Dynamic range compression by differential zone mapping based on psychophysical experiments

Abstract

In this paper we present a new technique for the display of High Dynamic Range (HDR) images on Low Dynamic Range (LDR) displays. The described process has three stages. First, the input image is segmented into luminance zones. Second, the tone mapping operator (TMO) that performs better in each zone is automatically selected. Finally, the resulting tone mapping (TM) outputs for each zone are merged, generating the final LDR output image. To establish the TMO that performs better in each luminance zone we conducted a preliminary psychophysical experiment using a set of HDR images and six different TMOs. We validated our composite technique on several (new) HDR images and conducted a further psychophysical experiment, using an HDR display as reference, that establishes the advantages of our hybrid three-stage approach over a traditional individual TMO.

High Dynamic Range Imaging by Perceptual Logarithmic Exposure Merging

“…Our method was penalized when compared to other TMOs due to their general adaptation being closer to the standard contrast sensitivity function (CSF) (Barten, 1999). Yet we stress that some TMOs (Krawczyk et al, 2005;Banterle et al, 2012) work only for calibrated images in specific scene luminance domain.…”

“…The best performing version of the proposed method was for μ = 0.37. The proposed method, when compared with various TMOs, ranked first, according to the overall quality and statistical naturalness, and it ranked fifth according to structural fidelity (after Banterle et al, 2012;Reinhard et al, 2005;Drago et al, 2003;Durand and Dorsey, 2002). Our method was penalized when compared to other TMOs due to their general adaptation being closer to the standard contrast sensitivity function (CSF) (Barten, 1999).…”

“…Krawczyk et al (2005) implemented the Gestalt based anchoring theory of Gilchrist et al (1999) to divide the image in frameworks and performed range compression by ensuring that frameworks are well-preserved. Banterle et al (2012) segmented the image into luminance components and applied independently the TMOs introduced by Drago et al (2003) and Reinhard et al (2005) for further adaptive fusion based on previously found areas. Ferradans et al (2012) proposed an elaborated model of the global HVS response and pursued local adaptation with an iterative variational algorithm.…”

“…With bold letters we marked the best result according to each category, while with italic the second one. et al (1997) 66.9 14.38 72.7 Fattal et al (2002) 59.9 6.4 61.0 Durand and Dorsey (2002) 81.7 41.0 85.4 Drago et al (2003) 82.3 50.2 87.0 Reinhard et al (2005) 83.1 50.5 87.5 Krawczyk et al (2005) 71.7 36.8 76.6 Banterle et al (2012) 83.7 52.1 87.8 Mertens et al (2007) 81.7 64.2 89.4 Zhang and Cham (2012) 77 online. 3 The implemented algorithms were optimized by the Toolbox creators to match with the initial article reported results and for better performance; hence, we used the implicit values for the algorithm parameters.…”

“…For comparative results we considered exposure fusion in the variant modified according to Mertens et al (2007) as well as Zhang and Cham (2012) using the author released code and the TMOs applied on the .hdr images described by Ward et al (1997), Fattal et al (2002, Durand and Dorsey (2002), Drago et al (2003), Reinhard et al (2005), Krawczyk et al (2005) and Banterle et al (2012) as they are the foremost such methods. The code for the TMOs is taken from the Matlab HDR Toolbox (Banterle et al, 2011) and is available Table 3.…”