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Relevant bibliographies by topics / Data encryption (Computer science) Image compression

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Academic literature on the topic 'Data encryption (Computer science) Image compression'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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Journal articles on the topic "Data encryption (Computer science) Image compression"

1

Ahmed, Mahmood Ali, and Khamees Khalaf Hasan. "Data Compression and Encryption for Remote Sensor Networks Using Different Techniques Methods." Journal of Cases on Information Technology 23, no. 2 (April 2021): 39–49. http://dx.doi.org/10.4018/jcit.20210401.oa4.

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Wireless sensor networks (WSNs) are widely used for the different multimedia applications in which the data in multimedia form (e.g., images and videos) are captured by visual sensors and transmitted to base stations or destination nodes, and such networks, commonly called wireless multimedia sensor networks (WMSN), transmit high-quality images over sensor nodes with image quality improvement and CS technique while the security of CS data is achieved. The one-way DF-based cooperative digital image transmission model over WSNs using advanced terminologies for image compression called advanced JPEG2000 compression and modified elliptical curve cryptography (ECC) encryption is proposed. Image quality improvement efficiency is essential in multimedia applications and wireless communications. The authors proposed a novel technique based on adaptive and dynamic noise removal and quality improvement for both before compression and after compression. The compression method designed is based on CS technique.

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Huang, Bin, Chun Wan, and Kaimeng Chen. "High-Capacity Reversible Data Hiding in Encrypted Images Based on Adaptive Predictor and Compression of Prediction Errors." Mathematics 9, no. 17 (September 5, 2021): 2166. http://dx.doi.org/10.3390/math9172166.

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Reversible data hiding in encrypted images (RDHEI) is a technology which embeds secret data into encrypted images in a reversible way. In this paper, we proposed a novel high-capacity RDHEI method which is based on the compression of prediction errors. Before image encryption, an adaptive linear regression predictor is trained from the original image. Then, the predictor is used to obtain the prediction errors of the pixels in the original image, and the prediction errors are compressed by Huffman coding. The compressed prediction errors are used to vacate additional room with no loss. After image encryption, the vacated room is reserved for data embedding. The receiver can extract the secret data and recover the image with no errors. Compared with existing approaches, the proposed method efficiently improves the embedding capacity.

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Ga, H., and W. Zeng. "Image compression and encryption based on wavelet transform and chaos." Computer Optics 43, no. 2 (April 2019): 258–63. http://dx.doi.org/10.18287/2412-6179-2019-43-2-258-263.

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With the rapid development of network technology, more and more digital images are transmitted on the network, and gradually become one important means for people to access the information. The security problem of the image information data increasingly highlights and has become one problem to be attended. The current image encryption algorithm basically focuses on the simple encryption in the frequency domain or airspace domain, and related methods also have some shortcomings. Based on the characteristics of wavelet transform, this paper puts forward the image compression and encryption based on the wavelet transform and chaos by combining the advantages of chaotic mapping. This method introduces the chaos and wavelet transform into the digital image encryption algorithm, and transforms the image from the spatial domain to the frequency domain of wavelet transform, and adds the hybrid noise to the high frequency part of the wavelet transform, thus achieving the purpose of the image degradation and improving the encryption security by combining the encryption approaches in the spatial domain and frequency domain based on the chaotic sequence and the excellent characteristics of wavelet transform. Testing experiments show that such algorithm reduces the memory consumption and implements the complexity, not only can decrease the key spending and compress the time spending, but also can improve the quality of decoded and reconstructed image, thus showing good encryption features with better encryption effect.

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Mehta, Garima, Malay Kishore Dutta, and Pyung Soo Kim. "An Efficient and Lossless Cryptosystem for Security in Tele-Ophthalmology Applications Using Chaotic Theory." International Journal of E-Health and Medical Communications 7, no. 4 (October 2016): 28–47. http://dx.doi.org/10.4018/ijehmc.2016100102.

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Tele-ophthalmology has gained a lot of popularity as it involves retinal fundus images which can be analyzed for identification of severe diseases like diabetic retinopathy and glaucoma. With this increasing popularity, requirement for medical data confidentiality and privacy has also increased during transmission or storage. To meet this challenge, this paper propose an efficient and lossless cryptosystem based upon chaotic theory for encryption of medical fundus images. In the proposed encryption scheme a strategic combination of scrambling and substitution architecture is proposed which complements each other. The proposed scheme of encryption for fundus images is challenging as these images are 3-D color image and cannot be compressed as compression may not be able to retain all relevant medical information. For performance analysis, the proposed algorithm has been evaluated for perceptual and cryptographic security. The experimental results indicate that the proposed method is lossless and resistant against attacks making the proposed scheme suitable for real time applications.

