessments of 100 images displayed in a variety of resolutions and physical dimensions, we show that an image's size significantly affects its aesthetic rating in a complicated way. Normally a picture looks better when it is bigger, but it may look worse depending on its content. We develop a set of regression models to predict a picture's absolute and relative aesthetic levels at a given display size based on its content and compositional features. In addition, we analyze the essential features that lead to the size-dependent property of image aesthetics. It is hoped that this work will motivate further research by showing that both content and presentation should be considered when evaluating an image's aesthetic appeals.