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On the use of deep learning and parallelism techniques to signifcantly reduce the HEVC intra‑coding time


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Title:
On the use of deep learning and parallelism techniques to signifcantly reduce the HEVC intra‑coding time
Authors:
Galiano, Vicente  
Migallon, Hector  
Martínez-Rach, Miguel Onofre  
López Granado, Otoniel Mario  
Perez Malumbres, Manuel  
Editor:
Springer
Department:
Departamentos de la UMH::Ingeniería de Computadores
Issue Date:
2022-08
URI:
https://hdl.handle.net/11000/30426
Abstract:
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
Keywords/Subjects:
CNN
Deep learning
HEVC
Deep learning
Parallel processing
Slices
Video coding
Knowledge area:
CDU: Ciencias aplicadas: Ingeniería. Tecnología
Type of document:
application/pdf
Access rights:
info:eu-repo/semantics/openAccess
DOI:
https://doi.org/10.1007/s11227-022-04764-1
Appears in Collections:
Artículos Ingeniería de computadores



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