• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.561-564
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• 2005

In this paper, we present a new JPEG (Joint Photograph Experts Group) compression architecture which compresses still image into fixed size of bitstream to use restricted system memory efficiently. The size of bitstream is determined by the complexity of image and the quantization table. But the quantization table is set in advance the complexity of image is the essential factor. Therefore the size of bitstream for high complexity image is large and the size for low complexity image is small. This means that the management of restricted system memory is difficult. The proposed JPEG encoder estimates the size of bitstream using the correlation between consecutive frames and selects the quantization table suited to the complexity of image. This makes efficient use of system memory.

• 전기학회논문지
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• 제63권5호
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• pp.690-695
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• 2014

This paper presents a modified CCI image interpolation method based on the even-odd decomposition (EOD). The CCI method is a well-known technique to interpolate images. Although the method provides better image quality than the linear interpolation, its complexity still is a problem. To remedy the problem, this paper introduces analysis on the EOD decomposition of CCI and then proposes a reduced CCI interpolation in terms of complexity, providing better image quality in terms of PSNR. To evaluate the proposed method, we conduct experiments and complexity comparison. The results indicate that our method do not only outperforms the existing methods by up to 43% in terms of MSE but also requires low-complexity with 37% less computing time than the CCI method.

• Journal of Information Processing Systems
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• 제2권3호
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• pp.153-158
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• 2006

In this paper, we propose an image interpolator for low computational complexity. The proposed image interpolator supports the image scaling using a modified cubic convolution interpolation between the input and output resolutions for a full screen display. In order to reduce the computational complexity, we use the difference in value of the adjacent pixels for selecting interpolation methods and linear function of the cubic convolution. The proposed image interpolator is compared with the conventional one for the computational complexity and image quality. The proposed image interpolator has been designed and verified by Verilog HDL(Hardware Description Language). It has been synthesized using the Xilinx VirtexE FPGA, and implemented using an FPGA-based prototype board.

• 한국통신학회논문지
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• 제31권3C
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• pp.258-263
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• 2006

정합 블록의 모든 화소가 블록 정합에 동등하게 기여하지 않고, 정합 오차는 영상 복잡도의 영향을 크게 받는다. 이런 사실을 근거로 본 논문에서 영상의 복잡도에 의해 선택되는 일부 서브블록만을 이용한 고속 움직임 추정 알고리즘을 제한한다. 제안된 알고리즘은 정합 블록을 16개의 서브블록으로 나누고, 각 서브블록의 영상 복잡도를 계산하고, 일부 복잡한 서브블록만을 사용하여 부분 블록 정합을 수행하여 움직임 벡터를 검출한다. 시뮬레이션 결과는 제안된 알고리즘이 기존 탐색 알고리즘과 비교하여 무시할 수 있을 정도의 화질 저하를 야기하지만 상당한 계산량을 줄여주는 것을 보여준다.

• 전자공학회논문지S
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• 제34S권8호
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• pp.74-83
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• 1997

In this paper, we propose a new fractal image decoding algorithm with fast convergence speed by using the data dependence and the improved initial image estimation. Conventional method for fractal image decoding requires high-degrdd computational complexity in decoding process, because of iterated contractive transformations applied to whole range blocks. On proposed method, Range of reconstruction imagte is divided into referenced range and data dependence region. And computational complexity is reduced by application of iterated contractive transformations for the referenced range only. Data dependence region can be decoded by one transformations when the referenced range is converged. In addition, more exact initial image is estimated by using bound () function in case of all, and an initial image more nearer to a fixed point is estimated by using range block division estimation. Consequently, the convergence speed of reconstruction iamge is improved with 40% reduction of computational complexity.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.678-683
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• 2009

A lot of researches [1] have been conducted on digital watermark embedding in brightness. A prerequisite for the digital watermark is that the image quality does not change even if the volume of the embedded information increases. Generally, the noise on complex images is perceived than the noise on fiat images. Thus, we present a method for watermarking an image by embedding complex areas by priority. The proposed method has achieved higher image quality of digital watermarking compared to other method that do not take into consideration the complexity of blocks, although the PSNR of the proposed method is lower than for a method not based on block complexity.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 춘계학술대회
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• pp.354-357
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• 2013

기본 영상 처리 알고리즘들 중 컴퓨터 비전 처리의 전반부에 매우 자주 사용하게 되는 알고리즘이 바로 필터링이다. 그런데 일반적으로 영상 신호는 2차원 신호이므로 테이터량과 연산량이 방대하다. 이런 방대한 연산량을 줄이기 위해, 분리가능 필터와 필터의 대칭성을 이용한 인수분해 공식을 필터링 연산에 적용하였다. 실험결과 이미지의 조건에 따라 다르지만, 상당한 연산량 감소를 확인 할 수 있었다.

• 한국멀티미디어학회논문지
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• 제21권3호
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• pp.342-347
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• 2018

We proposed a fast detection of copy-move image forgery using four step search algorithm in the spatial domain. In the four-step search algorithm, the search area is 21 (-10 ~ +10), and the number of pixels to be scanned is 33. Our algorithm reduced computational complexity more than conventional copy move image forgery methods. The proposed method reduced 92.34 % of computational complexity compare to exhaustive search algorithm.

• 대한전자공학회논문지SP
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• 제48권4호
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• pp.38-48
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• 2011

Backlight Profile과 Image Pixel의 Homogeneity 분석을 통한 Block 단위 LCD Backlight Nonideality 및 Crosstalk Compensation 방안이 제안되었다. Image에 따라 Block Size 및 연산에서 제외되는 Block 범위 그리고 연산에 고려되는 Backlight 범위를 최적화하여, 화질을 유지하면서 연산량을 최소화시켰다. 실제 영상을 바탕으로 하는 Simulation을 통해 제안된 Compensation 연산량과 화질이 평가되었다.

• 한국멀티미디어학회논문지
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• 제23권6호
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• pp.756-764
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• 2020

A typical algorithm used for image analysis is the Convolutional Neural Network(CNN). R-CNN, Fast R-CNN, Faster R-CNN, etc. have been studied to improve the performance of the CNN, but they essentially require large amounts of data and high algorithmic complexity., making them inappropriate for small and medium-sized services. Therefore, in this paper, the image label prediction algorithm based on CNN with collaborative layer with low complexity, high accuracy, and small amount of data was proposed. The proposed algorithm was designed to replace the part of the neural network that is performed to predict the final label in the existing deep learning algorithm by implementing collaborative filtering as a layer. It is expected that the proposed algorithm can contribute greatly to small and medium-sized content services that is unsuitable to apply the existing deep learning algorithm with high complexity and high server cost.