• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.267-268
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• 2007

A dynamic range compression algorithm using Markov random field (MRF) modeling to display high dynamic range (HDR) images on low dynamic range (LDR) devices is proposed in this work. The proposed algorithm separates foreground objects from the background using the edge information, and then compresses the color differences across the edges based on the MRF modeling. By minimizing a cost function using belief propagation, the proposed algorithm can provide an effective LDR image. Simulation results show that the proposed algorithm provides good results.

• Journal of Broadcast Engineering
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• v.20 no.5
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• pp.756-769
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• 2015

In this paper, we propose a scalable coding method for high dynamic range (HDR) and standard dynamic range (SDR) videos based on Scalable High Efficiency Video Coding (SHVC). The proposed method has multi-layer coding architecture that consists of base layer for SDR videos and enhancement layer for HDR videos to support the backward compatibility with legacy codec and display devices. Also, to improve coding efficiency of enhancement layers, a global inverse tone mapping is applied to the reconstructed SDR video and the compensated frames are referred for coding of the enhancement layer. The proposed method is found to achieve BD-Rate gain of 43.0% on average (maximum 76.3%) for the enhancement layer and 15.7% on average (maximum 31%) for dual-layer against the SHM 7.0 reference software.

• Journal of the Semiconductor & Display Technology
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• v.16 no.3
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• pp.82-86
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• 2017

High dynamic range (HDR) imaging offers a radically approach of representing colors in digital images. Instead of using the range of colors produced by given devices, HDR imaging method manipulates and stores all colors and brightness levels visible to the human eye. To faithfully represent, store and then reproduce all these effects, the original scene must be stored and treated using high fidelity HDR techniques. Then, tone mapping is required to accommodate HDR image to low dynamic range (LDR) devices, and tone mapping operation of HDR image for realistic display is commonly researched. However, color visualization for analyzing scene luminance in HDR imaging has less attention from researches. This paper presents and implements a method for reproduction and visualization of the false color in HDR images. We produce a color visualization framework with several mapping functions, and evaluate their effectiveness by using RMAE and SNR with commonly used HDR image data. Experiment reveals that the sigmodal mapping function shows better performance in the false color visualization, compared to other methods.

• Journal of Korea Multimedia Society
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• v.15 no.1
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• pp.9-17
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• 2012

Recently, a new image appearance model, named iCAM06, was developed for High-Dynamic-Range (HDR) image rendering. The dynamic range of a HDR image needs to be mapped on the range of output devices, which is called the tone reproduction or tone mapping. The iCAM06, the representative HDR rendering algorithm also uses the tone compression using a S-curve mapping function for image reproduction on the dynamic range of output devices. However the iCAM06 occurs white point shift during its tone compression process. Therefore, we propose a compensation method for white point shift problem using the corrected channel gain function. Experiment results show that the proposed method has better performance than the iCAM06.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.11a
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• pp.89-90
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• 2010

기존의 HDR (high dynamic range) 영상취득 기법은 한 장의 HDR 영상을 얻기 위해 여러 장의 LDR (low dynamic range) 영상을 취득하기 때문에 영상 취득에 많은 시간이 소요된다. 본 논문에서는 이런 단점을 보안하기 위해 두 장의 LDR 영상을 이용하여 평균 밝기 값에 대한 노출 곡선을 추정한다. 그리고 추정된 노출곡선을 이용하여 밝은 영상과 어두운 영상 각각의 최적의 노출 시간을 취득하는 기법을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.550-553
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• 2010

최근 영화, 광고 그리고 증강현실과 혼합현실 등 다양한 분야에서 실제 영상에 가상의 객체를 합성하는 기법이 자주 사용되고 있다. 보다 사실적인 합성 결과를 생성하기 위해서는 실제 배경영상의 광원정보를 그대로 적용해야 한다. 이러한 실 세계의 광원 정보를 이용하기 위해서는 HDR(High Dynamic Range) 영상을 생성해야 한다. 일반적으로 HDR 영상을 생성하기 위해서는 고가의 HDR 카메라를 사용하거나 LDR(Low Dynamic Range) 카메라를 사용하여 노출 시간을 달리한 일련의 LDR 영상을 촬영하여 이를 기반으로 HDR 영상을 생성해야 한다. 본 논문에서는 이러한 단점을 보완하기 위해 한 장의 LDR 환경 맵을 HDR 환경 맵으로 복원하는 방법에 대해 제안한다. 제안하는 방법을 통해 LDR 환경 맵을 HDR 환경 맵으로 복원할 수 있으며 결과에서 볼 수 있듯이 HDR 영상을 이용했을 때와 유사한 렌더링 결과를 생성할 수 있다.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.233-234
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• 2007

It is needed many images with different exposure to acquire high dynamic range (HDR) image of a scene using digital still camera. This paper proposed to acquire HDR image with small error using reduced number of image. Proposed method takes two pictures with different exposure and estimates dynamic range of scene using information two images, and takes three pictures with calculated proper exposure to acquire HDR image.

• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.714-717
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• 2018

This paper presents a method of HDR imaging with adaptive saturation compensation for brightness change. The saturation of HDR images were lighten on dark region because conventional HDR methods have focused on brightness change. Therefore, the proposed HDR method compensates saturation adaptively according to brightness change. For experiments of several images, the proposed algorithm is superior to conventional HDR methods qualitatively and quantitatively in terms of color saturation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.70-73
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• 2021

최근 영상 및 비디오 분야에 심층 신경망(DNN, Deep Neural Network)을 사용한 연구가 다양하게 진행됨에 따라 High Dynamic Range (HDR) 이미징 기술에서도 기존의 방법들 보다 우수한 성능을 보이는 심층 신경망 모델들이 등장하였다. 하지만, 심층 신경망을 사용한 방법은 큰 연산량과 많은 GPU 메모리를 사용한다는 문제점이 존재하며, 이는 심층 신경망 기반 기술들의 현실 적용 가능성에 제한이 되고 있다. 이에 본 논문에서는 제한된 연산량과 GPU 메모리 조건에서도 사용 가능한 다중 노출 HDR 경량화 심층 신경망을 제안한다. Kalantari Dataset에 대해 기존 HDR 모델들과의 성능 평가를 진행해 본 결과, PSNR-µ와 PSNR-l 수치에서 GPU 메모리 사용량 대비 우수한 성능을 보임을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1846-1863
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• 2016

High dynamic range (HDR) images can present the perfect real scene and rich color information. A commonly encountered problem in practical applications is how to well visualize HDR images on standard display devices. In this paper, we propose a multi-scale decomposition method using guided filtering for HDR image tone mapping. In our algorithm, HDR images are directly decomposed into three layers：base layer, coarse scale detail layer and fine detail layer. We propose an effective function to compress the base layer and the coarse scale detail layer. An adaptive function is also proposed for detail adjustment. Experimental results show that the proposed algorithm effectively accomplishes dynamic range compression and maintains good global contrast as well as local contrast. It also presents more image details and keeps high color saturation.