Literatura académica sobre el tema "Image compression"
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Artículos de revistas sobre el tema "Image compression"
Saudagar, Abdul Khader Jilani. "Biomedical Image Compression Techniques for Clinical Image Processing". International Journal of Online and Biomedical Engineering (iJOE) 16, n.º 12 (19 de octubre de 2020): 133. http://dx.doi.org/10.3991/ijoe.v16i12.17019.
Texto completoKhan, Sulaiman, Shah Nazir, Anwar Hussain, Amjad Ali y Ayaz Ullah. "An efficient JPEG image compression based on Haar wavelet transform, discrete cosine transform, and run length encoding techniques for advanced manufacturing processes". Measurement and Control 52, n.º 9-10 (19 de octubre de 2019): 1532–44. http://dx.doi.org/10.1177/0020294019877508.
Texto completoDavid S, Alex, Almas Begum y Ravikumar S. "Content clustering for MRI Image compression using PPAM". International Journal of Engineering & Technology 7, n.º 1.7 (5 de febrero de 2018): 126. http://dx.doi.org/10.14419/ijet.v7i1.7.10631.
Texto completoKatayama, O., S. Ishihama, K. Namiki y I. Ohi. "Color Changes in Electronic Endoscopic Images Caused by Image Compression". Diagnostic and Therapeutic Endoscopy 4, n.º 1 (1 de enero de 1997): 43–50. http://dx.doi.org/10.1155/dte.4.43.
Texto completoKhatun, Shamina y Anas Iqbal. "A Review of Image Compression Using Fractal Image Compression with Neural Network". International Journal of Innovative Research in Computer Science & Technology 6, n.º 2 (31 de marzo de 2018): 9–11. http://dx.doi.org/10.21276/ijircst.2018.6.2.1.
Texto completoKaur, Gaganpreet, Hitashi Hitashi y Dr Gurdev Singh. "PERFORMANCE EVALUATION OF IMAGE QUALITY BASED ON FRACTAL IMAGE COMPRESSION". INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, n.º 1 (2 de febrero de 2012): 20–27. http://dx.doi.org/10.24297/ijct.v2i1.2608.
Texto completoCardone, Barbara, Ferdinando Di Martino y Salvatore Sessa. "Fuzzy Transform Image Compression in the YUV Space". Computation 11, n.º 10 (1 de octubre de 2023): 191. http://dx.doi.org/10.3390/computation11100191.
Texto completoMohammed, Hind Rostom y Ameer Abd Al-Razaq. "SWF Image Compression by Evaluating objects compression ratio". Journal of Kufa for Mathematics and Computer 1, n.º 2 (30 de octubre de 2010): 105–18. http://dx.doi.org/10.31642/jokmc/2018/010209.
Texto completoPaul, Okuwobi Idowu y Yong Hua Lu. "A New Approach in Digital Image Compression Using Unequal Error Protection (UEP)". Applied Mechanics and Materials 704 (diciembre de 2014): 403–7. http://dx.doi.org/10.4028/www.scientific.net/amm.704.403.
Texto completoMohammed, Sajaa G., Safa S. Abdul-Jabbar y Faisel G. Mohammed. "Art Image Compression Based on Lossless LZW Hashing Ciphering Algorithm". Journal of Physics: Conference Series 2114, n.º 1 (1 de diciembre de 2021): 012080. http://dx.doi.org/10.1088/1742-6596/2114/1/012080.
Texto completoTesis sobre el tema "Image compression"
Hawary, Fatma. "Light field image compression and compressive acquisition". Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S082.
Texto completoBy capturing a scene from several points of view, a light field provides a rich representation of the scene geometry that brings a variety of novel post-capture applications and enables immersive experiences. The objective of this thesis is to study the compressibility of light field contents in order to propose novel solutions for higher-resolution light field imaging. Two main aspects were studied through this work. The compression performance on light fields of the actual coding schemes still being limited, there is need to introduce more adapted approaches to better describe the light field structures. We propose a scalable coding scheme that encodes only a subset of light field views and reconstruct the remaining views via a sparsity-based method. A residual coding provides an enhancement to the final quality of the decoded light field. Acquiring very large-scale light fields is still not feasible with the actual capture and storage facilities, a possible alternative is to reconstruct the densely sampled light field from a subset of acquired samples. We propose an automatic reconstruction method to recover a compressively sampled light field, that exploits its sparsity in the Fourier domain. No geometry estimation is needed, and an accurate reconstruction is achieved even with very low number of captured samples. A further study is conducted for the full scheme including a compressive sensing of a light field and its transmission via the proposed coding approach. The distortion introduced by the different processing is measured. The results show comparable performances to depth-based view synthesis methods
Obaid, Arif. "Range image compression". Thesis, University of Ottawa (Canada), 1995. http://hdl.handle.net/10393/10131.
