• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.137-140
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• 2007

A prediction model for the sound quality of household refrigerator noise was proposed by investigating subjective and objective attributes of the noise [Jeon et al. (2007) Appl. Acoust.]. In the present study, the just noticeable difference (JND) of each sound quality metric - Zwicker's loudness, sharpness, roughness and fluctuation strength - which constitute the prediction model was investigated. Loudness of recorded sound samples from five refrigerators were varied according to constant intervals in sound pressure levels. Sharpness was also changed at 14-16 barks. Auditory experiments were conducted to discriminate the JNDs of loudness and sharpness by method of limit. The results indicated that JNDs of loudness and sharpness were 0.50 sone and 0.08 acum, respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.99-100
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• 2015

Digital image blur occurs due to various environmental conditions at the time of shooting. Blur produces the low-frequency component in the image. This problem worsens the quality of the digital image. To address this issue, contrast improvement methods has been widely studied. 2D filter to enhance the edges is a simple structure with a fast processing speed. However, the sensitivity of the human visual system is different depending on the surrounding brightness locally. Thus, in this paper, we proposed feature-based contrast enhancement method for the digital image using 2D filter to enhance the edges and JND(Just Noticeable Difference) according to the surrounding brightness. We confirmed the result image of proposed method and identified that the contrast is improved.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1807-1810
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• 2003

Steganography based on Just Noticeable Difference(JND) has been used for natural images. However, it has been recognized to have defects for the non-natural images such as scanned text images, cartoons, etc. In this paper, an alternative method is proposed to improve this problem. A new scheme is designed specially for the non-natural images. Instead of JND, Noise Visibility Function(NVF) is used. NVF value and edge strength value of each pixel ate combined to decide the embedding data capacity and the visibility of data embedded images have been improved specially for the non-natural images.

• Journal of Broadcast Engineering
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• v.21 no.6
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• pp.929-943
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• 2016

This paper proposes the rate control algorithm based on the S-JND (Saliency-Just Noticeable Difference) model for considering perceptual visual quality. The proposed rate control algorithm employs the S-JND model to simultaneously reflect human visual sensitivity and human visual attention for considering characteristics of human visual system. During allocating bits for CTU (Coding Tree Unit) level in a rate control, the bit allocation model calculates the S-JND threshold of each CTU in a picture. The threshold of each CTU is used for adaptively allocating a proper number of bits; thus, the proposed bit allocation model can improve perceptual visual quality. For performance evaluation of the proposed algorithm, the proposed algorithm was implemented on HM 16.9 and tested for sequences in Class B and Class C under the CTC (Common Test Condition) RA (Random Access), Low-delay B and Low-delay P case. Experimental results show that the proposed method reduces the bit-rate of 2.3%, and improves BD-PSNR of 0.07dB and bit-rate accuracy of 0.06% on average. We achieved MOS improvement of 0.03 with the proposed method, compared with the conventional method based on DSCQS (Double Stimulus Continuous Quality Scale).

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.213-216
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• 2018

본 논문에서는 사람의 인지 시각 특성 중 하나인 JND(Just Noticeable Difference)를 이용한 인지 비디오 부호화 기법을 제안한다. JND 기반 인지 부호화 방법은 사람의 인지 시각 특성을 이용해 시각적으로 인지가 잘 되지 않는 인지 신호를 제거함으로 부호화 효율을 높이는 방법이다. 제안된 방법은 기존 수학적 모델 기반의 JND 기법이 아닌 최근 각광 받고 있는 데이터 중심(data-driven) 모델링 방법인 심층 신경망 기반 JND 모델 생성 기법을 제안한다. 제안된 심층 신경망 기반 JND 모델은 비디오 부호화 과정에서 입력 영상에 대한 전처리를 통해 입력 영상의 인지 중복(perceptual redundancy)를 제거하는 역할을 수행한다. 부호화 실험에서 제안된 방법은 동일하거나 유사한 인지화질을 유지한 상태에서 평균 16.86 %의 부호화 비트를 감소 시켰다.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05d
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• pp.855-860
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• 2002

