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On the use of deep learning and parallelism techniques to significantly reduce the HEVC intra-coding time


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Título :
On the use of deep learning and parallelism techniques to significantly reduce the HEVC intra-coding time
Autor :
Galiano, Vicente  
Migallon, Hector  
Martínez-Rach, Miguel Onofre  
López Granado, Otoniel Mario  
Malumbres, Manuel P
Editor :
Springer
Departamento:
Departamentos de la UMH::Ingeniería de Computadores
Fecha de publicación:
2022-09-03
URI :
https://hdl.handle.net/11000/32258
Resumen :
It is well-known that each new video coding standard signifcantly increases in computational complexity with respect to previous standards, and this is particularly true for the HEVC and VVC video coding standards. The development of techniques for reducing the required complexity without afecting the rate/distortion (R/D) performance is therefore always a topic of intense research interest. In this paper, we propose a combination of two powerful techniques, deep learning and parallel computing, to signifcantly reduce the complexity of the HEVC encoding engine. Our experimental results show that a combination of deep learning to reduce the CTU partitioning complexity with parallel strategies based on frame partitioning is able to achieve speedups of up to 26× when 16 threads are used. The R/D penalty in terms of the BD-BR metric depends on the video content, the compression rate and the number of OpenMP threads, and was consistently between 0.35 and 10% for the video sequence test set used in our experiments
Palabras clave/Materias:
CNN
Deep learning
HEVC
Deep learning
Parallel processing
Área de conocimiento :
CDU: Ciencias aplicadas: Ingeniería. Tecnología
Tipo documento :
application/pdf
Derechos de acceso:
info:eu-repo/semantics/closedAccess
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
DOI :
https://doi.org/10.1007/s11227-022-04764-1
Aparece en las colecciones:
Artículos Ingeniería de computadores



Creative Commons La licencia se describe como: Atribución-NonComercial-NoDerivada 4.0 Internacional.