Texto completoLacroix, Bruno. "Fractal image compression". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ36939.pdf.
Texto completoAydinoğlu, Behçet Halûk. "Stereo image compression". Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/15447.
Texto completoAbdul-Amir, Said. "Digital image compression". Thesis, De Montfort University, 1985. http://hdl.handle.net/2086/10681.
Texto completoHallidy, William H. Jr y Michael Doerr. "HYPERSPECTRAL IMAGE COMPRESSION". International Foundation for Telemetering, 1999. http://hdl.handle.net/10150/608744.
Texto completoSystems & Processes Engineering Corporation (SPEC) compared compression and decompression algorithms and developed optimal forms of lossless and lossy compression for hyperspectral data. We examined the relationship between compression-induced distortion and additive noise, determined the effect of errors on the compressed data, and showed that the data could separate targets from clutter after more than 50:1 compression.
Hernández-Cabronero, Miguel. "DNA Microarray Image Compression". Doctoral thesis, Universitat Autònoma de Barcelona, 2015. http://hdl.handle.net/10803/297706.
Texto completoIn DNA microarray experiments, two grayscale images are produced. It is convenient to save these images for future, more accurate re-analysis. Thus, image compression emerges as a particularly useful tool to alleviate the associated storage and transmission costs. This dissertation aims at improving the state of the art of the compression of DNA microarray images. A thorough investigation of the characteristics of DNA microarray images has been performed as a part of this work. Results indicate that algorithms not adapted to DNA microarray images typically attain only mediocre lossless compression results due to the image characteristics. By analyzing the first-order and conditional entropy present in these images, it is possible to determine approximate limits to their lossless compressibility. Even though context-based coding and segmentation provide modest improvements over generic-purpose algorithms, conceptual breakthroughs in data coding are arguably required to achieve compression ratios exceeding 2:1 for most images. Prior to the start of this thesis, several lossless coding algorithms that have performance results close to the aforementioned limit were published. However, none of them is compliant with existing image compression standards. Hence, the availability of decoders in future platforms -a requisite for future re-analysis- is not guaranteed. Moreover, the adhesion to standards is usually a requisite in clinical scenarios. To address these problems, a fast reversible transform compatible with the JPEG2000 standard -the Histogram Swap Transform (HST)- is proposed. The HST improves the average compression performance of JPEG2000 for all tested image corpora, with gains ranging from 1.97% to 15.53%. Furthermore, this transform can be applied with only negligible time complexity overhead. With the HST, JPEG2000 becomes arguably the most competitive alternatives to microarray-specific, non-standard compressors. The similarities among sets of microarray images have also been studied as a means to improve the compression performance of standard and microarray-specific algorithms. An optimal grouping of the images which maximizes the inter-group correlation is described. Average correlations between 0.75 and 0.92 are observed for the tested corpora. Thorough experimental results suggest that spectral decorrelation transforms can improve some lossless coding results by up to 0.6bpp, although no single transform is effective for all copora. Lossy coding algorithms can yield almost arbitrary compression ratios at the cost of modifying the images and, thus, of distorting subsequent analysis processes. If the introduced distortion is smaller than the inherent experimental variability, it is usually considered acceptable. Hence, the use of lossy compression is justified on the assumption that the analysis distortion is assessed. In this work, a distortion metric for DNA microarray images is proposed to predict the extent of this distortion without needing a complete re-analysis of the modified images. Experimental results suggest that this metric is able to tell apart image changes that affect subsequent analysis from image modifications that do not. Although some lossy coding algorithms were previously described for this type of images, none of them is specifically designed to minimize the impact on subsequent analysis for a given target bitrate. In this dissertation, a lossy coder -the Relative Quantizer (RQ) coder- that improves upon the rate- distortion results of previously published methods is proposed. Experiments suggest that compression ratios exceeding 4.5:1 can be achieved while introducing distortions smaller than half the inherent experimental variability. Furthermore, a lossy-to-lossless extension of this coder -the Progressive RQ (PRQ) coder- is also described. With the PRQ, images can be compressed once and then reconstructed at different quality levels, including lossless reconstruction. In addition, the competitive rate-distortion results of the RQ and PRQ coders can be obtained with computational complexity slightly smaller than that of the best-performing lossless coder of DNA microarray images.
