Sparse In-loop Video Coding Restoration Method

Ilustraciones

Autores:: Salazar Herrera, Carlos Alberto

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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dc.title.eng.fl_str_mv	Sparse In-loop Video Coding Restoration Method
dc.title.translated.spa.fl_str_mv	Método para la restauración de video en el bucle del proceso de compresión
title	Sparse In-loop Video Coding Restoration Method
spellingShingle	Sparse In-loop Video Coding Restoration Method 000 - Ciencias de la computación, información y obras generales::003 - Sistemas 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Videos - Conservación y restauración Procesamiento digital de imágenes Video compression restoration sparse AV2 HEVC VVC QP
title_short	Sparse In-loop Video Coding Restoration Method
title_full	Sparse In-loop Video Coding Restoration Method
title_fullStr	Sparse In-loop Video Coding Restoration Method
title_full_unstemmed	Sparse In-loop Video Coding Restoration Method
title_sort	Sparse In-loop Video Coding Restoration Method
dc.creator.fl_str_mv	Salazar Herrera, Carlos Alberto
dc.contributor.advisor.none.fl_str_mv	Branch Bedoya, John Willian (Thesis advisor) Trujillo Uribe, Maria Patricia
dc.contributor.author.none.fl_str_mv	Salazar Herrera, Carlos Alberto
dc.contributor.researchgroup.spa.fl_str_mv	Gidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificial
dc.contributor.subjectmatterexpert.none.fl_str_mv	Trujillo Uribe, Maria Patricia
dc.contributor.orcid.spa.fl_str_mv	Salazar Herrera, arlos Alberto [0000000194098229]
dc.contributor.cvlac.spa.fl_str_mv	SALAZAR HERRERA, CARLOS ALBERTO
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::003 - Sistemas 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::003 - Sistemas 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Videos - Conservación y restauración Procesamiento digital de imágenes Video compression restoration sparse AV2 HEVC VVC QP
dc.subject.lemb.none.fl_str_mv	Videos - Conservación y restauración Procesamiento digital de imágenes
dc.subject.proposal.eng.fl_str_mv	Video compression restoration
dc.subject.proposal.none.fl_str_mv	sparse
dc.subject.proposal.spa.fl_str_mv	AV2 HEVC VVC QP
description	Ilustraciones
publishDate	2023
dc.date.issued.none.fl_str_mv	2023-07-09
dc.date.accessioned.none.fl_str_mv	2024-07-10T13:57:27Z
dc.date.available.none.fl_str_mv	2024-07-10T13:57:27Z
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
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dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
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url	https://repositorio.unal.edu.co/handle/unal/86427 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	Sparse Representations. 2009 Ahmed, N. ; Natarajan, T. ; Rao, K.R.: Discrete Cosine Transform. En: IEEE Transactions on Computers C-23 (1974), Nr. 1, p. 90–93 Antsiferova, Anastasia ; Lavrushkin, Sergey ; Smirnov, Maksim ; Gushchin, Aleksandr ; Vatolin, Dmitriy S. ; Kulikov, Dmitriy. Video compression dataset and benchmark of learning-based video-quality metrics. 2022 Barman, Nabajeet ; Martini, Maria G. ; Reznik, Yuriy: Revisiting Bjontegaard Delta Bitrate (BD-BR) Computation for Codec Compression Efficiency Comparison. En: Proceedings of the 1st Mile-High Video Conference. New York, NY, USA : Association for Computing Machinery, 2022 (MHV ’22). – ISBN 9781450392228, p. 113–114 Cai, T. T. ; Wang, Lie: Orthogonal matching pursuit for sparse signal recovery with noise. En: IEEE Transactions on Information Theory 57 (2011), 7, p. 4680–4688. –ISSN 00189448 Chen, Ching-Yeh ; Tsai, Chia-Yang ; Huang, Yu-Wen ; Yamakage, Tomoo ; Chong, In S. ; Fu, Chih-Ming ; Itoh, Takayuki ; Watanabe, Takashi ; Chujoh, Takeshi ; Karczewicz, Marta ; Lei, Shaw-Min: The adaptive loop filtering techniques in the