• The Journal of Natural Sciences
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• v.5 no.2
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• pp.33-38
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• 1992

A new texture segmentation-based image coding technique which performs segmentation based on roughness of textural regions and properties of the human visual system (HVS) is presented for multime-dia teleconference. The segmentation is accomplished by thresholding the fractal dimension so that textural regions are classified into three texture classes; perceived constant intensity, smooth texture, and rough texture. An image coding system with high compression and good image quality is achieved by developing an efficient coding technique for each segment boundary and each texture class. We compare the coding efficiency of this technique with that of a well established technique (discrete cosine transform (DCT) image coding).

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.21-26
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• 2004

Color image quantization is a process of selecting a set of colors to display an image with some representative colors without noticeable perceived difference. It is very important in many applications to display a true color image in a low cost color monitor or printer. The basic problem is how to display 256 colors or less colors, called color palette, In this paper, we propose improved binary tree vector quantization based on spatial sensitivity which is one of the human visual properties. We combine the weights based on the responsibility of human visual system according to changes of three Primary colors in blocks of images with the process of splitting nodes using eigenvector in binary tree vector quantization. The test results show that the proposed method generates the quantized images with fine color and performs better than the conventional method in terms of clustering the similar regions. Also the proposed method can get the better result in subjective quality test and WSNR.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.11
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• pp.2264-2275
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• 1994

In this paper, image compression based on neural networks is presented with block classification and coding. Multilayer neural networks with error back-propagation learning algorithm are used to transform the normalized image date into the compressed hidden values by reducing spatial redundancies. Image compression can basically be achieved with smaller number of hidden neurons than the numbers of input and output neurons. Additionally, the image blocks can be grouped for adaptive compression rates depending on the characteristics of the complexity of the blocks in accordance with the sensitivity of the human visual system(HVS). The quantized output of the hidden neuron can also be entropy coded for an efficient transmission. In computer simulation, this approach lie in the good performances even with images outside the training set and about 25:1 compression rate was achieved using the entropy coding without much degradation of the reconstructed images.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.448-463
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• 2017

Multimedia is a ubiquitous and indispensable part of our daily life and learning such as audio, image, and video. Objective and subjective quality evaluations play an important role in various multimedia applications. Blind image quality assessment (BIQA) is used to indicate the perceptual quality of a distorted image, while its reference image is not considered and used. Blur is one of the common image distortions. In this paper, we propose a novel BIQA index for Gaussian blur distortion based on the fact that images with different blur degree will have different changes through the same blur. We describe this discrimination from three aspects: color, edge, and structure. For color, we adopt color histogram; for edge, we use edge intensity map, and saliency map is used as the weighting function to be consistent with human visual system (HVS); for structure, we use structure tensor and structural similarity (SSIM) index. Numerous experiments based on four benchmark databases show that our proposed index is highly consistent with the subjective quality assessment.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1228-1231
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• 2002

Digital image watermarking scheme using adaptive block division is proposed. To increase the perceptual invisibility, the image is divided into blocks by local properties and the human visual system (HVS), then the significant blocks are selected in the divided blocks. The significant coefficient is determined by Weber's law in these blocks. To increase the robustness, low frequency domains of the discrete cosine transform (DCT) and the discrete wavelet transform (DWT) are used. The watermark is embedded into the selected significant blocks of the DCT's and DWT's low frequency domains with adaptive watermark strengths. The watermark strength is determined by the variance and the local properties of the significant block. The experimental results prove that the proposed scheme has a good robustness against several image processing operations (e.g. median filtering, cropping, scaling, JPEG, JPEG2000, etc.) without significant degradation of the watermarked image.

