Gotowa bibliografia na temat „Image compression”
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Artykuły w czasopismach na temat "Image compression"
Saudagar, Abdul Khader Jilani. "Biomedical Image Compression Techniques for Clinical Image Processing". International Journal of Online and Biomedical Engineering (iJOE) 16, nr 12 (19.10.2020): 133. http://dx.doi.org/10.3991/ijoe.v16i12.17019.
Pełny tekst źródłaKhan, Sulaiman, Shah Nazir, Anwar Hussain, Amjad Ali i 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, nr 9-10 (19.10.2019): 1532–44. http://dx.doi.org/10.1177/0020294019877508.
Pełny tekst źródłaDavid S, Alex, Almas Begum i Ravikumar S. "Content clustering for MRI Image compression using PPAM". International Journal of Engineering & Technology 7, nr 1.7 (5.02.2018): 126. http://dx.doi.org/10.14419/ijet.v7i1.7.10631.
Pełny tekst źródłaKatayama, O., S. Ishihama, K. Namiki i I. Ohi. "Color Changes in Electronic Endoscopic Images Caused by Image Compression". Diagnostic and Therapeutic Endoscopy 4, nr 1 (1.01.1997): 43–50. http://dx.doi.org/10.1155/dte.4.43.
Pełny tekst źródłaKhatun, Shamina, i Anas Iqbal. "A Review of Image Compression Using Fractal Image Compression with Neural Network". International Journal of Innovative Research in Computer Science & Technology 6, nr 2 (31.03.2018): 9–11. http://dx.doi.org/10.21276/ijircst.2018.6.2.1.
Pełny tekst źródłaKaur, Gaganpreet, Hitashi Hitashi i Dr Gurdev Singh. "PERFORMANCE EVALUATION OF IMAGE QUALITY BASED ON FRACTAL IMAGE COMPRESSION". INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, nr 1 (2.02.2012): 20–27. http://dx.doi.org/10.24297/ijct.v2i1.2608.
Pełny tekst źródłaCardone, Barbara, Ferdinando Di Martino i Salvatore Sessa. "Fuzzy Transform Image Compression in the YUV Space". Computation 11, nr 10 (1.10.2023): 191. http://dx.doi.org/10.3390/computation11100191.
Pełny tekst źródłaMohammed, Hind Rostom, i Ameer Abd Al-Razaq. "SWF Image Compression by Evaluating objects compression ratio". Journal of Kufa for Mathematics and Computer 1, nr 2 (30.10.2010): 105–18. http://dx.doi.org/10.31642/jokmc/2018/010209.
Pełny tekst źródłaPaul, Okuwobi Idowu, i Yong Hua Lu. "A New Approach in Digital Image Compression Using Unequal Error Protection (UEP)". Applied Mechanics and Materials 704 (grudzień 2014): 403–7. http://dx.doi.org/10.4028/www.scientific.net/amm.704.403.
Pełny tekst źródłaMohammed, Sajaa G., Safa S. Abdul-Jabbar i Faisel G. Mohammed. "Art Image Compression Based on Lossless LZW Hashing Ciphering Algorithm". Journal of Physics: Conference Series 2114, nr 1 (1.12.2021): 012080. http://dx.doi.org/10.1088/1742-6596/2114/1/012080.
Pełny tekst źródłaRozprawy doktorskie na temat "Image compression"
Hawary, Fatma. "Light field image compression and compressive acquisition". Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S082.
Pełny tekst źródłaBy 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.
Pełny tekst źródłaLacroix, 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.
Pełny tekst źródłaAydinoğlu, Behçet Halûk. "Stereo image compression". Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/15447.
Pełny tekst źródłaAbdul-Amir, Said. "Digital image compression". Thesis, De Montfort University, 1985. http://hdl.handle.net/2086/10681.
Pełny tekst źródłaHallidy, William H. Jr, i Michael Doerr. "HYPERSPECTRAL IMAGE COMPRESSION". International Foundation for Telemetering, 1999. http://hdl.handle.net/10150/608744.
Pełny tekst źródłaSystems & 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.
Pełny tekst źródłaIn 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.
Pełny tekst źródłaThis 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.
Pełny tekst źródłaMandal, Mrinal Kumar. "Wavelets for image compression". Thesis, University of Ottawa (Canada), 1995. http://hdl.handle.net/10393/10277.
Pełny tekst źródłaKsiążki na temat "Image compression"
Pearlman, William A. Wavelet Image Compression. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-031-02248-7.
Pełny tekst źródłaKou, Weidong. Digital Image Compression. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-2361-8.
Pełny tekst źródłaFisher, Yuval, red. Fractal Image Compression. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-2472-3.
Pełny tekst źródłaShukla, K. K., i M. V. Prasad. Lossy Image Compression. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2218-0.
Pełny tekst źródłaBarnsley, Michael. Fractal image compression. Wellesley, Mass: AK Peters, 1993.
Znajdź pełny tekst źródłaBarnsley, Michael. Fractal image compression. Wellesley, Mass: AK Peters, 1993.
Znajdź pełny tekst źródłaStorer, James A. Image and Text Compression. Boston, MA: Springer US, 1992.
Znajdź pełny tekst źródłaStorer, James A., red. Image and Text Compression. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3596-6.
Pełny tekst źródłaS, Carasso Alfred, i National Institute of Standards and Technology (U.S.), red. Image compression and deblurring. Gaithersburg, Md: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2000.
Znajdź pełny tekst źródłaRabbani, Majid. Digital image compression techniques. Bellingham, Wash., USA: Spie Optical Engineering Press, 1991.
Znajdź pełny tekst źródłaCzęści książek na temat "Image compression"
Salomon, David. "Image Compression". W Data Compression, 163–249. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_4.