HEVC standard. En: Tescher, Andrew G. (Ed.): Applications of Digital Image Processing XXXV Vol. 8499, 2012, p. 849913 Dai, Yuanying ; Liu, Dong ; Wu, Feng: A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding Ding, Dandan ; Chen, Guangyao ; Mukherjee, Debargha ; Joshi, Urvang ; Chen, Yue: A CNN-based In-loop Filtering Approach for AV1 Video Codec. En: 2019 Picture Coding Symposium (PCS), 2019, p. 1–5 DIng, Dandan ; Chen, Guangyao ; Mukherjee, Debargha ; Joshi, Urvang ; Chen, Yue: A progressive CNN in-loop filtering approach for inter frame coding. En: 2019 Picture Coding Symposium, PCS 2019 (2019). ISBN 9781728147048 Dong, Junshuo ; Wu, Lingda: Comparison and Simulation Study of the Sparse Representation Matching Pursuit Algorithm and the Orthogonal Matching Pursuit Algorithm. (2021), p. 317–320 Dong, Weisheng ; Shi, Guangming ; Li, Xin: Image deblurring with low-rank approximation structured sparse representation. En: 2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, AP-SIPA ASC 2012 (2012), p. 14–18. ISBN 9780615700502 Dong, Weisheng ; Zhang, Lei ; Shi, Guangming ; Li, Xin: Nonlocally centralized sparse representation for image restoration. En: IEEE Transactions on Image Processing 22 (2013), Nr. 4, p. 1620–1630. – ISSN 10577149 Dong, Weisheng ; Zhang, Lei ; Shi, Guangming ; Li, Xin: Nonlocally centralized sparse representation for image restoration. En: IEEE Transactions on Image Processing 22 (2013), Nr. 4, p. 1620–1630. – ISSN 10577149 Dong, Weisheng ; Zhang, Lei ; Shi, Guangming ; Wu, Xiaolin: Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. En: IEEE Transactions on Image Processing 20 (2011), Nr. 7, p. 1838–1857. – ISSN 10577149 Donoho, David L.: Compressed sensing. En: IEEE Transactions on Information Theory 52 (2006), Nr. 4, p. 1289–1306. – ISSN 00189448 Donoho, David L. ; Elad, Michael: Optimally sparse representation in general (nonorthogonal) dictionaries via 1 minimization. En: PNAS March 4 (2003), p. 2197–2202 Ebadi, Salehe E. ; Ones, Valia G. ; Izquierdo, Ebroul: UHD Video Super-Resolution Using Low-Rank and Sparse Decomposition. En: Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017 2018-Janua (2017), p. 1889–1897. ISBN 9781538610343 Ekstrom, Michael P.: Realizable Wiener Filtering in Two Dimensions. En: IEEE Transactions on Acoustics, Speech, and Signal Processing 30 (1982), p. 31–40. – ISSN 00963518 Elad, Michael ; Aharon, Michal: Image denoising via learned dictionaries and sparse representation. En: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1 (2006), p. 895–900. – ISBN 0769525970 Elad, Michael ; Bruckstein, Alfred M.: A generalized uncertainty principle and sparse representation in pairs of bases. En: IEEE Transactions on Information Theory 48 (2002), 9, p. 2558–2567. – ISSN 00189448 Han, Jingning ; Li, Bohan ; Mukherjee, Debargha ; Chiang, Ching-Han ; Chen, Cheng ; Su, Hui ; Parker, Sarah ; Joshi, Urvang ; Chen, Yue ; Wang, Yunqing ; Wilkins, Paul ; Xu, Yaowu ; Bankoski, James: A Technical Overview of AV1. (2020), 8 Han, Jingning ; Xu, Yaowu ; Mukherjee, Debargha: A butterfly structured design of the hybrid transform coding scheme. (2013), p. 17–20 Hastie, Trevor ; Martin, Robert T. ; Hastie, Wainwright ; Tibshirani, * ; Wainwright, *. Statistical Learning with Sparsity The Lasso and Generalizations Statistical Learning with Sparsity Ji, Hui ; Huang, Sibin ; Shen, Zuowei ; Xu, Yuhong: Robust Video Restoration by Joint Sparse and Low Rank Matrix Approximation. En: SIAM Journal on Imaging Sciences 4 (2011), Nr. 4, p. 1122–1142 Jia, Chuanmin ; Wang, Shiqi ; Zhang, Xinfeng ; Wang, Shanshe ; Liu, Jiaying ; Pu, Shiliang ; Ma, Siwei: Content-Aware Convolutional Neural Network for In-Loop Filtering in High Efficiency Video Coding. En: IEEE Transactions on Image Processing 28 (2019), p. 3343–3356. – ISSN 19410042 Kato, Toshiyuki ; Hino, Hideitsu ; Murata, Noboru: Sparse Coding Approach for Multi-Frame Image Super Resolution. (2014), p. 1–20 Kim, Jiwon ; Lee, Jung K. ; Lee, Kyoung M.: Accurate Image Super-Resolution Using Very Deep Convolutional Networks. En: CoRR abs/1511.04587 (2015) Kong, Lingyi ; Ding, Dandan ; Liu, Fuchang ; Mukherjee, Debargha ; Joshi, Urvang ; Chen, Yue: Guided CNN Restoration with Explicitly Signaled Linear Combination. En: Proceedings - International Conference on Image Processing, ICIP 2020- Octob (2020), p. 3379–3383. – ISBN 9781728163956 Lin, Liqun ; Yu, Shiqi ; Zhao, Tiesong ; Wang, Zhou: PEA265: Perceptual Assessment of Video Compression Artifacts. (2019) Mackiewicz, Andrzej ; Ratajczak, Waldemar: Principal Components Analysis (PCA). En: Computers & Geosciences 19 (1993), p. 303–342 Mairal, Julien ; Elad, Michael ; Sapiro, Guillermo: Sparse representation for color image restoration. En: IEEE Transactions on Image Processing 17 (2008), p. 53–69. ISSN 10577149 Mairal, Julien ; Sapiro, Guillermo ; Elad, Michael: Learning multiscale sparse representations for image and video restoration. En: Multiscale Modeling and Simulation (2008), Nr. 1, p. 214–241. – ISSN 15403467 Moorthy, Anush K. ; Bovik, Alan C. STATISTICS OF NATURAL IMAGE DISTORTIONS Mukherjee, Debargha ; Han, Jingning ; Bankoski, Jim ; Bultje, Ronald ; Grange, Adrian ; Koleszar, John ; Wilkins, Paul ; Xu, Yaowu: A Technical Overview of VP9 – The Latest Open-Source Video Codec. (2013), p. 1–17 O’Shea, Keiron ; Nash, Ryan: An Introduction to Convolutional Neural Networks. En: CoRR abs/1511.08458 (2015) Oxford: A Dictionary of Statistics. Oxford University Press, 2014. – ISBN 9780191758317 Reininger, Randall C. ; Gibson, Jerry D.: Distributions of the Two-Dimensional DCT Coefficients for Images. En: IEEE Transactions on Communications 31 (1983), p. 835–839. – ISSN 00906778 Saad, Michele A. ; Bovik, Alan C. ; Charrier, Christophe: DCT statistics model- based blind image quality assessment, 2011. – ISBN 9781457713033, p. 3093–3096 Sankaraiah, Yediga R. ; Varadarajan, Sourirajan: An effective image deblurring scheme using cluster based sparse representation. En: ASEAN Engineering Journal 11 (2021), Nr. 4, p. 16–28. – ISSN 25869159 Scetbon, Meyer ; Elad, Michael ; Milanfar, Peyman: Deep K-SVD denoising. En: IEEE Transactions on Image Processing 30 (2021), Nr. 8, p. 5944–5955. – ISSN 19410042 Schneider, Jens ; Sauer, Johannes ; Wien, Mathias: RDPlot – An Evaluation Tool for Video Coding Simulations. En: 2021 International Conference on Visual Communications and Image Processing (VCIP), 2021, p. 1–1 Segall, C A. ; Katsaggelos, Aggelos K. ; Molina, Rafael: Chapter 11 Super- resolution from compressed video. En: Book (2001), p. 1–32 Siekmann, Mischa ; Bosse, Sebastian ; Schwarz, Heiko ; Wiegand, Thomas: SEPARABLE WIENER FILTER BASED ADAPTIVE IN-LOOP FILTER FOR VIDEO CODING Image Processing Department Fraunhofer Institute for Telecommunications Valin, Jean-Marc: The Daala Directional Deringing Filter. En: CoRR abs/1602.05975 (2016) Wang, Z. ; Simoncelli, E.P. ; Bovik, A.C.: Multiscale structural similarity for image quality assessment. En: The Thrity-Seventh Asilomar Conference on Signals, Systems and Computers, 2003 Vol. 2, 2003, p. 1398–1402 Vol.2 Wang, Zhou ; Bovik, A.C. ; Sheikh, H.R. ; Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. En: IEEE Transactions on Image Processing 13 (2004), Nr. 4, p. 600–612 Wiener, Norbert: Extrapolation, interpolation, and smoothing of stationary time series with engineering applications. 1964. – ISBN 9780262730051 Y., Dodge: Gamma Distribution. New York, NY : Springer New York, 2008. – 215–216 p.. – ISBN 978–0–387–32833–1 Yang, Jianchao ; Wright, John ; Huang, Thomas S. ; Ma, Yi: Image super- resolution via sparse representation. En: IEEE Transactions on Image Processing 19 (2010), Nr. 11, p. 2861–2873. – ISSN 10577149 Zhu, Shujin ; Yu, Zekuan: Self-guided filter for image denoising. En: IET Image Processing 14 (2020), Nr. 11, p. 2561–2566
