The Experts below are selected from a list of 243 Experts worldwide ranked by ideXlab platform
Heungkyu Lee - One of the best experts on this subject based on the ideXlab platform.
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Open Access Screenshot identification by analysis of directional inequality of Interlaced video
2013Co-Authors: Jiwon Lee, Minjeong Lee, Hae-yeoun Lee, Heungkyu LeeAbstract:As screenshots of copyrighted video content are spreading through the Internet without any regulation, cases of copyright infringement have been observed. Further, it is difficult to use existing forensic techniques for determining whether or not a given image was captured from a screen. Thus, we propose a screenshot identification scheme using the trace of screen capture. Since most television systems and camcorders use Interlaced Scanning, many screenshots are taken from Interlaced videos. Consequently, these screenshots contain the trace of Interlaced videos, combing artifacts. In this study, we identify a screenshot using the characteristics of combing artifacts that appear to be shaped like horizontal jagged noise and can be found around the edges. To identify a screenshot, the edge areas are extracted using the gray level co-occurrence matrix (GLCM). Then, the amount of combing artifacts is calculated in the extracted edge areas by using the similarity ratio (SR), the ratio of the horizontal noise to the vertical noise. By analyzing the directional inequality of noise components, the proposed scheme identifies the source of an input image. In the experiments conducted, the identification accuracy is measured in various environments. The results prove that the proposed identification scheme is stable and performs well
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Screenshot identification by analysis of directional inequality of Interlaced video
EURASIP Journal on Image and Video Processing, 2012Co-Authors: Jiwon Lee, Minjeong Lee, Hae-yeoun Lee, Heungkyu LeeAbstract:As screenshots of copyrighted video content are spreading through the Internet without any regulation, cases of copyright infringement have been observed. Further, it is difficult to use existing forensic techniques for determining whether or not a given image was captured from a screen. Thus, we propose a screenshot identification scheme using the trace of screen capture. Since most television systems and camcorders use Interlaced Scanning, many screenshots are taken from Interlaced videos. Consequently, these screenshots contain the trace of Interlaced videos, combing artifacts. In this study, we identify a screenshot using the characteristics of combing artifacts that appear to be shaped like horizontal jagged noise and can be found around the edges. To identify a screenshot, the edge areas are extracted using the gray level co-occurrence matrix (GLCM). Then, the amount of combing artifacts is calculated in the extracted edge areas by using the similarity ratio (SR), the ratio of the horizontal noise to the vertical noise. By analyzing the directional inequality of noise components, the proposed scheme identifies the source of an input image. In the experiments conducted, the identification accuracy is measured in various environments. The results prove that the proposed identification scheme is stable and performs well.
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screenshot identification using combing artifact from Interlaced video
ACM workshop on Multimedia and security, 2010Co-Authors: Jiwon Lee, Minjeong Lee, Seungjin Ryu, Heungkyu LeeAbstract:As screenshots of copyrighted video contents are spreading through the Internet without any regulation, the copyright infringement arises gradually. However, there are still no techniques that prevent illegal screenshots from spreading out. Since most television systems use Interlaced Scanning mode and camcorders offer Interlaced recording, many screenshots are Interlaced. In this paper, we propose a screenshot identification scheme using unique characteristic of Interlaced video, combing artifact. To do this, several blocks of the input image are selected for the significant combing artifact they have. In each block, eight features that represent the artifact are extracted. These extracted features are applied to train and test support vector machine for identifying whether the input image is a screenshot or not. In the experiments, the identification accuracy is measured when the input image is converted from and to common multimedia formats. The results prove that the proposed identification scheme performs well under the widely used image formats as JPEG, BMP, TIFF and video formats as MPEG-2, MPEG-4, H.264.
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MM&Sec - Screenshot identification using combing artifact from Interlaced video
Proceedings of the 12th ACM workshop on Multimedia and security - MM&Sec '10, 2010Co-Authors: Jiwon Lee, Minjeong Lee, Seungjin Ryu, Heungkyu LeeAbstract:As screenshots of copyrighted video contents are spreading through the Internet without any regulation, the copyright infringement arises gradually. However, there are still no techniques that prevent illegal screenshots from spreading out. Since most television systems use Interlaced Scanning mode and camcorders offer Interlaced recording, many screenshots are Interlaced. In this paper, we propose a screenshot identification scheme using unique characteristic of Interlaced video, combing artifact. To do this, several blocks of the input image are selected for the significant combing artifact they have. In each block, eight features that represent the artifact are extracted. These extracted features are applied to train and test support vector machine for identifying whether the input image is a screenshot or not. In the experiments, the identification accuracy is measured when the input image is converted from and to common multimedia formats. The results prove that the proposed identification scheme performs well under the widely used image formats as JPEG, BMP, TIFF and video formats as MPEG-2, MPEG-4, H.264.
Zhihong Wang - One of the best experts on this subject based on the ideXlab platform.