Pełny tekst źródłaSalomon, David. "Image Compression". W Data Compression, 221–456. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-86092-8_5.
Pełny tekst źródłaSha, Lei. "Image Compression". W Encyclopedia of GIS, 472–75. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_584.
Pełny tekst źródłaWalnut, David F. "Image Compression". W An Introduction to Wavelet Analysis, 371–95. Boston, MA: Birkhäuser Boston, 2004. http://dx.doi.org/10.1007/978-1-4612-0001-7_12.
Pełny tekst źródłaMann, Stephen. "Image Compression". W PACS, 257–80. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4757-3651-9_10.
Pełny tekst źródłaSha, Lei. "Image Compression". W Encyclopedia of GIS, 1–5. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_584-2.
Pełny tekst źródłaSalomon, David, i Giovanni Motta. "Image Compression". W Handbook of Data Compression, 443–730. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-903-9_7.
Pełny tekst źródłaSundararajan, D. "Image Compression". W Digital Image Processing, 363–405. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6113-4_13.
Pełny tekst źródłaSalomon, David. "Image Compression". W 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.
Pełny tekst źródłaWeik, Martin H. "image compression". W Computer Science and Communications Dictionary, 750. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_8640.
Pełny tekst źródłaStreszczenia konferencji na temat "Image compression"
Li, Hui, Yan Lu, Masahiro Takei, Mitsuaki Ochi, Yoshifuru Saito i Kiyoshi Horii. "Flow Image Compression Using Wavelets". W ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-1212.
Pełny tekst źródłaYi Yang, Oscar C. Au, Lu Fang, Xing Wen i Weiran Tang. "Reweighted Compressive Sampling for image compression". W 2009 Picture Coding Symposium (PCS). IEEE, 2009. http://dx.doi.org/10.1109/pcs.2009.5167354.
Pełny tekst źródłaDeng, Chenwei, Weisi Lin, Bu-sung Lee i Chiew Tong Lau. "Robust image compression based on compressive sensing". W 2010 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2010. http://dx.doi.org/10.1109/icme.2010.5583387.
Pełny tekst źródłaHubbard-Featherstone, Casey J., Mark A. Garcia i William Y. L. Lee. "Adaptive block compressive sensing for image compression". W 2017 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, 2017. http://dx.doi.org/10.1109/ivcnz.2017.8402490.
Pełny tekst źródłaMcGuire, Michael D. "Is Fractal Image Compression Related to Cortical Image Compression?" W Applied Vision. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/av.1989.wb4.
Pełny tekst źródłaLi, Hui, Masahiro Takei, Yoshifuru Saito i Kiyoshi Horii. "Application of Wavelet Packet to Particle Image Velocimetry Technique". W ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2090.
Pełny tekst źródłaMaha Lakshmi, G. V. "Implementation of image compression using Fractal Image Compression and neural networks for MRI images". W 2016 International Conference on Information Science (ICIS). IEEE, 2016. http://dx.doi.org/10.1109/infosci.2016.7845301.
Pełny tekst źródłaMailhes, Corinne, Paul Vermande i Francis Castanie. "Spectral Image Compression". W 1989 Intl Congress on Optical Science and Engineering, redaktorzy G. Duchossois, Frank L. Herr i Rodolphe J. Zander. SPIE, 1989. http://dx.doi.org/10.1117/12.961492.
Pełny tekst źródłaYang, Zhaohui, Yunhe Wang, Chang Xu, Peng Du, Chao Xu, Chunjing Xu i Qi Tian. "Discernible Image Compression". W MM '20: The 28th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394171.3413968.
Pełny tekst źródłaSabbatino, V. "Radar image compression". W Radar Systems (RADAR 97). IEE, 1997. http://dx.doi.org/10.1049/cp:19971771.
Pełny tekst źródłaRaporty organizacyjne na temat "Image compression"
NETROLOGIC INC SAN DIEGO CA. Image Compression. Fort Belvoir, VA: Defense Technical Information Center, czerwiec 1990. http://dx.doi.org/10.21236/ada224242.
Pełny tekst źródłaWang, Jun, i H. K. Huang. Digital Mammographic Image Compression. Fort Belvoir, VA: Defense Technical Information Center, lipiec 1995. http://dx.doi.org/10.21236/ada300271.
Pełny tekst źródłaNakassis, Anastase, i Alfred Carasso. Image compression and deblurring. Gaithersburg, MD: National Institute of Standards and Technology, 2000. http://dx.doi.org/10.6028/nist.ir.6521.
Pełny tekst źródłaBoss, R. D., i E. W. Jacobs. Fractal-Based Image Compression. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 1989. http://dx.doi.org/10.21236/ada215400.
Pełny tekst źródłaJacobs, E. W., R. D. Boss i Y. Fisher. Fractal-Based Image Compression, II. Fort Belvoir, VA: Defense Technical Information Center, czerwiec 1990. http://dx.doi.org/10.21236/ada226500.
Pełny tekst źródłaReynolds, W. D. Jr. Image compression using the W-transform. Office of Scientific and Technical Information (OSTI), grudzień 1995. http://dx.doi.org/10.2172/195703.
Pełny tekst źródłaMazieres, Bertrand. A New Approach for Fingerprint Image Compression. Office of Scientific and Technical Information (OSTI), grudzień 1997. http://dx.doi.org/10.2172/763151.
Pełny tekst źródłaHodges, Dewey H. AASERT-92/Image Compression & Wavelet Generation. Fort Belvoir, VA: Defense Technical Information Center, grudzień 1996. http://dx.doi.org/10.21236/ada337454.
Pełny tekst źródłaLibert, 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.
Pełny tekst źródłaLibert, 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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