Academic literature on the topic 'Low-distortion embedding'
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Journal articles on the topic "Low-distortion embedding"
Castermans, Thom, Kevin Verbeek, Bettina Speckmann, Michel A. Westenberg, Rob Koopman, Shenghui Wang, Hein van den Berg, and Arianna Betti. "SolarView: Low Distortion Radial Embedding with a Focus." IEEE Transactions on Visualization and Computer Graphics 25, no. 10 (October 1, 2019): 2969–82. http://dx.doi.org/10.1109/tvcg.2018.2865361.
Full textShih, Frank Y., and Xin Zhong. "Intelligent Watermarking for High-Capacity Low-Distortion Data Embedding." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 05 (April 21, 2016): 1654003. http://dx.doi.org/10.1142/s0218001416540033.
Full textLi, Wan Qi, Heng Wang, Che Nian, Huang Wei, and Hong Yao You. "Minimizing the Embedding Impact Using Network Flow Algorithms." Advanced Materials Research 341-342 (September 2011): 478–83. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.478.
Full textNguyen, Dinh-Chien, Thai-Son Nguyen, Chin-Chen Chang, Huan-Sheng Hsueh, and Fang-Rong Hsu. "High Embedding Capacity Data Hiding Algorithm for H.264/AVC Video Sequences without Intraframe Distortion Drift." Security and Communication Networks 2018 (August 1, 2018): 1–11. http://dx.doi.org/10.1155/2018/2029869.
Full textHu, Yongjin, Xiyan Li, and Jun Ma. "A Novel LSB Matching Algorithm Based on Information Pre-Processing." Mathematics 10, no. 1 (December 21, 2021): 8. http://dx.doi.org/10.3390/math10010008.
Full textWang, Ruosong, and David P. Woodruff. "Tight Bounds for ℓ 1 Oblivious Subspace Embeddings." ACM Transactions on Algorithms 18, no. 1 (January 31, 2022): 1–32. http://dx.doi.org/10.1145/3477537.
Full textZhang, Shun, Liang Yang, Xihao Xu, and Tiegang Gao. "Secure Steganography in JPEG Images Based on Histogram Modification and Hyper Chaotic System." International Journal of Digital Crime and Forensics 10, no. 1 (January 2018): 40–53. http://dx.doi.org/10.4018/ijdcf.2018010104.
Full textYang, Yuhang, Xuyu Xiang, Jiaohua Qin, Yun Tan, Zhangdong Wang, and Yajie Liu. "High-Embedded Low-Distortion Multihistogram Shift Video Reversible Data Hiding Based on DCT Coefficient." Electronics 12, no. 7 (March 31, 2023): 1652. http://dx.doi.org/10.3390/electronics12071652.
Full textTsai, Yuan-Yu, Yao-Hsien Huang, Ruo-Jhu Lin, and Chi-Shiang Chan. "An Adjustable Interpolation-based Data Hiding Algorithm Based on LSB Substitution and Histogram Shifting." International Journal of Digital Crime and Forensics 8, no. 2 (April 2016): 48–61. http://dx.doi.org/10.4018/ijdcf.2016040105.
Full textWu, Nan-I., Kuo-Chen Wu, and Chung-Ming Wang. "Exploring pixel-value differencing and base decomposition for low distortion data embedding." Applied Soft Computing 12, no. 2 (February 2012): 942–60. http://dx.doi.org/10.1016/j.asoc.2011.09.002.
Full textDissertations / Theses on the topic "Low-distortion embedding"
Carpenter, Timothy E. "Algorithms For Low-Distortion Embeddings Into Geometrically Restricted Spaces." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555337435997622.
Full textSidiropoulos, Anastasios. "Approximation algorithms for low-distortion embeddings into low-dimensional spaces." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/34126.
Full textIncludes bibliographical references (p. 33-35).
We present several approximation algorithms for the problem of embedding metric spaces into a line, and into the two-dimensional plane. We give an O([square root] n)-approximation algorithm for the problem of finding a line embedding of a metric induced by a given unweighted graph, that minimizes the (standard) multiplicative distortion. For the same problem, we give an exact algorithm, with running-time exponential in the distortion. We complement these results by showing that the problem is NP-hard to [alpha]-approximate, for some constant [alpha] > 1. For the two-dimensional case, we show a O([square root] n) upper bound for the distortion required to embed an n-point subset of the two-dimensional sphere, into the plane. We prove that this bound is asymptotically tight, by exhibiting n-point subsets such that any embedding into the plane has distortion [omega]([square root] n). These techniques yield a O(1)-approximation algorithm for the problem of embedding an n-point subset of the sphere into the plane.
by Anastasios Sidiropoulos.
