Método de compresión de archivos de imagen usando técnicas de deep learning
Ilustraciones
- Autores:
-
Varas González, Mario
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/82524
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
Procesamiento de imágenes
Deep Learning
file compression
Compresión de archivos
Procesamiento de imágenes
Image processing
File compression
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
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Método de compresión de archivos de imagen usando técnicas de deep learning |
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Image files compression method using deep learning techniques |
| title |
Método de compresión de archivos de imagen usando técnicas de deep learning |
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Método de compresión de archivos de imagen usando técnicas de deep learning 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Procesamiento de imágenes Deep Learning file compression Compresión de archivos Procesamiento de imágenes Image processing File compression |
| title_short |
Método de compresión de archivos de imagen usando técnicas de deep learning |
| title_full |
Método de compresión de archivos de imagen usando técnicas de deep learning |
| title_fullStr |
Método de compresión de archivos de imagen usando técnicas de deep learning |
| title_full_unstemmed |
Método de compresión de archivos de imagen usando técnicas de deep learning |
| title_sort |
Método de compresión de archivos de imagen usando técnicas de deep learning |
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Varas González, Mario |
| dc.contributor.advisor.none.fl_str_mv |
Branch Bedoya, John Willian |
| dc.contributor.author.none.fl_str_mv |
Varas González, Mario |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores |
| topic |
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Procesamiento de imágenes Deep Learning file compression Compresión de archivos Procesamiento de imágenes Image processing File compression |
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Procesamiento de imágenes |
| dc.subject.proposal.none.fl_str_mv |
Deep Learning file compression |
| dc.subject.proposal.spa.fl_str_mv |
Compresión de archivos Procesamiento de imágenes |
| dc.subject.proposal.eng.fl_str_mv |
Image processing File compression |
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Ilustraciones |
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2022 |
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2022-10-27T20:08:54Z |
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2022 |
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Universidad Nacional de Colombia |
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Abbas, H. (n.d.). Neural model for Karhunen-Loéve transform with application to adaptive image compression. Abu-El-Haija, S., Kothari, N., Lee, J., Natsev, P., Toderici, G., Varadarajan, B., & Vijayanarasimhan, S. (2016). YouTube-8M: A Large-Scale Video Classification Benchmark. http://arxiv.org/abs/1609.08675 Ahlswede, R., Ahlswede, A., Althöfer, I., Deppe, C., & Tamm, U. (2014). LZW Data compression. Foundations in Signal Processing, Communications and Networking, 10(02), 9–38. https://doi.org/10.1007/978-3-319-05479-7_2 Akyazi, P., & Ebrahimi, T. (2019). Learning-Based Image Compression using Convolutional Autoencoder and Wavelet Decomposition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2019-June, 1–5. Antoine, J.-P. (2003). Wavelet Transforms and Their ApplicationsWavelet Transforms and Their Applications , Lokenath Debnath , Birkhäuser, Boston, 2002. $79.95 (565 pp.). ISBN 0-8176-4204- 8 . Physics Today, 56(4), 68–68. https://doi.org/10.1063/1.1580056 AVIF for Next-Generation Image Coding | by Netflix Technology Blog | Netflix TechBlog. (n.d.). Retrieved June 18, 2022, from https://netflixtechblog.com/avif-for-next-generation-image-coding- b1d75675fe4 Ballé, J., Laparra, V., & Simoncelli, E. P. (2017). End-to-end optimized image compression. 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings. Cai, C., Lu, G., Hu, Q., Chen, L., & Gao, Z. (2019). Efficient learning based sub-pixel image compression. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2019-June. Cheng, Z., Sun, H., Takeuchi, M., & Katto, J. (2018). Deep Convolutional AutoEncoder-based Lossy Image Compression. 