• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.22-30
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• 2024

Image compression-decompression methods have become increasingly crucial in modern times, facilitating the transfer of high-quality images while minimizing file size and internet traffic. Historically, early image compression relied on rudimentary codecs, aiming to compress and decompress data with minimal loss of image quality. Recently, a novel compression framework leveraging colorization techniques has emerged. These methods, originally developed for infusing grayscale images with color, have found application in image compression, leading to colorization-based coding. Within this framework, the encoder plays a crucial role in automatically extracting representative pixels-referred to as color seeds-and transmitting them to the decoder. The decoder, utilizing colorization methods, reconstructs color information for the remaining pixels based on the transmitted data. In this paper, we propose a novel approach to image compression, wherein we decompose the compression task into grayscale image compression and colorization tasks. Unlike conventional colorization-based coding, our method focuses on the colorization process rather than the extraction of color seeds. Moreover, we employ the Denoising Diffusion Null-Space Model (DDNM) for colorization, ensuring high-quality color restoration and contributing to superior compression rates. Experimental results demonstrate that our method achieves higher-quality decompressed images compared to standard JPEG and JPEG2000 compression schemes, particularly in high compression rate scenarios.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.8
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• pp.1356-1362
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• 2008

In this paper. we propose a novel auto white balance algorithm which is one of the representative functions on cameras. White balance is the process of removing unrealistic color casts, which will make the captured white objects appear white. For white balance, we employ $CIEL^*a^*b^*$ color space which is the most complete color model available and is conventionally used to describe all the colors visible to the human eye and estimate the color difference on white objects with distribution of the image which is called the reference white estimation. For accuracy, we form groups or sets of pixels that are altered by the light sources and other elements. Moreover, Standard group is decided by judgment of specific-case images with the information of groups. Then, the reference white estimation is performed by the color sampling which is to choose all the accumulated pixels contained within the standard group. The color gain for image compensation by considering the color saturation is also computed. the proposed algorithm provides a significant performance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.1-11
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• 2021

This paper presents a procedure to extract lane line models using a set of proposed methods. Firstly, an image warping method based on homography is proposed to transform a target image into an image which is efficient to find lane pixels within a certain region in the image. Secondly, a method to use the combination of the results of edge detection and HSL (Hue, Saturation, and Lightness) transform is proposed to detect lane candidate pixels with reliability. Thirdly, erroneous candidate lane pixels are eliminated using a selection area method. Fourthly, a method to fit lane pixels to quadratic polynomials is proposed. In order to test the validity of the proposed procedure, a set of black-box images captured under varying illumination and noise conditions were used. The experimental results show that the proposed procedure could overcome the problems of color-only and edge-only based methods and extract lane pixels and model the lane line geometry effectively within less than 0.6 seconds per frame under a low-cost computing environment.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.218-221
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• 2000

In this paper, we propose a new algorithm to detect human faces for controling a camera used in video conference. We model the distribution of skin color and set up the standard skin color in YIQ color space. An input video frame image is segmented into skin and non-skin segments by comparing the standard skin color and each pixels in the input video frame. Then, shape filler is applied to select face segments from skin segments. Our algorithm detects human faces in real time to control a camera to capture a human face with a proper size and position.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.5
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• pp.37-44
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• 2011

This paper proposes a confusion-line separating algorithm in a CIE Lab color space using color segmentation for protanopia and deuteranopia. Images are segmented into regions by grouping adjacent pixels with similar color information using the hue components of the images. To this end, the region growing method and the seed points used in this method are the pixels that correspond to peak points in hue histograms that went through a low pass filter. In order to establish a color vision deficiency (CVD) confusion line map, we established 512 virtual boxes in an RGB 3-D space so that boxes existing on the same confusion line can be easily identified. After that, we checked if segmented regions existed on the same confusion line and then performed color adjustment in an CIE Lab color space so that all adjacent regions exist on different confusion lines in order to provide the best color identification effect to people with CVDs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1100-1105
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• 2006