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Prasad, Bhagwati, and Kunti Mishra. "A Combined Encryption Compression Scheme Using Chaotic Maps." Cybernetics and Information Technologies 13, no. 2 (June 1, 2013): 75–81. http://dx.doi.org/10.2478/cait-2013-0016.

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Abstract The intent of this paper is to propose an encryption compression scheme using multiple chaotic maps along with the concept of Galois field. This method improves the security of the encrypted data and a significant compression is also achieved. The obtained high security architectures are ideal for many real life applications such as medical images, legal documents and military and other operation

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Dengre, Amit, and A. D. Gawande. "Audio Encryption and Digital Image Watermarking in an Uncompress Video." International Journal of Advances in Applied Sciences 4, no. 2 (June 1, 2015): 66. http://dx.doi.org/10.11591/ijaas.v4.i2.pp66-72.

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The growth of high speed computer networks and that of the Internet, in particular, has increased the ease of Information Communication. Ironically, the cause for the development is also of the apprehension - use of digital formatted data. In comparison with Analog media, Digital media offers several distinct advantages such as high quality, easy editing, high fidelity copying, compression etc. But this type advancement in the field of data communication in other sense has hiked the fear of getting the data snooped at the time of sending it from the sender to the receiver. So, Information Security is becoming an inseparable part of Data Communication. In order to address this Information Security, Steganography plays an important role. Steganography is the art and science of writing hidden messages in such a way that no one apart from the sender and intended recipient even realizes there is a hidden message. In the current internet community, secure data transfer is limited due to its attack made on data communication. So more robust methods are chosen so that they ensure secured data transfer. One of the solutions which came to the rescue is the audio Steganography. But existing audio steganographic systems have poor interface, very low level implementation, difficult to understand and valid only for certain audio formats with restricted message size. Enhanced Audio Steganography (EAS) is one proposed system which is based on audio Steganography and cryptography & watermarking, ensures secure data transfer between the source and destination.

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Tütüncü, Kemal, and Özcan Çataltaş. "Compensation of degradation, security, and capacity of LSB substitution methods by a new proposed hybrid n-LSB approach." Computer Science and Information Systems, no. 00 (2021): 48. http://dx.doi.org/10.2298/csis210227048t.

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This study proposes a new hybrid n-LSB (Least Significant Bit) substitution-based image steganography method in the spatial plane. The previously proposed n-LSB substitution method by authors of this paper is combined with the Rivest-Shamir-Adleman (RSA), RC5, and Data Encryption Standard (DES) encryption algorithms to improve the security of the steganography, which is one of the requirements of steganography, and the Lempel-Ziv-Welch (LZW), Arithmetic and Deflate lossless compression algorithms to increase the secret message capacity. Also, embedding was done randomly using a logistic map-based chaos generator to increase the security more. The classical n-LSB substitution method and the proposed hybrid approaches based on the previously proposed n-LSB were implemented using different secret messages and cover images. When the results were examined, it has been seen that the proposed hybrid n-LSB approach showed improvement in all three criteria of steganography. The proposed hybrid approach that consists of previously proposed n-LSB, RSA, Deflate, and the logistic map had the best results regarding capacity, security, and imperceptibility.

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Tu, Rongxin, Wenying Wen, and Changsheng Hua. "An Unequal Image Privacy Protection Method Based on Saliency Detection." Security and Communication Networks 2020 (December 3, 2020): 1–13. http://dx.doi.org/10.1155/2020/8842376.

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Cloud platforms provide a good stage for storing and sharing big image data for users, although some privacy issues arise. Image encryption technology can prevent privacy leakage and can ensure secure image data sharing on cloud platforms. Hence, in this paper, an unequal encryption scheme based on saliency detection is proposed. First, based on the mechanism of visual perception and the theory of feature integration, the visual attention model is employed to realize the recognition of significant regions and insignificant regions. Then, a dynamic DNA encryption algorithm is proposed to exploit heavyweight encryption for significant regions, while semi-tensor product compressed sensing is introduced to exploit lightweight encryption and compression for insignificant regions. Experimental results demonstrate that the proposed framework can serve to secure big image data services.

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Wang, Jijun, and Soo Fun Tan. "Separable Reversible Data Hiding in Encryption Image with Two-Tuples Coding." Computers 10, no. 7 (July 7, 2021): 86. http://dx.doi.org/10.3390/computers10070086.