• Journal of Advanced Navigation Technology
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• v.10 no.4
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• pp.364-370
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• 2006

In this paper, the decision method of optimal sub-band which is supposed to embedded watermark incorporating fuzzy inference into transform-based watermarking is proposed. After performing the DCT, maximum variation of human visual properties, such as text degree, contrast sensitivity function is calculated, and by using these, membership function is generated. After embedding the watermark to the selected bands obtained from fuzzy inference, performance of imperceptibility and robustness are evaluated. In order to testify the proposed scheme, such attacks as JPEG, filtering, cropping are utilized. and in addition, by using an AWGN channel of OFDM/QPSK system, PSNR as well as correlation are calculated, and finally evaluated the performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.743-746
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• 2005

본 논문에서는 디지털 비디오 인코딩 과정의 VLC(variable length coding) 영역에서 블록계층의 DC/AC 성분을 이용한 인증과 서명의 이중 비디오 워터마킹 시스템을 제안하였다. 제안한 기법은 블록계층의 DC 성분과 AC 성분에서 HVS(human visual system)의 특성을 고려한 것이다. 인증 워터마킹은 주요한 정보를 포함하는 저주파 영역과 윤곽선 정보를 포함하는 중간 주파수 영역을 이용하여 인트라 프레임의 DC 성분과 움직임 벡터의 부호를 변형시켰고, 서명 워터마킹은 모든 프레임의 AC 성분들 중에서 마지막 AC 성분의 Level이 '1'인 경우에만 워터마크를 삽입하였다. 서명 워터 마크 검출은 저작권자의 비밀 키에 의해서만 가능하고, 기술적인 면에서 저자권자의 판별 기준이 될 수 있다. 제안한 이중 비디오 워터마킹 시스템의 특징은 인증과 서명의 두 가지 기능을 선택적으로 수행할 수 있으며, 계산과정이 복잡하지 않으면서 비트 스트림(bit-stream)을 유지시킨다. 그리고 실험 결과에서 기존의 방법보다 화질 면에서 $2{\sim}3dB$ 더 높은 수치를 얻어 우수함을 보였고, 인코딩 수행 속도에 미치는 영향은 거의 없었으며, 향후 실시간 인코딩 처리에 응용될 수 있다.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.104-111
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• 2010

This paper presents an adaptive image enhancement method using the spatial frequency property in the DCT(discrete cosine transform) compressed domain. The dc coefficients, the illumination components of image, are adjusted to compress the dynamic range of image, and the ac coefficients are modified to enhance the contrast by using the human visual system(HVS) and the spatial frequency property. The ac coefficients are separated into vertical direction, horizontal direction, and mixed spatial frequency components, and adaptively modified to minimize the block artifacts that possibly occur in the image enhancement. The proposed method using dynamic range compression and adaptive contrast enhancement shows the advanced performance without the block artifact compared with existing method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.438-455
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• 2024

Recently, many unsupervised learning-based models have emerged for Just Noticeable Difference (JND) estimation, demonstrating remarkable improvements in accuracy. However, these models suffer from a significant drawback is that their heavy reliance on handcrafted priors for guidance. This restricts the information for estimating JND simply extracted from regions that are highly related to handcrafted priors, while information from the rest of the regions is disregarded, thus limiting the accuracy of JND estimation. To address such issue, on the one hand, we extract the information for estimating JND in an Adversarial Complementary Learning (ACoL) way and propose an ACoL-JND network to estimate the JND by comprehensively considering the handcrafted priors-related regions and non-related regions. On the other hand, to make the handcrafted priors richer, we take two additional priors that are highly related to JND modeling into account, i.e., Patterned Masking (PM) and Contrast Masking (CM). Experimental results demonstrate that our proposed model outperforms the existing JND models and achieves state-of-the-art performance in both subjective viewing tests and objective metrics assessments.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.421-428
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• 2005

A new method of Semi-fragile image watermarking which ensures the integrity of the contents of digital image is presented. Proposed watermarking scheme embeds watermark in the form of quantization noise on the wavelet transform coefficients in a specific mid frequency subbands selected from a wavelet packet decomposition based on energy distribution of wavelet transform coefficients. By controlling the strength of embedded watermark using HVS (Human Visual System) characteristic, it is imperceptible by a human viewer while robust against non-malicious attack such as compression for storage and/or transmission. When an attack is applied on the original image, it is highly probable that wavelet transform coefficients not only at the exact attack positions but also the neighboring ones are modified. Therefore, proposed authentication method utilizes whether both current coefficient and its neighbors are damaged. together. So it can efficiently detect and accurately localize attacks inflicted on the content of original image. Decision threshold for authentication can be user controlled for different application areas as needed.