S.M.
Yan, Yiqing. "Scalable and accurate algorithms for computational genomics and dna-based digital storage." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS078.
Full textCost reduction and throughput improvement in sequencing technology have resulted in new advances in applications such as precision medicine and DNA-based storage. However, the sequenced result contains errors. To measure the similarity between the sequenced result and reference, edit distance is preferred in practice over Hamming distance due to the indels. The primitive edit distance calculation is quadratic complex. Therefore, sequence similarity analysis is computationally intensive. In this thesis, we introduce two accurate and scalable sequence similarity analysis algorithms, i) Accel-Align, a fast sequence mapper and aligner based on the seed–embed–extend methodology, and ii) Motif-Search, an efficient structure-aware algorithm to recover the information encoded by the composite motifs from the DNA archive. Then, we use Accel-Align as an efficient tool to study the random access design in DNA-based storage
Ke, Yen-Ching, and 柯衍慶. "A Study of Low Distortion or Distortion-free Data Embedding Algorithms for High Dynamic Range Images." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/11542322514894594244.
Full text國立中興大學
資訊網路多媒體研究所
100
In this paper, we investigate data embedding algorithms for high dynamic range images encoded by the RGBE image format. We present four algorithms that have the distortion-free feature and one algorithm that demonstrates the feature of very low distortion. Our first algorithm belongs to the distortion-free manner. In this algorithm, we make use of all statuses produced by the pixel variation and employ triplet coding technology to increase the embedding capacity. Comparing with the previous work, our algorithm can improve the embedding capacity in the range between 5.52% and 5.79%. No image distortion is encountered when tone mapping the high dynamic range embedded images to produce the low dynamic range embedded image. The second algorithm we introduce belongs to the distortion-free manner. In this algorithm, we take advantages of the “null” pixel, a new pixel category produced by the E channel, where we embed messages into these pixels to expand the embedding capacity. Experimental results show that comparing to our counterparts, our algorithm can offer an average of 48.37% embedding capacity without causing any image distortion. The third algorithm we develop belongs to the very low distortion manner. We adopt an optimization computation mechanism for the R, G, B channels to generate a number of potential pixels, referred to as “promising” and “feasible” pixels. These pixels cause the least image distortion when operating the message embedding. Comparing to our counterparts, the algorithm can largely increase the amount of embedding capacity with the magnitude between 2.77 and 3.02. The tone mapped image presents high PSNR values (76.60~84.44 dB) showing no perceivable visual difference. The final algorithm we propose is with the distortion-free manner. We take advantage of homogeneous pixel representation and combine a group of M pixels (M>=2) as a pixel cluster to generate sufficient statuses for message embedding. This approach allows us to adopt the triplet coding technique to increase the embedding capacity without incurring any image distortion. We compare our scheme of using 2 pixels as a cluster with previous results of using a single pixel. The comparison indicates that our algorithm can provide larger payloads in the range of 317~2397 bits. We adopt the tone mapping scheme to produce low dynamic range images to quantize the image difference, and we employ the HDR-VDP technique to inspect the visual difference between the cover and stego images. The image difference quantization results show that no distortion is encountered. The probability map produced by HDR-VDP inspection is in grey color indicating that the detection probability of visual difference is null. In conclusion, our work offers the following four contributions: we exploit the triplet coding technology and increase the capacity for non-distortion algorithm; we make use of a new pixels to convey messages, raising the embedding capacity; we develop the optimization computation mechanism fully using pixels not available in our counterpart to further increase the embedding capacity; we adopt the pixel cluster scheme allowing the increase of concealed messages without causing image distortion. The four algorithms developed are adequate for applications of image annotation and image steganography.
JHANG, YONG, and 張顒. "A Study of Data Hiding Algorithm with High Embedding Capacity and Low Distortion." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/08892940439139247182.
