Saliency-based parameter tuning for tone mapping

We present a saliency-based parameter tuning algorithm that can optimize the parameters of tone mapping operators automatically by minimizing the saliency distortion caused by the process of tone mapping. The algorithm employs an improved saliency detection model for HDR images, and the saliency distortion is quantified as the Kullback-Leibler divergence between the saliency distributions of the tone mapped images and those of the corresponding HDR images. We show that the minimization can be accomplished by employing an evolution strategy with individuals representing parameter settings and fitness values based on saliency distortion. The effectiveness of our algorithm is demonstrated through experiments using several tone mapping operators and test images.

[1]  Patrick Le Callet,et al.  Tone mapping based HDR compression: Does it affect visual experience? , 2014, Signal Process. Image Commun..

[2]  Erik Reinhard,et al.  09 – Image-based Lighting , 2006 .

[3]  Wen-Chieh Lin,et al.  Attention-based high dynamic range imaging , 2011, The Visual Computer.

[4]  I ChristensenHenrik,et al.  Computational visual attention systems and their cognitive foundations , 2010, TAP 2010.

[5]  Zeev Farbman,et al.  Interactive local adjustment of tonal values , 2006, ACM Trans. Graph..

[6]  Marcus Barkowsky,et al.  On the perceptual similarity of realistic looking tone mapped High Dynamic Range images , 2010, 2010 IEEE International Conference on Image Processing.

[7]  Christof Koch,et al.  Modeling attention to salient proto-objects , 2006, Neural Networks.

[8]  Zhou Wang,et al.  Objective Quality Assessment of Tone-Mapped Images , 2013, IEEE Transactions on Image Processing.

[9]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[10]  Greg Ward,et al.  A Contrast-Based Scalefactor for Luminance Display , 1994, Graphics Gems.

[11]  Erik Reinhard,et al.  High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting (The Morgan Kaufmann Series in Computer Graphics) , 2005 .

[12]  Rafal Mantiuk,et al.  Display adaptive tone mapping , 2008, ACM Trans. Graph..

[13]  Erik Reinhard,et al.  High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting , 2010 .

[14]  Greg Turk,et al.  LCIS: a boundary hierarchy for detail-preserving contrast reduction , 1999, SIGGRAPH.

[15]  Christophe Schlick,et al.  Quantization Techniques for Visualization of High Dynamic Range Pictures , 1995 .

[16]  Mark D. Fairchild,et al.  Meet iCAM: A Next-Generation Color Appearance Model , 2002, Color Imaging Conference.

[17]  Dirk V. Arnold,et al.  Virtual photograph based saliency analysis of high dynamic range images , 2013, CAE '13.

[18]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[19]  S. Avidan,et al.  Seam carving for content-aware image resizing , 2007, SIGGRAPH 2007.

[20]  Hans-Peter Seidel,et al.  Dynamic range independent image quality assessment , 2008, ACM Trans. Graph..

[21]  C. Koch,et al.  A saliency-based search mechanism for overt and covert shifts of visual attention , 2000, Vision Research.

[22]  Kurt Debattista,et al.  Advanced High Dynamic Range Imaging: Theory and Practice , 2011 .

[23]  Christian Bloch,et al.  Style-based tone mapping for HDR images , 2013, SA '13.

[24]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[25]  Michael Wimmer,et al.  Image Attributes and Quality for Evaluation of Tone Mapping Operators , 2006 .

[26]  Hans-Peter Seidel,et al.  Predicting visible differences in high dynamic range images: model and its calibration , 2005, IS&T/SPIE Electronic Imaging.

[27]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[28]  Hans-Paul Schwefel,et al.  Evolution strategies – A comprehensive introduction , 2002, Natural Computing.

[29]  Ingrid Heynderickx,et al.  Visual Attention in Objective Image Quality Assessment: Based on Eye-Tracking Data , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[30]  Dirk V. Arnold,et al.  Tone mapping by interactive evolution , 2009, GECCO.

[31]  Michael Ashikhmin,et al.  A Tone Mapping Algorithm for High Contrast Images , 2002, Rendering Techniques.

[32]  Karol Myszkowski,et al.  Adaptive Logarithmic Mapping For Displaying High Contrast Scenes , 2003, Comput. Graph. Forum.

[33]  Kenneth Chiu,et al.  Spatially Nonuniform Scaling Functions for High Contrast Images , 1993 .

[34]  W. Vent,et al.  Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .

[35]  Henrik I. Christensen,et al.  Computational visual attention systems and their cognitive foundations: A survey , 2010, TAP.

[36]  Jean-Philippe Tarel,et al.  Saliency maps of high dynamic range images , 2009, APGV '09.

[37]  Michael Ashikhmin,et al.  A reality check for tone-mapping operators , 2006, TAP.

[38]  Erik Reinhard,et al.  Ieee Transactions on Visualization and Computer Graphics 1 Dynamic Range Reduction Inspired by Photoreceptor Physiology , 2022 .

[39]  Laurent Itti,et al.  Automatic foveation for video compression using a neurobiological model of visual attention , 2004, IEEE Transactions on Image Processing.

[40]  Erik Reinhard,et al.  Parameter Estimation for Photographic Tone Reproduction , 2002, J. Graphics, GPU, & Game Tools.