• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권7호
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• pp.3286-3300
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• 2016

High Efficiency Video Coding (HEVC) Standard, as the latest coding standard, introduces satisfying compression structures with respect to its predecessor Advanced Video Coding (H.264/AVC). The new coding standard can offer improved encoding performance compared with H.264/AVC. However, it also leads to enormous computational complexity that makes it considerably difficult to be implemented in real time application. In this paper, based on machine learning, a fast partitioning method is proposed, which can search for the best splitting structures for Intra-Prediction. In view of the video texture characteristics, we choose the entropy of Gray-Scale Difference Statistics (GDS) and the minimum of Sum of Absolute Transformed Difference (SATD) as two important features, which can make a balance between the computation complexity and classification performance. According to the selected features, adaptive decision trees can be built for the Coding Units (CU) with different size by offline training. Furthermore, by this way, the partition of CUs can be resolved as a binary classification problem. Experimental results have shown that the proposed algorithm can save over 34% encoding time on average, with a negligible Bjontegaard Delta (BD)-rate increase.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권4호
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• pp.1927-1943
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• 2016

Jitterbug is a passive network covert timing channel supplying reliable stealthy transmission. It is also the basic manner of some improved covert timing channels designed for higher undetectability. The existing entropy-based detection scheme based on training sample binning may suffer from model mismatching, which results in detection performance deterioration. In this paper, a new detection method based on the feature of Jitterbug covert channel traffic is proposed. A fixed binning strategy without training samples is used to obtain bins distribution feature. Coefficient of variation (CV) is calculated for several sets of selected bins and the weighted mean is used to calculate the final CV value to distinguish Jitterbug from normal traffic. Furthermore, the timing window parameter of Jitterbug is estimated based on the detected traffic. Experimental results show that the proposed detection method can achieve high detection performance even with interference of network jitter, and the parameter estimation method can provide accurate values after accumulating plenty of detected samples.

• Journal of Information Processing Systems
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• 제9권4호
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• pp.575-591
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• 2013

Most of the edge detection methods available in literature are gradient based, which further apply thresholding, to find the final edge map in an image. In this paper, we propose a novel method that is based on fuzzy logic for edge detection in gray images without using the gradient and thresholding. Fuzzy logic is a mathematical logic that attempts to solve problems by assigning values to an imprecise spectrum of data in order to arrive at the most accurate conclusion possible. Here, the fuzzy logic is used to conclude whether a pixel is an edge pixel or not. The proposed technique begins by fuzzifying the gray values of a pixel into two fuzzy variables, namely the black and the white. Fuzzy rules are defined to find the edge pixels in the fuzzified image. The resultant edge map may contain some extraneous edges, which are further removed from the edge map by separately examining the intermediate intensity range pixels. Finally, the edge map is improved by finding some left out edge pixels by defining a new membership function for the pixels that have their entire 8-neighbourhood pixels classified as white. We have compared our proposed method with some of the existing standard edge detector operators that are available in the literature on image processing. The quantitative analysis of the proposed method is given in terms of entropy value.

• 대한원격탐사학회지
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• 제18권3호
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• pp.181-186
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• 2002

One of the most important procedure in stereo DEM generation is the stereo matching process which finds the conjugate pixels in a pair of stereo imagery. In order to be found as conjugate pixels, the pixels should have distinct spatial feature to be distinguished from other pixels. However, in the homogeneous areas such as water covered or forest canopied areas, it is very difficult to find the conjugate pixels due to the lack of distinct spatial feature. Most of erroneous elevation values in the stereo DEM are produced in those homogeneous areas. This paper presents a simple method for improving the quality of stereo DEM utilizing the texture filters. An entropy filter was applied to one of the input stereo imagery to extract very homogeneous areas before stereo matching process. Those extracted homogeneous areas were excluded from being candidates for stereo matching process. Also a statistical texture filter was applied to the generated elevation values before the interpolation process was applied in odor to remove the remaining anomalous elevation values. Stereo pair of SPOT level 1B panchromatic imagery were used for the experiments. The results showed that by utilizing the texture filters as a pre and a post processor of stereo matching process, the quality of the stereo DEM could be dramatically improved.

• 한국컴퓨터정보학회논문지
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• 제7권1호
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• pp.112-119
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• 2002

본 논문에서는 계수분할을 이용한 개선된 웨이블릿 패킷 영상 부호화 알고리듬을 제안한다. 제안된 알고리듬은 전체 압축시간을 단축을 위해 엔트로피를 이용하여 영상을 웨이블릿 패킷 분할하고, 부대역간 상관성을 이용하여 계수들을 제로트리 방식으로 부호화하여 비트율 및 왜곡 성능을 개선한다. 영상 복원시 오차 감소를 위해 각 주파수 부대역간의 상관성을 이용하여 새로운 부모-자식노드 관계를 추출하고, 이를 이용하여 계수들에 대한 부호화 순서를 결정한다. 컴퓨터 모의실험을 통해 제안된 웨이블릿 패킷영상 부호화 알고리듬에 대한 성능 평가를 위해 텍스처 이미지에 대해 기존의 알고리듬과 비트율과 제곱오차에 대한 비교 평가하였으며, 비트율 및 왜곡 성능이 개선되었음을 확인하였다.