2018 Picture Coding Symposium, PCS 2018 - Proceedings, 253–257. https://doi.org/10.1109/PCS.2018.8456308 Chest X-Ray Images (Pneumonia) | Kaggle. (n.d.). Retrieved June 19, 2022, from https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia?resource=download Chua, L., & Lin, T. (1988). A neural network approach to transform image coding. Cramer, C., Gelenbe, E., & Bakircioglu, H. (1996). Video compression with random neural networks. Proceedings of International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing, NICROSP, 476–484. https://doi.org/10.1109/nicrsp.1996.542792 Daugman, J. G. (1988). Complete Discrete 2-D Gabor Transforms by Neural Networks for Image Analysis and Compression. IEEE Transactions on Acoustics, Speech, and Signal Processing, 36(7), 1169–1179. https://doi.org/10.1109/29.1644 Dhawale, N. (2015). Implementation of Huffman algorithm and study for optimization. Proceedings - 2014 IEEE International Conference on Advances in Communication and Computing Technologies, ICACACT 2014. https://doi.org/10.1109/EIC.2015.7230711 Du, B., Yuang, D., & Zhang, H. (2022). Collaborative image compression and classification with multi- task learning for visual Internet of Things. Electronics, A. K., & Paper, W. (n.d.). 8k: the next level in imaging. 1–15. Gardner, M. W., & Dorling, S. R. (1998). Artificial neural networks (the multilayer perceptron) - a review of applications in the atmospheric sciences. Atmospheric Environment, 32(14–15), 2627–2636. https://doi.org/10.1016/S1352-2310(97)00447-0 Gelenbe, E. (1989). Random Neural Networks with Negative and Positive Signals and Product Form Solution. Neural Computation, 1(4), 502–510. https://doi.org/10.1162/neco.1989.1.4.502 Gelenbe, E., & Sungur, M. (1994). Random network learning and image compression. IEEE International Conference on Neural Networks - Conference Proceedings, 6, 3996–3999. https://doi.org/10.1109/icnn.1994.374852 GitHub - Netflix/vmaf: Perceptual video quality assessment based on multi-method fusion. (n.d.). Retrieved June 18, 2022, from https://github.com/Netflix/vmaf Hai, F., Hussain, K. F., & Gelenbe, E. (2001). Video compression with wavelets and random neural network approximations. Horé, A., & Ziou, D. (2010). Image quality metrics: PSNR vs. SSIM. Proceedings - International Conference on Pattern Recognition, August, 2366–2369. https://doi.org/10.1109/ICPR.2010.579 LeCun, Y., & Bengio, Y. (2015). Deep Learning, Nature, vol. 521, no. 7553, p. 436. Melegati, J., Wang, X., & Abrahams, P. (2019). Hypotheses Engineering: First Essential Steps of Experiment-Driven Software Development. Rippel, O., & Bourdev, L. (2017). Real-time adaptive image compression,. S. Golomb. (1966). Run-length encodings (Corresp.),. 6–8. Sambasivan, N., Kapania, S., & Highfll, H. (2021). Everyone wants to do the model work, not the data work: Data cascades in high-stakes ai. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3411764.3445518 Si, Z., & Shen, K. (2016). Research on the WebP image format. Lecture Notes in Electrical Engineering, 369, 271–277. https://doi.org/10.1007/978-981-10-0072-0_35 Skodras, A., Christopoulos, C., & Ebrahimi, T. (2001). The JPEG 2000 still image compression standard. IEEE Signal Processing Magazine, 18(5), 36–58. https://doi.org/10.1109/79.952804 Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2015). Striving for simplicity: The all convolutional net. 3rd International Conference on Learning Representations, ICLR 2015 - Workshop Track Proceedings, 1–14. Theis, L., Shi, W., Cunningham, A., & Huszár, F. (2017). Lossy image compression with compressive autoencoders. 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings, 1–19. VMAF: The Journey Continues. by Zhi Li, Christos Bampis, Julie... | by Netflix Technology Blog | Netflix TechBlog. (n.d.). Retrieved June 18, 2022, from https://netflixtechblog.com/vmaf-the-journey- continues-44b51ee9ed12 Wallace, G. K. (1992). The JPEG still Picture Compression Standard. Architecture, 38(1). Wang, Y., Wang, L., Yang, J., An, W., & Guo, Y. (2019). Flickr1024: A large-scale dataset for stereo image super-resolution. Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 3852–3857. https://doi.org/10.1109/ICCVW.2019.00478 Xu, B., Wang, N., Chen, T., & Li, M. (2015). Empirical Evaluation of Rectified Activations in Convolutional Network. http://arxiv.org/abs/1505.00853 Yamanaka, J., Kuwashima, S., & Kurita, T. (2017). Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10635 LNCS, 217–225. https://doi.org/10.1007/978-3-319-70096-0_23 Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2018). Places: A 10 Million Image Database for Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1452–1464. https://doi.org/10.1109/TPAMI.2017.2723009 Zhou, L., Cai, C., Gao, Y., Su, S., & Wu, J. (2018). Variational autoencoder for low bit-rate image compression. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2018-Janua, 2617–2620. |
