• Journal of information and communication convergence engineering
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• v.6 no.1
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• pp.29-33
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• 2008

This paper presents the proposal and hardware design of JPEG baseline encoder. The JPEG encoder system consists of line buffer, 2-D DCT, quantization, entropy encoding, and packer. A fully pipelined scheme for JPEG encoder is adopted to speed-up an image compression. The proposed architecture was described in VHDL and synthesized in Xilinx ISE 7.1i and simulated by modelsim 6.1i. The results showed that the performance of the designed JPEG baseline encoder is higher than that demanded by real-time applications for $1024{\times}768$ image size. The designed JPEG encoder IP can be easily integrated into various application systems, such as scanner, PC camera, color FAX, and network camera, etc.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.11a
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• pp.266-270
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• 2000

내용기반 영상검색시스템에서 객체 단위로 영상을 검색하기 위해서는 영상에서 의미있는 객체를 추출하는 과정이 필수적이며, 이를 위해 영역 분할을 효율적으로 수행하기 위한 양자화가 선행되어야 한다. 일반적인 칼라 양자화 기법은 칼라 수를 줄이되 양자화 된 영상이 원시 영상과 가능할 비슷해 보이도록 하는 것을 목적으로 하지만, 영역 분할을 위한 칼라 양자화에서는 칼라의 표현보나는 의미있는 객체를 용이하게 추출할 수 있도록 양자화 하는 것을 목적으로 한다. 본 논문에서는 기존의 Octree 양자화 방법과 K-means 알고리즘의 장점을 조합하여 영역 분할에 용이한 양자화 결과를 얻을 수 있는 방법을 제안한다. 먼저, Octree 양자화 방법을 수행하여 얻어진 양자화 된 칼라들 중에서 시각적으로 유사한 칼라를 병합함으로써, Octree 양자화 방법의 단점인 강제 분할 문제점을 해결한다. 이어서, 병합 후의 양자화 된 칼라에 대해서만 K-means 알고리즘을 수행함으로써, 보다 빠른 시간 내에 영역 분할에 적합한 양자화 된 영상을 얻는다. 실험을 통해 제안한 방법의 효용성을 확인하였다.

• Annual Conference of KIPS
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• 2000.10a
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• pp.255-258
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• 2000

정보 산업의 발달에 의해 여러 가지 형태의 정보 전달이 가능하게 되었으며 최근에 칼라 화상의 정보 전달에 있어서 빠른 전송과 압축기법이 필요하게 되었다. 본 논문에서는 칼라 화상을 압축하는데 있어 개선된 SOM 알고리즘을 이용하여 칼라 이미지에 대한 벡터 양자화 기법을 제안한다. 제안된 방법은 기존의 LBG 알고리즘을 이용한 벡터 양자화 기법에 비해 블록화 현상을 줄일 수 있었으며 이미지 전체에 대해 블록의 수만큼 계속해서 반복하지 않고 동적으로 코드북을 생성시킴으로써 실행 시간도 줄일 수 있었다. 또한 웨이블릿을 칼라 화상에 적용시켜 화상의 특징을 더욱더 두드러지게 함으로써 개선된 SOM을 적용시 재생의 효과를 높일 수 있었다.

• ETRI Journal
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• v.34 no.3
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• pp.429-438
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• 2012

This study presents two reversible data hiding schemes based on the coefficient shifting (CS) algorithm. The first scheme uses the CS algorithm with a mean predictor in the spatial domain to provide a large payload while minimizing distortion. To guard against manipulations, the second scheme uses a robust version of the CS algorithm with feature embedding implemented in the integer wavelet transform domain. Simulations demonstrate that both the payload and peak signal-to-noise ratio generated by the CS algorithm with a mean predictor are better than those generated by existing techniques. In addition, the marked images generated by the variant of the CS algorithm are robust to various manipulations created by JPEG2000 compression, JPEG compression, noise additions, (edge) sharpening, low-pass filtering, bit truncation, brightness, contrast, (color) quantization, winding, zigzag and poster edge distortion, and inversion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3186-3203
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• 2021

In this paper, a multi-scale local difference directional number (MLDDN) pattern is proposed for pig identification. Firstly, the color images of individual pig are converted into grey images by the most significant bits (MSB) quantization, which makes the grey values have better discrimination. Then, Gabor amplitude and phase responses on different scales are obtained by convoluting the grey images with Gabor masks. Next, by calculating the main difference of local edge directions instead of traditionally edge information, the directional numbers of Gabor amplitude and phase responses are encoded. Finally, the block histograms of the encoded images are concatenated on each scale, and the maximum pooling is adopted on different scales to avoid the high feature dimension. Experimental results on two pigsties show that MLDDN impressively outperforms the other widely used local descriptors.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.772-774
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• 2008