Severe distortions of colors in the obtained images have made it difficult for color codes to expand their applications. To reduce the effect of color distortions on reading colors, it will be more desirable to statistically process as many pixels in the individual color region as possible, than relying on some regularly sampled pixels. This process may require segmentation, which usually requires edge detection. However, edges in color codes can be disconnected due tovarious distortions such as zipper effect and reflection, to name a few, making segmentation incomplete. Edge linking is also a difficult process. In this paper, a more efficient approach to reducing the effect of color distortions on reading colors, one that excludes precise edge detection for segmentation, was obtained by employing the k-means clustering algorithm. And, in detecting color codes, the properties of both six safe colors and grays were utilized. Experiments were conducted on 144, 4M-pixel, outdoor images. The proposed method resulted in a color-code detection rate of 100% fur the test images, and an average color-reading accuracy of over 99% for the detected codes, while the highest accuracy that could be achieved with an approach employing Canny edge detection was 91.28%.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.889-894
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• 2009

Accompanied by the rapid development of Computer Vision, Visual surveillance has achieved great evolution with more and more complicated processing. However there are still many problems to be resolved for robust and reliable visual surveillance, and the cast shadow occurring in motion detection process is one of them. Shadow pixels are often misclassified as object pixels so that they cause errors in localization, segmentation, tracking and classification of objects. This paper proposes a novel cast shadow removal method. As opposed to previous conventional methods, which considers pixel properties like intensity properties, color distortion, HSV color system, and etc., the proposed method utilizes observations about edge patterns in the shadow region in the current frame and the corresponding region in the background scene, and applies Laplacian edge detector to the blob regions in the current frame and the background scene. Then, the product of the outcomes of application determines whether the blob pixels in the foreground mask comes from object blob regions or shadow regions. The proposed method is simple but turns out practically very effective for Gaussian Mixture Model, which is verified through experiments.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.1
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• pp.33-42
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• 2005

Identifying color codes needs precise color information of their constituents, and is far from trivial because colors usually suffer severe distortions throughout the entire procedures from printing to acquiring image data. To accomplish accurate identification of colors, we need a reliable segmentation method to separate different color regions from each other, which would enable us to process the whole pixels in the region of a color statistically, instead of a subset of pixels in the region. Color image segmentation can be accomplished by performing edge detection on component image(s). In this paper, we separately detected edges on component images from RGB, HSI, and YIQ color models, and performed mathematical analyses and experiments to find out a pair of component images that provided the best edge image when combined. The best result was obtained by combining Y- and R-component edge images.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.822-824
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• 2018

Estimation of 3-D objects from 2-D images is inherently performed by either motion or scene features methods as it has been described in different literatures. Structure from motion as a method employed in this study uses calibrated camera and reconstructed 3-D points from the structure of the scene for reliable and precise estimates. In this study we construct 3-D shapes using color pixels and reconstructed 3-D points to determine observable differences for the constructed 3-D images. The estimation using reconstructed 3-D points indicates that the sphere is recovered by the use of scale factor due to its known size while the one obtained by using color pixels has look similar to the former but different in the scales of the axes.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.08a
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• pp.278-278
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• 2010

Electrically tunable photonic band gap (PBG) materials based on crystalline structures have been developed for active components of display. Despite considerable advances, the intrinsic drawbacks of the crystalline PBG materials such as the strong angle dependent hue and difficulty of fabricating defect-free structures in large area have yet to be addressed for their practical applications. Here we report quasi-amorphous colloidal structures exhibiting angle-independent photonic colors in response to the electric stimuli. Moderately polydisperse colloidal Fe3O4@SiO2 nanoparticles dispersed in organic solvents exclusively form quasi-amorphous photonic materials at sufficiently high concentrations (> 30 wt%), and which reversibly reflect incident light in visible region ($\lambda$ peak = 490~655 nm) in response to the relatively low bias voltage (0~4 V). We show the angle-independent tunable photonic colors with the fast response time (50~170 ms) due to the isotropic nature of quasi-amorphous structures. Conventional vacuum injection technique is applicable for fabricating flexible full color photonic display pixels with various pre-defined shapes.