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A Plant Image Compression Algorithm Based on Wireless Sensor Network
Journal of Computational Chemistry, 2019Co-Authors: Guiling Sun, Yuanqiang Chu, Xiaochao Liu, Zhihong WangAbstract:This paper designs and implements an image transmission algorithm applied to plant information collection based on the wireless sensor network. It can effectively reduce the volume of transmitted data, low-energy, high-availability image compression algorithm. This algorithm mainly has two aspects of improvement measures: the first is to reduce the number of pixels that transmit images, from Interlaced Scanning to Interlaced neighbor Scanning; the second is to use JPEG image compression algorithm [1], changing the value of the quantization table in the algorithm [2]. After image compression, the image data volume is greatly reduced; the transmission efficiency is improved; and the problem of excessive data volume during image transmission is effectively solved.
Jiwon Lee - One of the best experts on this subject based on the ideXlab platform.
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Open Access Screenshot identification by analysis of directional inequality of Interlaced video
2013Co-Authors: Jiwon Lee, Minjeong Lee, Hae-yeoun Lee, Heungkyu LeeAbstract:As screenshots of copyrighted video content are spreading through the Internet without any regulation, cases of copyright infringement have been observed. Further, it is difficult to use existing forensic techniques for determining whether or not a given image was captured from a screen. Thus, we propose a screenshot identification scheme using the trace of screen capture. Since most television systems and camcorders use Interlaced Scanning, many screenshots are taken from Interlaced videos. Consequently, these screenshots contain the trace of Interlaced videos, combing artifacts. In this study, we identify a screenshot using the characteristics of combing artifacts that appear to be shaped like horizontal jagged noise and can be found around the edges. To identify a screenshot, the edge areas are extracted using the gray level co-occurrence matrix (GLCM). Then, the amount of combing artifacts is calculated in the extracted edge areas by using the similarity ratio (SR), the ratio of the horizontal noise to the vertical noise. By analyzing the directional inequality of noise components, the proposed scheme identifies the source of an input image. In the experiments conducted, the identification accuracy is measured in various environments. The results prove that the proposed identification scheme is stable and performs well
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Screenshot identification by analysis of directional inequality of Interlaced video
EURASIP Journal on Image and Video Processing, 2012Co-Authors: Jiwon Lee, Minjeong Lee, Hae-yeoun Lee, Heungkyu LeeAbstract:As screenshots of copyrighted video content are spreading through the Internet without any regulation, cases of copyright infringement have been observed. Further, it is difficult to use existing forensic techniques for determining whether or not a given image was captured from a screen. Thus, we propose a screenshot identification scheme using the trace of screen capture. Since most television systems and camcorders use Interlaced Scanning, many screenshots are taken from Interlaced videos. Consequently, these screenshots contain the trace of Interlaced videos, combing artifacts. In this study, we identify a screenshot using the characteristics of combing artifacts that appear to be shaped like horizontal jagged noise and can be found around the edges. To identify a screenshot, the edge areas are extracted using the gray level co-occurrence matrix (GLCM). Then, the amount of combing artifacts is calculated in the extracted edge areas by using the similarity ratio (SR), the ratio of the horizontal noise to the vertical noise. By analyzing the directional inequality of noise components, the proposed scheme identifies the source of an input image. In the experiments conducted, the identification accuracy is measured in various environments. The results prove that the proposed identification scheme is stable and performs well.
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screenshot identification using combing artifact from Interlaced video
ACM workshop on Multimedia and security, 2010Co-Authors: Jiwon Lee, Minjeong Lee, Seungjin Ryu, Heungkyu LeeAbstract:As screenshots of copyrighted video contents are spreading through the Internet without any regulation, the copyright infringement arises gradually. However, there are still no techniques that prevent illegal screenshots from spreading out. Since most television systems use Interlaced Scanning mode and camcorders offer Interlaced recording, many screenshots are Interlaced. In this paper, we propose a screenshot identification scheme using unique characteristic of Interlaced video, combing artifact. To do this, several blocks of the input image are selected for the significant combing artifact they have. In each block, eight features that represent the artifact are extracted. These extracted features are applied to train and test support vector machine for identifying whether the input image is a screenshot or not. In the experiments, the identification accuracy is measured when the input image is converted from and to common multimedia formats. The results prove that the proposed identification scheme performs well under the widely used image formats as JPEG, BMP, TIFF and video formats as MPEG-2, MPEG-4, H.264.
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MM&Sec - Screenshot identification using combing artifact from Interlaced video
Proceedings of the 12th ACM workshop on Multimedia and security - MM&Sec '10, 2010Co-Authors: Jiwon Lee, Minjeong Lee, Seungjin Ryu, Heungkyu LeeAbstract:As screenshots of copyrighted video contents are spreading through the Internet without any regulation, the copyright infringement arises gradually. However, there are still no techniques that prevent illegal screenshots from spreading out. Since most television systems use Interlaced Scanning mode and camcorders offer Interlaced recording, many screenshots are Interlaced. In this paper, we propose a screenshot identification scheme using unique characteristic of Interlaced video, combing artifact. To do this, several blocks of the input image are selected for the significant combing artifact they have. In each block, eight features that represent the artifact are extracted. These extracted features are applied to train and test support vector machine for identifying whether the input image is a screenshot or not. In the experiments, the identification accuracy is measured when the input image is converted from and to common multimedia formats. The results prove that the proposed identification scheme performs well under the widely used image formats as JPEG, BMP, TIFF and video formats as MPEG-2, MPEG-4, H.264.