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Doctorado en Ingeniería - Sistemas
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Branch Bedoya, John Willian (Thesis advisor)34390fbb8320c8acc3e784d158767b53600Trujillo Uribe, Maria Patricia76d87fa6eeafe50af623fcb3414d3161Salazar Herrera, Carlos Albertof19aafafc1b3b0ac3caef37d8c36ec43Gidia: Grupo de Investigación YyDesarrollo en Inteligencia ArtificialTrujillo Uribe, Maria PatriciaSalazar Herrera, arlos Alberto [0000000194098229]SALAZAR HERRERA, CARLOS ALBERTO2024-07-10T13:57:27Z2024-07-10T13:57:27Z2023-07-09https://repositorio.unal.edu.co/handle/unal/86427Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/IlustracionesVideo in-loop restoration methods have traction much attention across the standardization groups for future video codecs AV2 1 and VVC 2. Primarily, because of potential benefits to compensate the artifacts generated during super-resolution scenarios and the effect of quan- tization process. Thus, new sophisticated learned-based algorithms have been proposed, in recent years, surpassing the classical switchable filter implementation in objective quality rate. However, CNN-based approaches requirements on computational cost and decoder complexity are still challenging. Therefore, we propose a low-complex learned-based method that leverages the solid and consistent sparse representation theory to exploit the spatial redundancy of frames. Our approach models the decoding residual, the distance between each reference and the respec- tive decoded frame. Furthermore, the proposed methods shrink the support of the sparse vector to two in order to control the restoration signal information. In addition, our method uses the Discrete Cosine Transform (DCT) orthogonal basis as a dictionary to exploit the statistical correlation between nonzero coefficients and the quantization level. Finally, we leverage the official and public available AV2 raw video dataset to compare our performance against the anchor AV2 codec through three objective visual quality metrics. The validation protocol includes benchmark data sets for the anchor and the restoration-enabled configurations. Our experimental results show a consistent restoration using sparse representation as well as an effective mechanism for sharing nonzero coefficients leveraging a Gaussian correlation. The experimental evaluation showed that our method has a 1%-2% gain regarding AV2, using SSIM and VMAF under similar bitrate conditions.Los métodos de restauración de vídeo en bucle han venido incrementando el intereste por parte los grupos de estandarización para los futuros códecs de vídeo AV2 y VVC. Esto principalmente debido a a los beneficios potenciales para compensar efectos no deseados en el video producidos durante los procesos de super-resolución y cuantización. Así, en los últimos años se han propuesto nuevos y sofisticados algoritmos basados en aprendizaje, que superan a la clásica implementación de filtros conmutables en cuanto a tasa de calidad objetiva. Sin embargo, los requisitos de los enfoques basados en CNN en cuanto a coste computacional y complejidad del descodificador siguen siendo un desafio. Por ello, proponemos un método de baja complejidad basado en aprendizaje, que aprovecha la sólida y consistente teoría de la representación dispersa para explotar la redundancia espacial de los fotogramas que componen un video. Nuestro enfoque modela el residuo de descodificación, la distancia entre cada referencia y el respectivo fotograma descodificado. Además, el método propuesto reduce el soporte del vector disperso a dos para controlar la información de la señal de