@InProceedings{OliveiraMJGSSEHTF:2021:LeEnVi,
               author = "Oliveira, Matheus Costa de and Martins, Luiz Gustavo Rodrigues and 
                         Jung, Henrique Costa and Guerin Junior, Nilson Donizete and Silva, 
                         Renam Castro da and Silva, Eduardo Peixoto Fernandes da and 
                         Espinoza, Bruno Luiggi Macchiavello and Hung, Edson Mintsu and 
                         Testoni, Vanessa and Freitas, Pedro Garcia",
          affiliation = "{University of Bras{\'{\i}}lia } and {University of 
                         Bras{\'{\i}}lia } and {University of Bras{\'{\i}}lia } and 
                         {University of Bras{\'{\i}}lia } and {Samsung R\&D Brazil } and 
                         {University of Bras{\'{\i}}lia } and {University of 
                         Bras{\'{\i}}lia } and {University of Bras{\'{\i}}lia } and 
                         {Samsung R\&D Brazil } and {Samsung R\&D Brazil}",
                title = "Learning-based End-to-End Video Compression Using Predictive 
                         Coding",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "neural coding, video coding, intra prediction, inter prediction, 
                         autoencoder.",
             abstract = "Driven by the growing demand for video applications, deep learning 
                         techniques have become alternatives for implementing end-to-end 
                         encoders to achieve applicable compression rates. Conventional 
                         video codecs exploit both spatial and temporal correlation. 
                         However, due to some restrictions (e.g. computational complexity), 
                         they are commonly limited to linear transformations and 
                         translational motion estimation. Autoencoder models open up the 
                         way for exploiting predictive end-to-end video codecs without such 
                         limitations. This paper presents an entire learning-based video 
                         codec that exploits spatial and temporal correlations. The 
                         presented codec extends the idea of P-frame prediction presented 
                         in our previous work. The architecture adopted for I-frame coding 
                         is defined by a variational autoencoder with non-parametric 
                         entropy modeling. Besides an entropy model parameterized by a 
                         hyperprior, the inter-frame encoder architecture has two other 
                         independent networks, responsible for motion estimation and 
                         residue prediction. Experimental results indicate that some 
                         improvements still have to be incorporated into our codec to 
                         overcome the all-intra coding set up regarding the traditional 
                         algorithms HEVC and VVC.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00030",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00030",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CG3KP",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CG3KP",
           targetfile = "Camera Ready Version - PDF eXpress.pdf",
        urlaccessdate = "2024, May 06"
}