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http://dx.doi.org/10.5909/JBE.2017.22.4.496

Hybrid Tone Mapping Technique Considering Contrast and Texture Area Information for HDR Image Restoration  

Kang, Ju-Mi (Department of Intelligent Robotics, Hanyang University)
Park, Dae-Jun (Department of Intelligent Robotics, Hanyang University)
Jeong, Jechang (Department of Intelligent Robotics, Hanyang University)
Publication Information
Journal of Broadcast Engineering / v.22, no.4, 2017 , pp. 496-508 More about this Journal
Abstract
In this paper, we propose a Tone Mapping Operator (TMO) that preserves global contrast and precisely preserves boundary information. In order to reconstruct a High Dynamic Range (HDR) image to a Low Dynamic Range (LDR) display by using Threshold value vs. Intensity value (TVI) based on Human Visual System (HVS) and contrast value. As a result, the global contrast of the image can be preserved. In addition, by combining the boundary information detected using Guided Image Filtering (GIF) and the detected boundary information using the spatial masking of the Just Noticeable Difference (JND) model, And improved the perceived image quality of the output image. The conventional TMOs are classified into Global Tone Mapping (GTM) and Local Tone Mapping (LTM). GTM preserves global contrast, has the advantages of simple implementation and fast execution time, but it has a disadvantage in that the boundary information of the image is lost and the regional contrast is not preserved. On the other hand, the LTM preserves the local contrast and boundary information of the image well, but some areas are expressed unnatural like the occurrence of the halo artifact phenomenon in the boundary region, and the calculation complexity is higher than that of GTM. In this paper, we propose TMO which preserves global contrast and combines the merits of GTM and LTM to preserve boundary information of images. Experimental results show that the proposed tone mapping technique has superior performance in terms of cognitive quality.
Keywords
High Dynamic Range (HDR); Human Visual System (HVS); Contrast; Threshold vs. Intensity (TVI); Just Noticeable Difference (JND); Guided Image Filtering (GIF);
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Times Cited By KSCI : 2  (Citation Analysis)
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