A navigation system for virtual environments using low-quality HMD(head mounted display)must quantize images when the system presents true-color image with restricted number of colors. Such navigation system quantizes an image by using fixed palette. If the system represents an image by using a variable palette which is made considering a region around the viewpoint then user can perceive a virtual environments more vividly because human visual system is sensitive to the colors variation in the region around the viewpoint. In this paper we propose a color quantization algorithm that quantize a region around the viewpoint more finely than other regions at each variation of viewpoint for virtual environments navigation system and compose virtual environments navigation system using proposed algorithm. The system quantizes an image at each variation of viewpoint and shows a quantized image to user through HMD. We tested user preferences for our proposed system and the results show that users preferred our system.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.91-97
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• 2013

Image and it's processing techniques are widely applied and very important in the recent IT environment. In this paper, we try to reconstruct original BMP(Bitmap) image into indexed image and codebook utilizing vector quantization and represent high quality image only with same pixel depth of previous indexed image like JPEG etc. That is, By adopting common map method onto index image with $2^n$ color codebook, image can be represented as high quality as $2^{n+1}$ color codebook. When proposed output image is compared with original BMP image, it provides as much around 2dB as higher PSNR than conventional 8 bit index image(normal JPEG). Furthermore, this improvement(2 dB higher PSNR) could be provided when using the 9 bit indexed image.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.847-852
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• 2010

Text in natural images has a various and important feature of image. Therefore, to detect text and extraction of text, recognizing it is a studied as an important research area. Lately, many applications of various fields is being developed based on mobile phone camera technology. Detecting edge component form gray-scale image and detect an boundary of text regions by local standard deviation and get an connected components using Euclidean distance of RGB color space. Labeling the detected edges and connected component and get bounding boxes each regions. Candidate of text achieved with heuristic rule of text. Detected candidate text regions was merged for generation for one candidate text region, then text region detected with verifying candidate text region using ectilarity characterization of adjacency and ectilarity between candidate text regions. Experctental results, We improved text region detection rate using completentary of edge and color connected component.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.49-59
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• 2009

Contents-based image retrieval methods are in general more objective and effective than text-based image retrieval algorithms since they use color and texture in search and avoid annotating all images for search. SIM(Self-organizing Image browsing Map) is one of contents-based image retrieval algorithms that uses only browsable mapping results obtained by SOM(Self Organizing Map). However, SOM may have an error in selecting the right BMU in learning phase if there are similar nodes with distorted color information due to the intensity of light or objects' movements in the image. Such images may be mapped into other grouping nodes thus the search rate could be decreased by this effect. In this paper, we propose an improved SIM that uses HSV color model in extracting image features with color quantization. In order to avoid unexpected learning error mentioned above, our SOM consists of two layers. In learning phase, SOM layer 1 has the color feature vectors as input. After learning SOM Layer 1, the connection weights of this layer become the input of SOM Layer 2 and re-learning occurs. With this multi-layered SOM learning, we can avoid mapping errors among similar nodes of different color information. In search, we put the query image vector into SOM layer 2 and select nodes of SOM layer 1 that connects with chosen BMU of SOM layer 2. In experiment, we verified that the proposed SIM was better than the original SIM and avoid mapping error effectively.

• Journal of KIISE
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• v.44 no.9
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• pp.992-1004
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• 2017

To assess image quality accurately, an image quality assessment (IQA) metric is required to reflect the human visual system (HVS) properly. In other words, the structure, color, and contrast ratio of the image should be evaluated in consideration of various factors. In addition, as mobile embedded devices such as smartphone become popular, a fast computing speed is important. In this paper, the proposed IQA metric combines color similarity, gradient similarity, and phase similarity synergistically to satisfy the HVS and is designed by using optimized pooling and quantization for fast computation. The proposed IQA metric is compared against existing 13 methods using 4 kinds of evaluation methods. The experimental results show that the proposed IQA metric ranks the first on 3 evaluation methods and the first on the remaining method, next to VSI which is the most remarkable IQA metric. Its computing speed is on average about 20% faster than VSI's. In addition, we find that the proposed IQA metric has a bigger amount of correlation with the HVS than existing IQA metrics.