Agostini, Luciano Volcan. "Projeto de arquiteturas integradas para a compressão de imagens JPEG". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2002. http://hdl.handle.net/10183/11431.
Texto completoThis dissertation presents the design of architectures for JPEG image compression. Architectures for a gray scale images JPEG compressor that were developed are herein presented. This work also addresses a color images JPEG compressor and a color space converter. The designed architectures are described in detail and they were completely described in VHDL, with synthesis directed for Altera Flex10KE family of FPGAs. The integrated architecture for gray scale images JPEG compressor has a minimum latency of 237 clock cycles and it processes an image of 640x480 pixels in 18,5ms, allowing a processing rate of 54 images per second. The compression rate, according to estimates, would be of 6,2 times or 84%, in percentage of bits compression. The integrated architecture for color images JPEG compression was generated starting from incremental changes in the architecture of gray scale images compressor. This architecture also has the minimum latency of 237 clock cycles and it can process a color image of 640 x 480 pixels in 54,4ms, allowing a processing rate of 18,4 images per second. The compression rate, according to estimates, would be of 14,4 times or 93%, in percentage of bits compression. The architecture for space color conversor from RBG to YCbCr has a latency of 6 clock cycles and it is able to process a color image of 640 x 480 pixels in 84,6ms, allowing a processing rate of 11,8 images per second. This architecture was finally not integrated with the color images compressor architecture, but some suggestions, alternatives and estimates were made in this direction.
Nicholl, Peter Nigel. "Feature directed spiral image compression : (a new technique for lossless image compression)". Thesis, University of Ulster, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339326.
Texto completoMandal, Mrinal Kumar. "Wavelets for image compression". Thesis, University of Ottawa (Canada), 1995. http://hdl.handle.net/10393/10277.
Texto completoLibros sobre el tema "Image compression"
Pearlman, William A. Wavelet Image Compression. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-031-02248-7.
Texto completoKou, Weidong. Digital Image Compression. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-2361-8.
Texto completoFisher, Yuval, ed. Fractal Image Compression. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-2472-3.
Texto completoShukla, K. K. y M. V. Prasad. Lossy Image Compression. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2218-0.
Texto completoBarnsley, Michael. Fractal image compression. Wellesley, Mass: AK Peters, 1993.
Buscar texto completoBarnsley, Michael. Fractal image compression. Wellesley, Mass: AK Peters, 1993.
Buscar texto completoStorer, James A. Image and Text Compression. Boston, MA: Springer US, 1992.
Buscar texto completoStorer, James A., ed. Image and Text Compression. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3596-6.
Texto completoS, Carasso Alfred y National Institute of Standards and Technology (U.S.), eds. Image compression and deblurring. Gaithersburg, Md: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2000.
Buscar texto completoRabbani, Majid. Digital image compression techniques. Bellingham, Wash., USA: Spie Optical Engineering Press, 1991.
Buscar texto completoCapítulos de libros sobre el tema "Image compression"
Salomon, David. "Image Compression". En Data Compression, 163–249. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_4.
Texto completoSalomon, David. "Image Compression". En Data Compression, 221–456. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-86092-8_5.
Texto completoSha, Lei. "Image Compression". En Encyclopedia of GIS, 472–75. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_584.
Texto completoWalnut, David F. "Image Compression". En An Introduction to Wavelet Analysis, 371–95. Boston, MA: Birkhäuser Boston, 2004. http://dx.doi.org/10.1007/978-1-4612-0001-7_12.
Texto completoMann, Stephen. "Image Compression". En PACS, 257–80. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4757-3651-9_10.
Texto completoSha, Lei. "Image Compression". En Encyclopedia of GIS, 1–5. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_584-2.
Texto completoSalomon, David y Giovanni Motta. "Image Compression". En Handbook of Data Compression, 443–730. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-903-9_7.
Texto completoSundararajan, D. "Image Compression". En Digital Image Processing, 363–405. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6113-4_13.
Texto completoSalomon, David. "Image Compression". En A Guide to Data Compression Methods, 81–166. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-21708-6_4.
Texto completoWeik, Martin H. "image compression". En Computer Science and Communications Dictionary, 750. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_8640.
