The visible differences predictor: an algorithm for the assessment of image fidelity
Digital images and human vision
Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Putting social sciences together again: an introduction to the volume
Dynamics in human and primate societies
Photographic tone reproduction for digital images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
A Visibility Matching Tone Reproduction Operator for High Dynamic Range Scenes
IEEE Transactions on Visualization and Computer Graphics
Tone Reproduction for Realistic Images
IEEE Computer Graphics and Applications
Perceptually Optimized 3D Graphics
IEEE Computer Graphics and Applications
Dynamic Range Reduction Inspired by Photoreceptor Physiology
IEEE Transactions on Visualization and Computer Graphics
ACM SIGGRAPH 2005 Papers
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting (The Morgan Kaufmann Series in Computer Graphics)
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
ACM SIGGRAPH 2008 papers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic foveation for video compression using a neurobiological model of visual attention
IEEE Transactions on Image Processing
A cognitive approach for effective coding and transmission of 3D video
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
Virtual photograph based saliency analysis of high dynamic range images
Proceedings of the Symposium on Computational Aesthetics
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A number of computational models of visual attention have been proposed based on the concept of saliency map. Some of them have been validated as predictors of the visual scan-path of observers looking at images and videos, using oculometric data. They are widely used for Computer Graphics applications, mainly for image rendering, in order to avoid spending too much computing time on non salient areas, and in video coding, in order to keep a better image quality in salient areas. However, these algorithms were not used so far with High Dynamic Range (HDR) inputs. In this paper, we show that in the case of HDR images, the predictions using algorithms based on Itti, Koch and Niebur [1] are less accurate than with 8-bit images. To improve the saliency computation for HDR inputs, we propose a new algorithm derived from Itti and Koch [3]. From an eye tracking experiment with a HDR scene, we show that this algorithm leads to good results for the saliency map computation, with a better fit between the saliency map and the ocular fixation map than Itti, Koch and Niebur's algorithm. These results may impact image retargeting issues, for the display of HDR images on both LDR and HDR display devices.