Size does matter: how image size affects aesthetic perception?

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.

[1]  S. D. Jong SIMPLS: an alternative approach to partial least squares regression , 1993 .

[2]  Wei Luo,et al.  Content-Based Photo Quality Assessment , 2013, IEEE Trans. Multim..

[3]  Kok-Lim Low,et al.  Saliency-enhanced image aesthetics class prediction , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[4]  Yan Ke,et al.  The Design of High-Level Features for Photo Quality Assessment , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[5]  Jingrui He,et al.  Classification of Digital Photos Taken by Photographers or Home Users , 2004, PCM.

[6]  Jun Gao,et al.  Learning to predict the perceived visual quality of photos , 2011, 2011 International Conference on Computer Vision.

[7]  Daniel Cohen-Or,et al.  Optimizing Photo Composition , 2010, Comput. Graph. Forum.

[8]  Vicente Ordonez,et al.  High level describable attributes for predicting aesthetics and interestingness , 2011, CVPR 2011.

[9]  Tsuhan Chen,et al.  > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < , 2022 .

[10]  James Ze Wang,et al.  ACQUINE: aesthetic quality inference engine - real-time automatic rating of photo aesthetics , 2010, MIR '10.

[11]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  W. Chu Studying Aesthetics in Photographic Images Using a Computational Approach , 2013 .

[13]  Rongrong Ji,et al.  Photo assessment based on computational visual attention model , 2009, ACM Multimedia.

[14]  Chin-Laung Lei,et al.  A crowdsourceable QoE evaluation framework for multimedia content , 2009, ACM Multimedia.

[15]  Andrew Zisserman,et al.  Representing shape with a spatial pyramid kernel , 2007, CIVR '07.