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Separable Reversible Data Hiding in Encryption Image (RDH-EI) has become widely used in clinical and military applications, social cloud and security surveillance in recent years, contributing significantly to preserving the privacy of digital images. Aiming to address the shortcomings of recent works that directed to achieve high embedding rate by compensating image quality, security, reversible and separable properties, we propose a two-tuples coding method by considering the intrinsic adjacent pixels characteristics of the carrier image, which have a high redundancy between high-order bits. Subsequently, we construct RDH-EI scheme by using high-order bits compression, low-order bits combination, vacancy filling, data embedding and pixel diffusion. Unlike the conventional RDH-EI practices, which have suffered from the deterioration of the original image while embedding additional data, the content owner in our scheme generates the embeddable space in advance, thus lessening the risk of image destruction on the data hider side. The experimental results indicate the effectiveness of our scheme. A ratio of 28.91% effectively compressed the carrier images, and the embedding rate increased to 1.753 bpp with a higher image quality, measured in the PSNR of 45.76 dB.

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Goldberg, Mark A. "Image data compression." Journal of Digital Imaging 11, S1 (August 1998): 230–32. http://dx.doi.org/10.1007/bf03168323.

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Dissertations / Theses on the topic "Data encryption (Computer science) Image compression"

1

Maniccam, Suchindran S. "Image-video compression, encryption and information hiding /." Online version via UMI:, 2001.

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Wong, Hon Wah. "Image watermarking and data hiding techniques /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202003%20WONGH.

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Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2003.
Includes bibliographical references (leaves 163-178). Also available in electronic version. Access restricted to campus users.

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Uehara, Takeyuki. "Contributions to image encryption and authentication." Access electronically, 2003. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20040920.124409/index.html.

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Koutsogiannis, Vassilis. "A study of color image data compression /." Online version of thesis, 1992. http://hdl.handle.net/1850/11060.

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Lee, Joshua Ka-Wing. "A model-adaptive universal data compression architecture with applications to image compression." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111868.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 59-61).
In this thesis, I designed and implemented a model-adaptive data compression system for the compression of image data. The system is a realization and extension of the Model-Quantizer-Code-Separation Architecture for universal data compression which uses Low-Density-Parity-Check Codes for encoding and probabilistic graphical models and message-passing algorithms for decoding. We implement a lossless bi-level image data compressor as well as a lossy greyscale image compressor and explain how these compressors can rapidly adapt to changes in source models. We then show using these implementations that Restricted Boltzmann Machines are an effective source model for compressing image data compared to other compression methods by comparing compression performance using these source models on various image datasets.
by Joshua Ka-Wing Lee.
S.M.

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Todd, Martin Peter. "Image data compression based on a multiresolution signal model." Thesis, University of Warwick, 1989. http://wrap.warwick.ac.uk/100937/.

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Image data compression is an important topic within the general field of image processing. It has practical applications varying from medical imagery to video telephones, and provides significant implications for image modelling theory. In this thesis a new class of linear signal models, linear interpolative multiresolution models, is presented and applied to the data compression of a range of natural images. The key property of these models is that whilst they are non- causal in the two spatial dimensions they are causal in a third dimension, the scale dimension. This leads to computationally efficient predictors which form the basis of the data compression algorithms. Models of varying complexity are presented, ranging from a simple stationary form to one which models visually important features such as lines and edges in terms of scale and orientation. In addition to theoretical results such as related rate distortion functions, the results of applying the compression algorithms to a variety of images are presented. These results compare favourably, particularly at high compression ratios, with many of the techniques described in the literature, both in terms of mean squared quantisation noise and more meaningfully, in terms of perceived visual quality. In particular the use of local orientation over various scales within the consistent spatial interpolative framework of the model significantly reduces perceptually important distortions such as the blocking artefacts often seen with high compression coders. A new algorithm for fast computation of the orientation information required by the adaptive coder is presented which results in an overall computational complexity for the coder which is broadly comparable to that of the simpler non-adaptive coder. This thesis is concluded with a discussion of some of the important issues raised by the work.

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Norman, Kelly Robert. "Encryption of Computer Peripheral Devices." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1232.pdf.

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Bhupathiraju, Kalyan Varma. "Empirical analysis of BWT-based lossless image compression." Morgantown, W. Va. : [West Virginia University Libraries], 2010. http://hdl.handle.net/10450/10958.

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Thesis (M.S.)--West Virginia University, 2010.
Title from document title page. Document formatted into pages; contains v, 61 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 54-56).

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Jalumuri, Nandakishore R. "A study of scanning paths for BWT based image compression." Morgantown, W. Va. : [West Virginia University Libraries], 2004. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3633.

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Thesis (M.S.)--West Virginia University, 2004.
Title from document title page. Document formatted into pages; contains vii, 56 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 52-54).

