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You searched for subject:(Predictive video encoding). Showing records 1 – 2 of 2 total matches.

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Universidade do Rio Grande do Sul

1. Vizzotto, Bruno Boessio. Algoritmos para o módulo de controle de taxa de codificação de vídeos multivistas do padrão H.264/MVC.

Degree: 2012, Universidade do Rio Grande do Sul

Esta dissertação de mestrado apresenta um novo esquema de controle de taxa hierárquico – HRC – para o padrão MVC – extensão para vídeos de múltiplas vistas do padrão H.264 – com objetivo de melhorar o aproveitamento da largura de banda oferecida por um canal entregando o vídeo comprimido com a melhor qualidade possível. Este esquema de controle de taxa hierárquico foi concebido para controlar de forma conjunta os níveis de quadro e de unidades básicas (BU). O esquema proposto explora a correlação existente entre as distribuições das taxas de bits em quadros vizinhos para predizer de forma eficiente o comportamento dos futuras bitrates através da aplicação de um controle preditivo baseado em modelos – MPC – que define uma ação de controle apropriada sobre as ações de adaptação do parâmetro de quantização (QP). Para prover um ajuste em granularidade fina, o QP é adicionalmente adaptado internamente para cada quadro por um processo de decisão de Markov (MDP) implementado em nível de BU capaz de considerar mapas com Regiões de Interesse (RoI). Um retorno acoplado aos dois níveis supracitados é realizado para garantir a consistência do sistema. Aprendizagem por Reforço é utilizada para atualizar os parâmetros do Controle Preditivo baseado em Modelos e do processo de decisão de Markov. Resultados experimentais mostram a superioridade da utilização do esquema de controle proposto, comparado às soluções estado-da-arte, tanto em termos de precisão na alocação de bits quanto na otimização da razão taxa-distorção, entregando um vídeo de maior qualidade visual nos níveis de quadros e de BUs.

This master thesis presents a novel Hierarchical Rate Control – HRC – for the Multiview Video Coding standard targeting an increased bandwidth usage and high video quality. The HRC is designed to jointly address the rate control at both framelevel and Basic Unit (BU)-level. This scheme is able to exploit the bitrate distribution correlation with neighboring frames to efficiently predict the future bitrate behavior by employing a Model Predictive Control that defines a proper control action through QP (Quantization Parameter) adaptation. To provide a fine-grained tuning, the QP is further adapted within each frame by a Markov Decision Process implemented at BU-level able to take into consideration a map of the Regions of Interest. A coupled frame/BU-level feedback is performed in order to guarantee the system consistency. A Reinforcement Learning method is responsible for updating the Model Predictive Control and the Markov Decision Process parameters. Experimental results show the superiority of the Hierarchical Rate Control compared to state-of-the-art solutions, in terms of bitrate allocation accuracy and rate-distortion, while delivering smooth video quality at both frame and Basic Unit levels.

Advisors/Committee Members: Bampi, Sergio.

Subjects/Keywords: Video encoding; Microeletrônica; Multiview video coding; Vídeo digital; Codificacao : Video digital; Rate control; Model predictive control; Markov decision process; Reinforcement learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Vizzotto, B. B. (2012). Algoritmos para o módulo de controle de taxa de codificação de vídeos multivistas do padrão H.264/MVC. (Thesis). Universidade do Rio Grande do Sul. Retrieved from http://hdl.handle.net/10183/54865

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Vizzotto, Bruno Boessio. “Algoritmos para o módulo de controle de taxa de codificação de vídeos multivistas do padrão H.264/MVC.” 2012. Thesis, Universidade do Rio Grande do Sul. Accessed September 17, 2019. http://hdl.handle.net/10183/54865.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Vizzotto, Bruno Boessio. “Algoritmos para o módulo de controle de taxa de codificação de vídeos multivistas do padrão H.264/MVC.” 2012. Web. 17 Sep 2019.

Vancouver:

Vizzotto BB. Algoritmos para o módulo de controle de taxa de codificação de vídeos multivistas do padrão H.264/MVC. [Internet] [Thesis]. Universidade do Rio Grande do Sul; 2012. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/10183/54865.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vizzotto BB. Algoritmos para o módulo de controle de taxa de codificação de vídeos multivistas do padrão H.264/MVC. [Thesis]. Universidade do Rio Grande do Sul; 2012. Available from: http://hdl.handle.net/10183/54865

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

2. Somoye, Idris Olansile. GPU accelerated adaptive compressed sensing.

Degree: MS, Electrical and Computer Engineering, 2016, Georgia Tech

There are presently image sensors based around compressed sensing that apply the fundamental theory to video acquisition; however, these imagers require specialized hardware modules that are not widely available and therefore are not currently practical for video sensing. To deliver a practical image sensor that applies compressive sensing, I propose an imaging system based on a GPU and an off-the-shelf conventional image sensor that takes advantage of parallel computations for efficient transforming of data to the compressed sensing domain. This imaging system, by taking advantage of GPU processing along with straightforward communication methods between the host and the GPU, easily accommodates algorithms that rapidly change the sensing basis, making compressed sensing more applicable despite the general lack of hardware. Measurement results show that the GPU based compressive sensing imaging system delivers a viable and practical imager that is able to quickly compress images, providing a real-time video encoder for low power systems. Advisors/Committee Members: Chatterjee, Abhijit (advisor), Raychowdhury, Arijit (committee member), Romberg, Justin (committee member).

Subjects/Keywords: GPU; Compressed sensing; GPGPU; Predictive video encoding

…common in images. An application that implements ACS, called predictive video encoding (… …benefit being that predictive video encoding provides a real-time video encoding method… …itself. The objective of using predictive video encoding in this thesis is to implement an… …sensors based around compressed sensing that apply the fundamental theory to video acquisition… …therefore are not currently practical for video sensing. To deliver a practical image sensor that… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Somoye, I. O. (2016). GPU accelerated adaptive compressed sensing. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/56379

Chicago Manual of Style (16th Edition):

Somoye, Idris Olansile. “GPU accelerated adaptive compressed sensing.” 2016. Masters Thesis, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/56379.

MLA Handbook (7th Edition):

Somoye, Idris Olansile. “GPU accelerated adaptive compressed sensing.” 2016. Web. 17 Sep 2019.

Vancouver:

Somoye IO. GPU accelerated adaptive compressed sensing. [Internet] [Masters thesis]. Georgia Tech; 2016. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/56379.

Council of Science Editors:

Somoye IO. GPU accelerated adaptive compressed sensing. [Masters Thesis]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/56379

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