Full text稻江科技暨管理學院
數位內容設計與管理學系研究所
102
In this thesis, a novel data hiding method, designed base on human visual perception and capable of offering high invisibility, is proposed to enable the user to conceal large amounts of secret data into edge areas of still images. In order to achieve a higher payload and to obtain a better stego image quality, the principle of payload allotment is to hide more data into edge areas than smooth areas, thus serving the ultimate purpose of maintaining human imperceptibility to the distortion in the image caused by the embedding of the secret message. In our new scheme, we utilize the pixel-value differencing to determine the payload of a pixel pair. The higher difference values the areas are, the large the messages are hidden, and vice versa. In the embedding algorithm, the binary secret data is transferred into non-decimal number system and then hid into each pixel pair using modulus function. Since the characteristics of the pixel pairs remain the same after the secret data is hidden, the proposed scheme qualifies as a so-called "blind" scheme, meaning that the presence of the original image is not required in the extraction procedure. The experiments we conducted have demonstrated that the proposed scheme is indeed capable of hiding more data while keeping down the stego-image distortion and remaining the imperceptibility. The performance of the proposed scheme, compared with those of the existing techniques, proving that our scheme is not only feasible but better.
Book chapters on the topic "Low-distortion embedding"
Faragó, András. "Low Distortion Metric Embedding into Constant Dimension." In Lecture Notes in Computer Science, 114–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20877-5_12.
Full textSarkar, Rik. "Low Distortion Delaunay Embedding of Trees in Hyperbolic Plane." In Graph Drawing, 355–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25878-7_34.
Full textBabilon, Robert, Jiří Matoušek, Jana Maxová, and Pavel Valtr. "Low-Distortion Embeddings of Trees." In Graph Drawing, 343–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45848-4_27.
Full textBhattacharya, Arnab, Purushottam Kar, and Manjish Pal. "On Low Distortion Embeddings of Statistical Distance Measures into Low Dimensional Spaces." In Lecture Notes in Computer Science, 164–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03573-9_13.
Full textWu, Hanzhou. "Graph Models in Information Hiding." In Graph Theory [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.98592.
Full textBabilon, Robert, Jiří Matoušek, Jana Maxová, and Pavel Valtr. "Low-Distortion Embeddings of Trees." In Graph Algorithms and Applications 4, 399–409. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812773296_0018.
Full textDiwaker, Ritesh, and Deepak Asrani. "Multimedia Security in Audio Signal." In Artificial intelligence and Multimedia Data Engineering, 70–81. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815196443123010008.
Full textIndyk, Piotr, and Ji_í Matou_ek. "Low-Distortion Embeddings of Finite Metric Spaces." In Handbook of Discrete and Computational Geometry, Second Edition. Chapman and Hall/CRC, 2004. http://dx.doi.org/10.1201/9781420035315.ch8.
Full text"Low-Distortion Embeddings of Finite Metric Spaces." In Handbook of Discrete and Computational Geometry, Second Edition, 190–209. Chapman and Hall/CRC, 2004. http://dx.doi.org/10.1201/9781420035315-8.
Full textConference papers on the topic "Low-distortion embedding"
Koranne, S. "Analysis of very large resistive networks using low distortion embedding." In 2013 14th International Symposium on Quality Electronic Design (ISQED 2013). IEEE, 2013. http://dx.doi.org/10.1109/isqed.2013.6523659.
Full textOstrovsky, Rafail, and Yuval Rabani. "Low distortion embeddings for edit distance." In the thirty-seventh annual ACM symposium. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1060590.1060623.
Full textBraunsmann, Juliane, Marko Rajkovic, Martin Rumpf, and Benedikt Wirth. "Learning low bending and low distortion manifold embeddings." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2021. http://dx.doi.org/10.1109/cvprw53098.2021.00498.
Full textIndyk, P. "Algorithmic applications of low-distortion geometric embeddings." In Proceedings 42nd IEEE Symposium on Foundations of Computer Science. IEEE, 2001. http://dx.doi.org/10.1109/sfcs.2001.959878.
Full textRousselot, S., D. Truffi, G. Doulgeris, S. Mistry, V. Pachidis, and P. Pilidis. "Generation of a Quasi 3-D Map of a Half-Embedded Ultra High Bypass Ratio Turbofan Intake on the Wing of a Broad Delta Wing Airframe." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-51008.
Full textBǎdoiu, Mihai, Julia Chuzhoy, Piotr Indyk, and Anastasios Sidiropoulos. "Low-distortion embeddings of general metrics into the line." In the thirty-seventh annual ACM symposium. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1060590.1060624.
Full textFawzi, Omar, Patrick Hayden, and Pranab Sen. "From low-distortion norm embeddings to explicit uncertainty relations and efficient information locking." In the 43rd annual ACM symposium. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1993636.1993738.
Full textMeng, Xiangrui, and Michael W. Mahoney. "Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression." In the 45th annual ACM symposium. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2488608.2488621.
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