본 논문에서는 화질의 열화를 최소화함과 동시에 강인한 워터마크의 은닉을 위하여 멀티 웨이브릿 변환영역에서 인간 시각 특성(human visual system: HVS)을 고려한 적응 워터마크 은닉 기술을 제안한다. 멀티웨이브릿은 영상을 완전 복원시키는데 탁월한 효과가 있으며 경계영역에서 우수한 성능을 갖는다. 본 논문에서 사용한 멀티웨이브릿 함수는 근사화 차수가 2인 DHGM 멀티웨이브릿을 사용한다. 워터마크는 멀티웨이브릿 분해된 부대역들 중 저주파 영역을 제외한 중간 및 고주파 영역의 웨이브릿 계수 중에 HVS를 이용하여 얻은 문턱값에 의해 선택된 웨이브릿 계수들에만 은닉한다. 워터마크를 은닉하기 위한 지각모델은 영상 압축시 지각적 양자화기에 사용되는 정적 JND(just noticeable difference) 모델을 이용한다. 제안한 은닉모델은 영상의 특성에 따라 은닉하는 워터마크의 수가 각각 달라졌다. 또한, 여러 실험결과 우수한 비가시성과 강인성을 확인하였다.

• Journal of Broadcast Engineering
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• v.3 no.2
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• pp.155-163
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• 1998

본 논문에서는 DCT를 기반으로 영상의 지역적인 특성에 따라 워터마크를 삽입하는 위치와 워터마크의 크기를 조절하며 영상에 워터마크를 삽입하는 기법을 소개하고자 한다. 영상의 속성에 따라 워터마크의 크기를 조정할 수 있는 JND(Just Noticeable Difference)와 워터마크가 삽입될 위치를 정하는 기준이 되는 Variance를 사용함으로써 워터마크를 영상에 삽입하였을 때, 원래 영상의 질(Qualtiy)을 저하시키지 않고도 영상변형에 강인하도록 한다. 또한, 전체 영상의 JND특성을 함께 고려해 줌으로써 영상에 삽입할 수 있는 워터마크의 크기를 지역적인 영상의 JND를 통해서 구해진 한계치보다 크게 조정하여 워터마크의 강인성을 증가시킨다. 실험 결과에 의하면 본 알고리즘은 워터마크가 삽입된 영상을 필터링(blurring, sharpening) 처리하거나 압축, 또는 노이즈를 첨가하는 등의 영상변형을 가안 후에도 워터마크를 검출함으로써 영상변형에 강인한 알고리즘을 확인할 수 있다.

• Journal of Broadcast Engineering
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• v.22 no.4
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• pp.496-508
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• 2017

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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.16-22
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• 2009

In this paper, we propose an auto gain/offset which considers the visibility of human visual system (HVS) and the histogram of a target image jointly. In the proposed method, the lower and upper clipping thresholds are determined to maximize the averaged visibility of the contrast-stretched image. The target image is then contrast-stitched by the gain and offset derived from the clipping thresholds. We define the visibility as a quantity related to the spatial JND, which means the threshold below which any change of a pixel from its textured neighbors is not recognized by the HVS. Experimental results show that the contrast-stretched images by the proposed method have better global and local contrasts compared to the results by some conventional methods.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11a
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• pp.104-107
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• 2003

본 논문에서는 웨이브릿 영역에서 HVS 및 NVF 함수를 사용하여 영상특성에 기반한 통계적 판정법을 이용한 적응 워터마크 검출 알고리즘을 판정법을 제안한다. 워터마크는 4레벨로 분해된 웨이브릿 영역에서 JND(just noticeable difference) 특성과 NVF(noise visibility function)를 이용한 통계적 특성을 기반으로 정상상태 가우시안 모델에 따라 지각적 동조 특성을 이용하여 적응적으로 삽입하고, Bayes 이론 및 Neyman-Pearson 정리를 이용한 통계적 판정법을 이용하여 워터마크를 추출함으로써 기존의 통계적 판정법 보다 정확하게 워터마크 존재 유무를 판정 할 수 있음을 확인하였다.