restauración. Por otra parte, nuestro método utiliza la base ortogonal de la transformada discreta de coseno (DCT) como diccionario para explotar la correlación estadística entre los coeficientes distintos de cero y el nivel de cuantificación. Por último, aprovechamos el conjunto de datos de vídeo de AV2, oficial y público, para comparar nuestro rendimiento con el códec AV2 de referencia, mediante tres métricas objetivas de calidad visual.El protocolo de validación incluye conjuntos de datos de referencia para las funciones de anclaje y restauración. Nuestros resultados experimentales muestran una restauración coherente utilizando una representación dispersa así como un mecanismo eficaz para compartir coeficientes distintos de cero aprovechando una correlación gaussiana. La evaluación experimental mostró que nuestro método tiene una ganancia del 1%-2% con respecto a AV2, utilizando SSIM y VMAF en condiciones de bitrate similares.DoctoradoDoctor en IngenieríaAnálisis de videoÁrea Curricular de Ingeniería de Sistemas e Informática74 páginasapplication/pdfengUniversidad Nacional de ColombiaMedellín - Minas - Doctorado en Ingeniería - SistemasFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::003 - Sistemas000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresVideos - Conservación y restauraciónProcesamiento digital de imágenesVideo compressionrestorationsparseAV2HEVCVVCQPSparse In-loop Video Coding Restoration MethodMétodo para la restauración de video en el bucle del proceso de compresiónTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDSparse Representations. 2009Ahmed, N. ; Natarajan, T. ; Rao, K.R.: Discrete Cosine Transform. En: IEEE Transactions on Computers C-23 (1974), Nr. 1, p. 90–93Antsiferova, Anastasia ; Lavrushkin, Sergey ; Smirnov, Maksim ; Gushchin, Aleksandr ; Vatolin, Dmitriy S. ; Kulikov, Dmitriy. Video compression dataset and benchmark of learning-based video-quality metrics. 2022Barman, Nabajeet ; Martini, Maria G. ; Reznik, Yuriy: Revisiting Bjontegaard Delta Bitrate (BD-BR) Computation for Codec Compression Efficiency Comparison. En: Proceedings of the 1st Mile-High Video Conference. New York, NY, USA : Association for Computing Machinery, 2022 (MHV ’22). – ISBN 9781450392228, p. 113–114Cai, T. T. ; Wang, Lie: Orthogonal matching pursuit for sparse signal recovery with noise. En: IEEE Transactions on Information Theory 57 (2011), 7, p. 4680–4688. –ISSN 00189448Chen, Ching-Yeh ; Tsai, Chia-Yang ; Huang, Yu-Wen ; Yamakage, Tomoo ; Chong, In S. ; Fu, Chih-Ming ; Itoh, Takayuki ; Watanabe, Takashi ; Chujoh, Takeshi ; Karczewicz, Marta ; Lei, Shaw-Min: The adaptive loop filtering techniques in the HEVC standard. En: Tescher, Andrew G. (Ed.): Applications of Digital Image Processing XXXV Vol. 8499, 2012, p. 849913Dai, Yuanying ; Liu, Dong ; Wu, Feng: A Convolutional Neural Network Approach for Post-Processing in HEVC Intra CodingDing, Dandan ; Chen, Guangyao ; Mukherjee, Debargha ; Joshi, Urvang ; Chen, Yue: A CNN-based In-loop Filtering Approach for AV1 Video Codec. En: 2019 Picture Coding Symposium (PCS), 2019, p. 1–5DIng, Dandan ; Chen, Guangyao ; Mukherjee, Debargha ; Joshi, Urvang ; Chen, Yue: A progressive CNN in-loop filtering approach for inter frame coding. En: 2019 Picture Coding Symposium, PCS 2019 (2019). ISBN 9781728147048Dong, Junshuo ; Wu, Lingda: Comparison and Simulation Study of the Sparse Representation Matching Pursuit Algorithm and the Orthogonal Matching Pursuit Algorithm. (2021), p. 317–320Dong, Weisheng ; Shi, Guangming ; Li, Xin: Image deblurring with low-rank approximation structured sparse representation. En: 2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, AP-SIPA ASC 2012 (2012), p. 14–18. ISBN 9780615700502Dong, Weisheng ; Zhang, Lei ; Shi, Guangming ; Li, Xin: Nonlocally centralized sparse representation