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Roth, Christopher. "A distributed password scheme for network operating systems." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Jun%5FRoth.pdf.

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Books on the topic "Data encryption (Computer science) Image compression"

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name, No. Mathematics of data/image coding, compression, and encryption V, with applications: 9-10 July 2002, Seattle, Washington, USA. Bellingham, WA: SPIE, 2003.

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NATO Advanced Study Institute on Fractal Image Encoding and Analysis (1995 Trondheim, Norway). Fractal image encoding and analysis. Berlin: Springer, 1998.

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NATO Advanced Study Institute on Fractal Image Encoding and Analysis (1995 Trondheim, Norway). Fractal image encoding and analysis. Berlin: Springer, 1998.

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Schmalz, Mark S. Mathematics of data/image coding, compression, and encryption with applications XII: 2-4 August 2010, San Diego, California, United States. Edited by Society of Photo-optical Instrumentation Engineers. Bellingham, Wash: SPIE, 2010.

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(Society), SPIE, ed. Mathematics for signal and information processing: 2-5 August 2009, San Diego, California, United States. Bellingham, Wash: SPIE, 2009.

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6

Salomon, David. Data Compression: The Complete Reference. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000.

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Giovanni, Motta, ed. Handbook of data compression. 5th ed. London: Springer, 2010.

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V, Prasad M., ed. Lossy image compression: Domain decomposition-based algorithms. London: Springer, 2011.

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Glover, D. Subband coding for image data archiving. [Washington, DC: National Aeronautics and Space Administration, 1992.

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Farrelle, Paul Michael. Recursive Block Coding for Image Data Compression. New York, NY: Springer New York, 1990.

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Book chapters on the topic "Data encryption (Computer science) Image compression"

1

Pal, Arup Kumar, G. P. Biswas, and S. Mukhopadhyay. "An Efficient Compression-Encryption Scheme for Batch-Image." In Communications in Computer and Information Science, 85–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20209-4_12.

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Todd-Pokropek, A. "Image Data Compression Techniques: A Survey." In Mathematics and Computer Science in Medical Imaging, 167–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83306-9_7.

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Moravie, P., H. Essafi, C. Lambert-Nebout, and J. L. Basille. "Real-time image compression using data-parallelism." In Lecture Notes in Computer Science, 723–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/bfb0020511.

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Pogrebnyak, Oleksiy, Pablo Manrique Ramírez, Luis Pastor Sanchez Fernandez, and Roberto Sánchez Luna. "Data Dependent Wavelet Filtering for Lossless Image Compression." In Lecture Notes in Computer Science, 285–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11578079_30.

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Kuroda, Hideo, Makoto Fujimura, and Kazumasa Hamano. "Considerations of Image Compression Scheme Hiding a Part of Coded Data into Own Image Coded Data." In Communications in Computer and Information Science, 312–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10546-3_37.

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Holt, Murray J. J. "A fast binary template matching algorithm for document image data compression." In Lecture Notes in Computer Science, 230–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/3-540-19036-8_22.

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Shin, Sang Uk, Kyeong Seop Sim, and Kyung Hyune Rhee. "A Secrecy Scheme for MPEG Video Data Using the Joint of Compression and Encryption." In Lecture Notes in Computer Science, 191–201. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-47790-x_16.

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Fujiyoshi, Masaaki, and Hitoshi Kiya. "Histogram-Based Near-Lossless Data Hiding and Its Application to Image Compression." In Lecture Notes in Computer Science, 225–35. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24078-7_22.

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Li, Wei-Soul, Wen-Shyong Hsieh, and Ming-Hong Sun. "Index LOCO-I: A Hybrid Method of Data Hiding and Image Compression." In Lecture Notes in Computer Science, 463–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11553939_66.

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Shaw, Chandrama, Sukanta Das, and Biplab K. Sikdar. "Cellular Automata Based Encoding Technique for Wavelet Transformed Data Targeting Still Image Compression." In Lecture Notes in Computer Science, 141–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11861201_19.

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Conference papers on the topic "Data encryption (Computer science) Image compression"

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Holtz, Klaus E., Eric S. Holtz, and Diana Kalienky. "Autosophy data/image compression and encryption." In Optical Science and Technology, the SPIE 49th Annual Meeting, edited by Mark S. Schmalz. SPIE, 2004. http://dx.doi.org/10.1117/12.549029.

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Phadikar, A., and S. P. Maity. "Quality access control of image by encryption and data hiding." In 2009 International Conference on Methods and Models in Computer Science (ICM2CS). IEEE, 2009. http://dx.doi.org/10.1109/icm2cs.2009.5397991.

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