Texto completoActas de conferencias sobre el tema "Image compression"
Li, Hui, Yan Lu, Masahiro Takei, Mitsuaki Ochi, Yoshifuru Saito y Kiyoshi Horii. "Flow Image Compression Using Wavelets". En ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-1212.
Texto completoYi Yang, Oscar C. Au, Lu Fang, Xing Wen y Weiran Tang. "Reweighted Compressive Sampling for image compression". En 2009 Picture Coding Symposium (PCS). IEEE, 2009. http://dx.doi.org/10.1109/pcs.2009.5167354.
Texto completoDeng, Chenwei, Weisi Lin, Bu-sung Lee y Chiew Tong Lau. "Robust image compression based on compressive sensing". En 2010 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2010. http://dx.doi.org/10.1109/icme.2010.5583387.
Texto completoHubbard-Featherstone, Casey J., Mark A. Garcia y William Y. L. Lee. "Adaptive block compressive sensing for image compression". En 2017 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, 2017. http://dx.doi.org/10.1109/ivcnz.2017.8402490.
Texto completoMcGuire, Michael D. "Is Fractal Image Compression Related to Cortical Image Compression?" En Applied Vision. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/av.1989.wb4.
Texto completoLi, Hui, Masahiro Takei, Yoshifuru Saito y Kiyoshi Horii. "Application of Wavelet Packet to Particle Image Velocimetry Technique". En ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2090.
Texto completoMaha Lakshmi, G. V. "Implementation of image compression using Fractal Image Compression and neural networks for MRI images". En 2016 International Conference on Information Science (ICIS). IEEE, 2016. http://dx.doi.org/10.1109/infosci.2016.7845301.
Texto completoMailhes, Corinne, Paul Vermande y Francis Castanie. "Spectral Image Compression". En 1989 Intl Congress on Optical Science and Engineering, editado por G. Duchossois, Frank L. Herr y Rodolphe J. Zander. SPIE, 1989. http://dx.doi.org/10.1117/12.961492.
Texto completoYang, Zhaohui, Yunhe Wang, Chang Xu, Peng Du, Chao Xu, Chunjing Xu y Qi Tian. "Discernible Image Compression". En MM '20: The 28th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394171.3413968.
Texto completoSabbatino, V. "Radar image compression". En Radar Systems (RADAR 97). IEE, 1997. http://dx.doi.org/10.1049/cp:19971771.
Texto completoInformes sobre el tema "Image compression"
NETROLOGIC INC SAN DIEGO CA. Image Compression. Fort Belvoir, VA: Defense Technical Information Center, junio de 1990. http://dx.doi.org/10.21236/ada224242.
Texto completoWang, Jun y H. K. Huang. Digital Mammographic Image Compression. Fort Belvoir, VA: Defense Technical Information Center, julio de 1995. http://dx.doi.org/10.21236/ada300271.
Texto completoNakassis, Anastase y Alfred Carasso. Image compression and deblurring. Gaithersburg, MD: National Institute of Standards and Technology, 2000. http://dx.doi.org/10.6028/nist.ir.6521.
Texto completoBoss, R. D. y E. W. Jacobs. Fractal-Based Image Compression. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 1989. http://dx.doi.org/10.21236/ada215400.
Texto completoJacobs, E. W., R. D. Boss y Y. Fisher. Fractal-Based Image Compression, II. Fort Belvoir, VA: Defense Technical Information Center, junio de 1990. http://dx.doi.org/10.21236/ada226500.
Texto completoReynolds, W. D. Jr. Image compression using the W-transform. Office of Scientific and Technical Information (OSTI), diciembre de 1995. http://dx.doi.org/10.2172/195703.
Texto completoMazieres, Bertrand. A New Approach for Fingerprint Image Compression. Office of Scientific and Technical Information (OSTI), diciembre de 1997. http://dx.doi.org/10.2172/763151.
Texto completoHodges, Dewey H. AASERT-92/Image Compression & Wavelet Generation. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 1996. http://dx.doi.org/10.21236/ada337454.
Texto completoLibert, John M. Guidance on Contactless Friction Ridge Image Compression. Gaithersburg, MD: National Institute of Standards and Technology, 2023. http://dx.doi.org/10.6028/nist.ir.8465.
Texto completoLibert, John M. Guidance on Contactless Friction Ridge Image Compression. Gaithersburg, MD: National Institute of Standards and Technology, 2023. http://dx.doi.org/10.6028/nist.sp.500-340.
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