• International Journal of Computer Science & Network Security
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• 제22권1호
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• pp.1-6
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• 2022

Exploration has always been an instinct of humans, and underwater life is as fascinating as it seems. So, for studying flora and fauna below water, there is a need for high-quality images. However, the underwater images tend to be of impaired quality due to various factors, which calls for improved and enhanced underwater images. There are various Histogram Equalization (HE) based techniques which could aid in solving these issues. Classifying the HE methods broadly, there is Global Histogram Equalization (GHE), Mean Brightness Preserving HE (MBPHE), Bin Modified HE (BMHE), and Local HE (LHE). Each of these HE extensions have their own pros and cons and thus, by considering them we have considered BBHE, CLAHE, BPDHE, BPDFHE, and DSIHE enhancement algorithms, which are based on Mean Brightness Preserving HE and Local HE, for this study. The performance is evaluated with non-reference performance measures like Entropy, UCIQE, UICM, and UIQM. In this study, we apply the enhancement algorithms on 300 images from the UIEB benchmark dataset and then apply the techniques of cascading fusion on the best-performing algorithms.

• Journal of Information Processing Systems
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• 제19권4호
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• pp.417-426
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• 2023

To improve the effect of image restoration and solve the image detail loss, an image dehazing enhancement algorithm based on mean guided filtering is proposed. The superpixel calculation method is used to pre-segment the original foggy image to obtain different sub-regions. The Ncut algorithm is used to segment the original image, and it outputs the segmented image until there is no more region merging in the image. By means of the mean-guided filtering method, the minimum value is selected as the value of the current pixel point in the local small block of the dark image, and the dark primary color image is obtained, and its transmittance is calculated to obtain the image edge detection result. According to the prior law of dark channel, a classic image dehazing enhancement model is established, and the model is combined with a median filter with low computational complexity to denoise the image in real time and maintain the jump of the mutation area to achieve image dehazing enhancement. The experimental results show that the image dehazing and enhancement effect of the proposed algorithm has obvious advantages, can retain a large amount of image detail information, and the values of information entropy, peak signal-to-noise ratio, and structural similarity are high. The research innovatively combines a variety of methods to achieve image dehazing and improve the quality effect. Through segmentation, filtering, denoising and other operations, the image quality is effectively improved, which provides an important reference for the improvement of image processing technology.

• 한국정보통신학회논문지
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• 제19권12호
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• pp.2885-2891
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• 2015

온라인 소셜 네트워크 서비스 중 하나인 트위터는 가장 보편적으로 사용되는 마이크로 블로그인데, 트위터의 개방적 구조로 인해 자동화 프로그램인 트윗 봇이 많이 생성되고 있다. 이 트윗 봇은 적법한 봇과 악성 봇으로 분류되는데, 이 중 악성 봇은 일반 사용자들에게 많은 양의 스팸 정보나 유해한 컨텐츠를 배포하기 때문에 트윗 봇을 검출하는 작업은 반드시 필요하다. 기존 연구에서는 시간적 정보를 활용하여 사람과 트윗 봇을 분류하였다. 본 논문에서는 사용자들의 고 정밀 위치 정보를 알려주는 공간 태그된 트윗 정보를 활용하여 트위터 사용자들의 정확한 위치와 트윗 전송시각을 알아낸 후, 각 사용자의 시공간 엔트로피를 계산하여 트윗 봇을 검출하는 개선된 두 단계 알고리즘을 제안한다. 주요 결과로써, 시간 정보만을 이용한 기존 연구결과보다 각 신뢰도별 봇 검출 확률 및 거짓 경보 확률이 모두 우수하게 나타난다.

• 정보처리학회논문지C
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• 제8C권5호
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• pp.525-534
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• 2001

일반적으로 무손실 영상압축 및 암호화 방법에는 압축과 암호화 과정이 독립적으로 이루어진다. 압축 후 암호화를 수행하면 압축열이 암호에 대한 평문으로 사용되므로 압축에 따른 엔트로피가 감소하여 랜덤한 성질을 갖게된다. 그러나 압축열 전체에 대한 암호화는 수행시간이 길어져 실시간 처리를 저해하는 원인이 되기도 한다. 본 논문에서는 무손실 영상압축과 암호의 결합에서 전체 처리시간을 줄이는 방법을 제안한다. 이는 쿼드트리 압축 알고리즘으로 그레이 영상을 분해하여 구조부분만을 암호화하는 방법이다. 아울러 영상의 무상관성과 동질영역을 확보하기 위한 변환과정을 수행하여 무손실 압축성능을 개선하였고, 쿼드트리 분해시 암호화되지 않은 데이터를 레벨별로 재구성하여 안전성을 갖도록 하였다. 모의 실험을 통하여 제안한 방법이 영상 압축율의 개선과 암호화 방법의 안전성 확보 및 실시간 처리가 가능함을 확인하였다.

• 한국융합학회논문지
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• 제8권5호
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• pp.13-17
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• 2017

음성 인식에서 기존의 음성 특징 추출 방법은 명확하지 않은 스레숄드 값으로 인해 부정확한 음성 인식률을 가진다. 본 연구에서는 음성과 비음성에 대한 특징 추출을 묵음 특징 정규화를 융합한 음성 인식 성능 향상을 위한 방법을 모델링 한다. 제안한 방법에서는 잡음의 영향을 최소화하여 모델을 구성하였고, 각 음성 프레임에 대해 음성 신호 특징을 추출하여 음성 인식 모델을 구성하였고, 이를 묵음 특징 정규화를 융합하여 에너지 스펙트럼을 엔트로피와 유사하게 표현하여 원래의 음성 신호를 생성하고 음성의 특징이 잡음을 적게 받도록 하였다. 셉스트럼에서 음성과 비음성 분류의 기준 값을 정하여 신호 대 잡음 비율이 낮은 신호에서 묵음 특징 정규화로 성능을 향상하였다. 논문에서 제시하는 방법의 성능 분석은 HMM과 CHMM을 비교하여 결과를 보였으며, 기존의 HMM과 CHMM을 비교한 결과 음성 종속 단계에서는 2.1%p의 인식률 향상이 있었으며, 음성 독립 단계에서는 0.7%p 만큼의 인식률 향상이 있었다.