for image restoration. En: IEEE Transactions on Image Processing 22 (2013), Nr. 4, p. 1620–1630. – ISSN 10577149Dong, Weisheng ; Zhang, Lei ; Shi, Guangming ; Li, Xin: Nonlocally centralized sparse representation for image restoration. En: IEEE Transactions on Image Processing 22 (2013), Nr. 4, p. 1620–1630. – ISSN 10577149Dong, Weisheng ; Zhang, Lei ; Shi, Guangming ; Wu, Xiaolin: Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. En: IEEE Transactions on Image Processing 20 (2011), Nr. 7, p. 1838–1857. – ISSN 10577149Donoho, David L.: Compressed sensing. En: IEEE Transactions on Information Theory 52 (2006), Nr. 4, p. 1289–1306. – ISSN 00189448Donoho, David L. ; Elad, Michael: Optimally sparse representation in general (nonorthogonal) dictionaries via 1 minimization. En: PNAS March 4 (2003), p. 2197–2202Ebadi, Salehe E. ; Ones, Valia G. ; Izquierdo, Ebroul: UHD Video Super-Resolution Using Low-Rank and Sparse Decomposition. En: Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017 2018-Janua (2017), p. 1889–1897. ISBN 9781538610343Ekstrom, Michael P.: Realizable Wiener Filtering in Two Dimensions. En: IEEE Transactions on Acoustics, Speech, and Signal Processing 30 (1982), p. 31–40. – ISSN 00963518Elad, Michael ; Aharon, Michal: Image denoising via learned dictionaries and sparse representation. En: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1 (2006), p. 895–900. – ISBN 0769525970Elad, Michael ; Bruckstein, Alfred M.: A generalized uncertainty principle and sparse representation in pairs of bases. En: IEEE Transactions on Information Theory 48 (2002), 9, p. 2558–2567. – ISSN 00189448Han, Jingning ; Li, Bohan ; Mukherjee, Debargha ; Chiang, Ching-Han ; Chen, Cheng ; Su, Hui ; Parker, Sarah ; Joshi, Urvang ; Chen, Yue ; Wang, Yunqing ; Wilkins, Paul ; Xu, Yaowu ; Bankoski, James: A Technical Overview of AV1. (2020), 8Han, Jingning ; Xu, Yaowu ; Mukherjee, Debargha: A butterfly structured design of the hybrid transform coding scheme. (2013), p. 17–20Hastie, Trevor ; Martin, Robert T. ; Hastie, Wainwright ; Tibshirani, * ; Wainwright, *. Statistical Learning with Sparsity The Lasso and Generalizations Statistical Learning with SparsityJi, Hui ; Huang, Sibin ; Shen, Zuowei ; Xu, Yuhong: Robust Video Restoration by Joint Sparse and Low Rank Matrix Approximation. En: SIAM Journal on Imaging Sciences 4 (2011), Nr. 4, p. 1122–1142Jia, Chuanmin ; Wang, Shiqi ; Zhang, Xinfeng ; Wang, Shanshe ; Liu, Jiaying ; Pu, Shiliang ; Ma, Siwei: Content-Aware Convolutional Neural Network for In-Loop Filtering in High Efficiency Video Coding. En: IEEE Transactions on Image Processing 28 (2019), p. 3343–3356. – ISSN 19410042Kato, Toshiyuki ; Hino, Hideitsu ; Murata, Noboru: Sparse Coding Approach for Multi-Frame Image Super Resolution. (2014), p. 1–20Kim, Jiwon ; Lee, Jung K. ; Lee, Kyoung M.: Accurate Image Super-Resolution Using Very Deep Convolutional Networks. 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En: IET Image Processing 14 (2020), Nr. 11, p. 2561–2566InvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86427/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53ORIGINAL1115065522.2023.pdf1115065522.2023.pdfTesis de Doctorado en Ingeniería - Sistemasapplication/pdf39704326https://repositorio.unal.edu.co/bitstream/unal/86427/4/1115065522.2023.pdf243ef7f870b188ba682b6fec5a6b1bc2MD54THUMBNAIL1115065522.2023.pdf.jpg1115065522.2023.pdf.jpgGenerated Thumbnailimage/jpeg4061https://repositorio.unal.edu.co/bitstream/unal/86427/5/1115065522.2023.pdf.jpg36d73e90c2d06d540d3ed36dfe898f60MD55unal/86427oai:repositorio.unal.edu.co:unal/864272024-07-10 23:11:25.718Repositorio Institucional Universidad Nacional de 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

Sparse In-loop Video Coding Restoration Method

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