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Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Branch Bedoya, John Willian112eaa0bbeeaeb0d3d14dfe15d672a15600Varas González, Mario63ab3847f57c408fd6aafb66f7775f6d2022-10-27T20:08:54Z2022-10-27T20:08:54Z2022https://repositorio.unal.edu.co/handle/unal/82524Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/IlustracionesEn los últimos años, el tráfico en internet ha estado mayormente dominado por aplicaciones relacionadas con archivos de imagen y vídeo, especialmente servicios de streaming de contenido y aplicaciones de distribución de video bajo demanda. Más de tres cuartas partes del tráfico total de internet corresponden a archivos de imagen y vídeo. Que estas tareas sean lo más eficientes posible repercute directamente en la experiencia de uso que tengan los usuarios y en la calidad del servicio prestado. Preservar la calidad de esta experiencia de usuario es el principal objetivo en el desarrollo de estos sistemas de compresión, así como el punto donde estos sistemas más pueden flaquear. Es por ello que minimizar la distorsión o pérdida de información generada en el proceso de compresión de un archivo es algo prioritario y un asunto que ha tratado de abordarse desde diversas perspectivas y métodos a lo largo de la historia. El presente trabajo se centra en aquellas propuestas de reciente publicación donde el aprendizaje profundo o Deep Learning juega un papel principal en este proceso, proponiendo un método basado en redes neuronales para enfrentar el problema de compresión de archivos de imagen, mostrando la investigación llevada a cabo, el desarrollo del método y su puesta a prueba. (texto tomado de la fuente)In recent years, Internet traffic has been largely dominated by applications related to image and video files, especially content streaming services and video-on-demand distribution applications. Today, more than three quarters of all Internet traffic is image and video files. Making these tasks as efficient as possible has a direct impact on the user experience and the quality of the service provided. Preserving the quality of this user experience is the main objective in the development of these compression systems, as well as the point where these systems can falter the most. That is why minimizing the distortion or loss of information generated in the file compression process is a priority and an issue that has been addressed from various perspectives and methods throughout history. This project focuses on those recently published proposals where Deep Learning plays a major role in this process, proposing a method based on neural networks to address the problem of image files compression, showing the research carried out, the development of the method and its testingMaestríaMagíster en Ingeniería - Ingeniería de SistemasÁrea Curricular de Ingeniería de Sistemas e Informáticaxv, 170 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - Ingeniería de SistemasDepartamento de la Computación y la DecisiónFacultad de MinasMedellínUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresProcesamiento de imágenesDeep Learningfile compressionCompresión de archivosProcesamiento de imágenesImage processingFile compressionMétodo de compresión de archivos de imagen usando técnicas de deep learningImage files compression method using deep learning techniquesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAbbas, H. (n.d.). Neural model for Karhunen-Loéve transform with application to adaptive image compression.Abu-El-Haija, S., Kothari, N., Lee, J., Natsev, P., Toderici, G., Varadarajan, B., & Vijayanarasimhan, S. (2016). YouTube-8M: A Large-Scale Video Classification Benchmark. http://arxiv.org/abs/1609.08675Ahlswede, R., Ahlswede, A., Althöfer, I., Deppe, C., & Tamm, U. (2014). LZW Data compression. Foundations in Signal Processing, Communications and Networking, 10(02), 9–38. https://doi.org/10.1007/978-3-319-05479-7_2Akyazi, P., & Ebrahimi, T. (2019). Learning-Based Image Compression using Convolutional Autoencoder and Wavelet Decomposition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2019-June, 1–5.Antoine, J.-P. (2003). Wavelet Transforms and Their ApplicationsWavelet Transforms and Their Applications , Lokenath Debnath , Birkhäuser, Boston, 2002. $79.95 (565 pp.). ISBN 0-8176-4204- 8 . Physics Today, 56(4), 68–68. https://doi.org/10.1063/1.1580056AVIF for Next-Generation Image Coding | by Netflix Technology Blog | Netflix TechBlog. (n.d.). Retrieved June 18, 2022, from https://netflixtechblog.com/avif-for-next-generation-image-coding- b1d75675fe4Ballé, J., Laparra, V., & Simoncelli, E. P. (2017). End-to-end optimized image compression. 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings.Cai, C., Lu, G., Hu, Q., Chen, L., & Gao, Z. (2019). Efficient learning based sub-pixel image compression. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2019-June.Cheng, Z., Sun, H., Takeuchi, M., & Katto, J. (2018). Deep Convolutional AutoEncoder-based Lossy Image Compression. 