• Journal of Electrical Engineering and information Science
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• v.3 no.2
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• pp.202-210
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• 1998

This paper is concerned with an efficient generation of stereoscopic views for complex virtual environments by exploiting frame coherence in visibility. The basic idea is to keep visible polygons throughout the rendering process. P-buffer, a buffer of image size, holds the id of the visible polygon for each pixel. This contrasts to the frame buffer and the Z-buffer which hold the color information and the depth information, respectively. For the generation of a consecutive image, the position and the orientation of the visible polygons in the current view are updated according to the viewer's movements, and re-rendered on the current image under the assumption that, when the viewer moves slightly, the visibility of polygons remains unchanged. In the case of stereoscopic views, it may not introduce much difficulty when we render the right(left) image using visible polygons on the (right) image only, The less difference in two images is, the easier the matching becomes in perceiving depth. Some psychophysical experiments have been conducted to support this claim. The computational complexity for generating a fight(left) image from the previous left(right) image is bounded by the size of image space, and accordingly. It is somewhat independent of the complexity of the 3-D scene.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.118-126
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• 2020

In this paper, we present an effective single image fog removal by using the Joint Bright and Dark Channel (JBDC) and pixel-based transmission estimation to enhance the visibility of outdoor images susceptible to degradation due to weather and environmental conditions. The conventional methods include refinement process of coarse transmission with heavy computational complexity. The proposed transmission estimation reveals excellent edge-preserving performance and does not require the refinement process. We estimate the atmospheric light in pixel-based fashion, which can improve the transmission estimation performance and visual quality of the restored image. Moreover, we propose an adaptive transmission estimation to enhance the visual quality specifically in sky regions. Comprehensive experiments on various fog images show that the proposed method exhibits reduced computational complexity and excellent fog removal performance, compared with the existing methods; thus, it can be applied to various fields including real-time devices.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.2
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• pp.9-15
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• 2000

In this paper, we proposed a content-based image retrieval method using color and object's complexity for indexing of image database. Generally, the retrieval methods using color feature can not sufficiently include the spatial information in the image. So they are reduced retrieval efficiency. Then we combined object's complexity which extracted from chain code and the conventional color feature. As a result, experiments shooed that the proposed method which considers the shape feature improved performance in conducting content-based search.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.73-76
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• 2018

A Gaussian noise is caused by surrounding environment or channel interference when transmitting image. The noise reduces not only image quality degradation but also high-level image processing performance. The Non-Local Means (NLM) filter finds similarity in the neighboring sets of pixels to remove noise and assigns weights according to similarity. The weighted average is calculated based on the weight. The NLM filter method shows low noise cancellation performance and high complexity in the process of finding the similarity using weight allocation and neighbor set. In order to solve these problems, we propose an algorithm that shows an excellent noise reduction performance by using Summed Square Image (SSI) to reduce the complexity and applying the weighting function based on a cosine Gaussian kernel function. Experimental results demonstrate the effectiveness of the proposed algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.20-25
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• 2016

This study addresses a low complexity and lossless frame memory compression algorithm based on block-buffer structure for efficient high resolution video processing. Our study utilizes the block-based MHT (modified Hadamard transform) for spatial decorrelation and AGR (adaptive Golomb-Rice) coding as an entropy encoding stage to achieve lossless image compression with low complexity and efficient hardware implementation. The MHT contains only adders and 1-bit shift operators. As a result of AGR not requiring additional memory space and memory access operations, AGR is effective for low complexity development. Comprehensive experiments and computational complexity analysis demonstrate that the proposed algorithm accomplishes superior compression performance relative to existing methods, and can be applied to hardware devices without image quality degradation as well as negligible modification of the existing codec structure. Moreover, the proposed method does not require the memory access operation, and thus it can reduce costs for hardware implementation and can be useful for processing high resolution video over Full HD.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.1-7
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• 2019

This paper presents low-complexity single-image dehazing to enhance the visibility of outdoor images that are susceptible to degradation due to weather and environmental conditions, and applies it to various devices. The conventional methods involve refinement of coarse transmission with high computational complexity and extensive memory requirements. But the proposed transmission estimation method includes excellent edge-preserving performance from comparison of the pixel-based dark channel and the patch-based dark channel in the vicinity of edges, and transmission can be estimated with low complexity since no refinement is required. Moreover, it is possible to accurately estimate transmissions and adaptively remove haze according to the characteristics of the images via prediction of the atmospheric light for each pixel using joint bright and dark channel (JBDC). Comprehensive experiments on various hazy images show that the proposed method exhibits reduced computational complexity and excellent dehazing performance, compared to the existing methods; thus, it can be applied to various fields including real-time devices.

• Journal of KIISE:Software and Applications
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• v.32 no.10
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• pp.949-955
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• 2005

In this Paper, we propose fast block matching algorithm by dividing complex areas based on complexity order of reference block and square sub-block to reduce an amount of computation of full starch(FS) algorithm for fast motion estimation, while keeping the same prediction quality compared with the full search algorithm. By using the fact that matching error is proportional to the gradient of reference block, we reduced unnecessary computations with square sub-block adaptive matching scan based image complexity instead of conventional sequential matching scan and row/column based matching scan. Our algorithm reduces about $30\%$ of computations for block matching error compared with the conventional partial distortion elimination(PDE) algorithm without any prediction quality, and our algorithm will be useful in real-time video coding applications using MPEG-4 AVC or MPEG-2.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.257-264
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• 2019

This paper presents a single image haze removal method via a pixel-based joint BDCP (bright and dark channel prior) and a hierarchical bilateral filter in order to reduce computational complexity and memory requirement while improving the dehazing performance. Pixel-based joint BDCP reduces the computational complexity compared to the patch-based DCP, while making it possible to estimate the atmospheric light in pixel unit and the transmission more accurately. Moreover the bilateral filter, which can smooth an image effectively while preserving edges, refines the transmission to reduce the halo effects, and its hierarchical structure applied to edges only prevents the increase of complexity from the iterative application. Experimental results on various hazy images show that the proposed method exhibits excellent haze removal performance with low computational complexity compared to the conventional methods, and thus it can be applied in various fields.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.33 no.1
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• pp.68-75
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• 1997

In this paper, we propose the improved initial image estimation method for a fast fractal image decoding. When the correlation between a domain and a range is given as the linear equation, the value of initial image estimation using the conventional method is the intersection between its linear equation and y=x. If the gradient of linear equation is large, that the difference of the value between each adjacent pixels is large, the conventional method has disadvantage which has the impossibility of exact estimation. The method of the proposed initial image estimation performs well by two steps. he first step can improve the disadvantage of the conventional method. The second step upgrades the range value which was found previous step by referring information of its domain. Though the computational complexity for the initial image estimation increses slightly, the total computational complexity decreases by 30% than that of the conventional method because of diminishing in the number of iterations.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.171-176
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• 1999

Template matching is one of the most often used techniques for machine vision applications to find a template of size M$\times$M or subimage in a scene image of size N$\times$N. Most template matching methods, however, require pixel operations between the template and the image under analysis resulting in high computational cost of O(M2N2). So in this thesis, we present a two stage template matching method. In the first stage, we use a novel low cost feature whose complexity is approaching O(N2) to select matching candidates. In the second stage, we use conventional template matching method to find out the exact matching point. We compare the result with other methods in terms of complexity, efficiency and performance. Proposed method was proved to have constant time complexity and to be quite invariant to noise.