Ming-kun Cheng - One of the best experts on this subject based on the ideXlab platform.
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Video de-interlacing algorithm based on motion adaptive spatial-temporal technique
Journal of Electronic Imaging, 2013Co-Authors: Shih-chang Hsia, Ming-kun ChengAbstract:Television (TV) systems use an Interlaced Scanning method because transmission bandwidth is limited. Although technological developments can solve the problem of bandwidth, the Interlaced Scanning approach is still used in video broadcasting due to the high cost of updating equipment. However, the current LCD displays employ progressive Scanning screens. The de-interlacing technique that converts the Interlaced signal to a progressive signal is required for a TV receiver to create the display. In this study, adaptive spatial and temporal processing is proposed in implementing a high-performance de-interlace algorithm. First, the spatial de-interlace method is proposed for the conditional interpolation using edge detection to improve the quality. Then the motion adaptive de-interlace technique is proposed based on motion detection with multifield references. With this method, we can differentiate between the moving states of the current field using forward or backward prediction. Then the missing lines can be interpolated based on motion features from the adaptation of spatial and temporal pixels. Results demonstrate that the proposed algorithm can achieve a better tradeoff between complexity and performance compared with the competing methods.
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TV De-interlacing Algorithm based on Motion Adaptive Spatial-Temporal Technique
國立高雄第一科技大學-電腦與通訊工程研究所, 2011Co-Authors: Ming-kun ChengAbstract:[[abstract]]在50 年代因為科技發展的限制，使得當時傳輸頻寬受到了限制，因此才會有交錯式掃描訊號的產生，當時這個規格一直沿用到近代，雖然現在科技的發展使得傳輸頻寬已經不是問題，但是更新影像廣播設備需要昂貴的費用，所以現在的影像廣播主要還是以交錯式掃描訊號為主。而現今的主要顯示設備卻已經由交錯式掃描更換成為漸進式掃描顯示器，且尺寸越來越大，解析度也越來越高，所以解交錯演算法的好壞，也直接影響了顯示影像品質。 本論文採用移動適應性解交錯演算法，並對其移動偵測和補插技術上做改善。在移動偵測上利用五圖場的資訊，將前前圖場、前圖場、現在圖場和後圖場組成一個群組，使用四圖場移動偵測方法去做移動偵測，另外使用前圖場、現在圖場、後圖場和後後圖場組成另一個群組，也同樣使用四圖場移動偵測去做移動偵測，這樣的方法可分辨出是現在圖場與前圖場動和後圖場移動狀態，若是前後圖場都是屬於動態則使用時間-空間域增強邊緣偵測線平均所補插出的像素，若是前後圖場都屬於靜態則採用前後圖場像素平均，如果只有一個為靜態另一個為動態，則使用圖場重複解交錯，結合這幾個方法來得到高品質的影像。[[abstract]]In the 1950s, TV system used Interlaced Scanning method because of transmission bandwidth limited. Although the evolution of technology can solve the problem of bandwidth, the Interlaced Scanning approach is still used in visual broadcasting due to the high cost of updating equipment. However, the popular LCD display had been changed into progressive Scanning screen. The de-interlace chip that convert interlace signal to progress one is required in TV receiver for display. In this study, the adaptive spatial and temporal processing is proposed to implement de-interlace algorithm. First, the spatial de-interlace is proposed by spatial-temporal edge detection and interpolation to improve the quality. Then motion adaptive de-interlace is adopted to based on motion detection with use five fields. By this method, we can differentiate the moving states of current field, forward field and backward fields. Then the missing pixel can be interpolated based on motion feature, from spatial or temporal pixel. Results demonstrate that the proposed algorithm can improve about 2~5 dB PSNR in average compared with the competing methods
Satoshi Nishimura - One of the best experts on this subject based on the ideXlab platform.
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restored Interlaced volumetric imaging increases image quality and Scanning speed during intravital imaging in living mice
Journal of Biophotonics, 2020Co-Authors: Maina Sogabe, Masayuki Ohzeki, Koji Fujimoto, Atsuko Seharafujisawa, Satoshi NishimuraAbstract:Dynamic intravital imaging is essential for revealing ongoing biological phenomena within living organisms and is influenced primarily by several factors: motion artifacts, optical properties and spatial resolution. Conventional imaging quality within a volume, however, is degraded by involuntary movements and trades off between the imaged volume, imaging speed and quality. To balance such trade-offs incurred by two-photon excitation microscopy during intravital imaging, we developed a unique combination of Interlaced Scanning and a simple image restoration algorithm based on biological signal sparsity and a graph Laplacian matrix. This method increases the Scanning speed by a factor of four for a field size of 212 μm × 106 μm × 130 μm, and significantly improves the quality of four-dimensional dynamic volumetric data by preventing irregular artifacts due to the movement observed with conventional methods. Our data suggest this method is robust enough to be applied to multiple types of soft tissue.