2018 Picture Coding Symposium, PCS 2018 - Proceedings, 253–257. https://doi.org/10.1109/PCS.2018.8456308Chest X-Ray Images (Pneumonia) | Kaggle. (n.d.). Retrieved June 19, 2022, from https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia?resource=downloadChua, L., & Lin, T. (1988). A neural network approach to transform image coding.Cramer, C., Gelenbe, E., & Bakircioglu, H. (1996). Video compression with random neural networks. Proceedings of International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing, NICROSP, 476–484. https://doi.org/10.1109/nicrsp.1996.542792Daugman, J. G. (1988). Complete Discrete 2-D Gabor Transforms by Neural Networks for Image Analysis and Compression. IEEE Transactions on Acoustics, Speech, and Signal Processing, 36(7), 1169–1179. https://doi.org/10.1109/29.1644Dhawale, N. (2015). Implementation of Huffman algorithm and study for optimization. Proceedings - 2014 IEEE International Conference on Advances in Communication and Computing Technologies, ICACACT 2014. https://doi.org/10.1109/EIC.2015.7230711Du, B., Yuang, D., & Zhang, H. (2022). Collaborative image compression and classification with multi- task learning for visual Internet of Things.Electronics, A. K., & Paper, W. (n.d.). 8k: the next level in imaging. 1–15.Gardner, M. W., & Dorling, S. R. (1998). Artificial neural networks (the multilayer perceptron) - a review of applications in the atmospheric sciences. Atmospheric Environment, 32(14–15), 2627–2636. https://doi.org/10.1016/S1352-2310(97)00447-0Gelenbe, E. (1989). Random Neural Networks with Negative and Positive Signals and Product Form Solution. Neural Computation, 1(4), 502–510. https://doi.org/10.1162/neco.1989.1.4.502Gelenbe, E., & Sungur, M. (1994). Random network learning and image compression. IEEE International Conference on Neural Networks - Conference Proceedings, 6, 3996–3999. https://doi.org/10.1109/icnn.1994.374852GitHub - Netflix/vmaf: Perceptual video quality assessment based on multi-method fusion. (n.d.). Retrieved June 18, 2022, from https://github.com/Netflix/vmafHai, F., Hussain, K. F., & Gelenbe, E. (2001). Video compression with wavelets and random neural network approximations.Horé, A., & Ziou, D. (2010). Image quality metrics: PSNR vs. SSIM. Proceedings - International Conference on Pattern Recognition, August, 2366–2369. https://doi.org/10.1109/ICPR.2010.579LeCun, Y., & Bengio, Y. (2015). Deep Learning, Nature, vol. 521, no. 7553, p. 436.Melegati, J., Wang, X., & Abrahams, P. (2019). Hypotheses Engineering: First Essential Steps of Experiment-Driven Software Development.Rippel, O., & Bourdev, L. (2017). Real-time adaptive image compression,.S. Golomb. (1966). Run-length encodings (Corresp.),. 6–8.Sambasivan, N., Kapania, S., & Highfll, H. (2021). Everyone wants to do the model work, not the data work: Data cascades in high-stakes ai. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3411764.3445518Si, Z., & Shen, K. (2016). Research on the WebP image format. Lecture Notes in Electrical Engineering, 369, 271–277. https://doi.org/10.1007/978-981-10-0072-0_35Skodras, A., Christopoulos, C., & Ebrahimi, T. (2001). The JPEG 2000 still image compression standard. IEEE Signal Processing Magazine, 18(5), 36–58. https://doi.org/10.1109/79.952804Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2015). Striving for simplicity: The all convolutional net. 3rd International Conference on Learning Representations, ICLR 2015 - Workshop Track Proceedings, 1–14.Theis, L., Shi, W., Cunningham, A., & Huszár, F. (2017). Lossy image compression with compressive autoencoders. 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings, 1–19.VMAF: The Journey Continues. by Zhi Li, Christos Bampis, Julie... | by Netflix Technology Blog | Netflix TechBlog. (n.d.). Retrieved June 18, 2022, from https://netflixtechblog.com/vmaf-the-journey- continues-44b51ee9ed12Wallace, G. K. (1992). The JPEG still Picture Compression Standard. Architecture, 38(1).Wang, Y., Wang, L., Yang, J., An, W., & Guo, Y. (2019). Flickr1024: A large-scale dataset for stereo image super-resolution. Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 3852–3857. https://doi.org/10.1109/ICCVW.2019.00478Xu, B., Wang, N., Chen, T., & Li, M. (2015). Empirical Evaluation of Rectified Activations in Convolutional Network. http://arxiv.org/abs/1505.00853Yamanaka, J., Kuwashima, S., & Kurita, T. (2017). Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10635 LNCS, 217–225. https://doi.org/10.1007/978-3-319-70096-0_23Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2018). Places: A 10 Million Image Database for Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1452–1464. https://doi.org/10.1109/TPAMI.2017.2723009Zhou, L., Cai, C., Gao, Y., Su, S., & Wu, J. (2018). Variational autoencoder for low bit-rate image compression. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2018-Janua, 2617–2620.InvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/82524/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL712484.2022.pdf712484.2022.pdfTesis Maestría en Ingeniería - Ingeniería de Sistemasapplication/pdf76078723https://repositorio.unal.edu.co/bitstream/unal/82524/2/712484.2022.pdfbddae033e360d43dffcc2124d1eb6847MD52THUMBNAIL712484.2022.pdf.jpg712484.2022.pdf.jpgGenerated Thumbnailimage/jpeg4322https://repositorio.unal.edu.co/bitstream/unal/82524/3/712484.2022.pdf.jpg342fb44135a51ce689c963d3dc801cf8MD53unal/82524oai:repositorio.unal.edu.co:unal/825242024-08-12 02:00:28.155Repositorio Institucional Universidad Nacional de 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