Home > Articles > Published articles > GPU-oriented architecture for an end-to-end image/video codec based on JPEG2000 |
Date: | 2020 |
Abstract: | Modern image and video compression standards employ computationally intensive algorithms that provide advanced features to the coding system. Current standards often need to be implemented in hardware or using expensive solutions to meet the real-time requirements of some environments. Contrarily to this trend, this paper proposes an end-to-end codec architecture running on inexpensive Graphics Processing Units (GPUs) that is based on, though not compatible with, the JPEG2000 international standard for image and video compression. When executed in a commodity Nvidia GPU, it achieves real time processing of 12K video. The proposed S/W architecture utilizes four CUDA kernels that minimize memory transfers, use registers instead of shared memory, and employ a double-buffer strategy to optimize the streaming of data. The analysis of throughput indicates that the proposed codec yields results at least 10× superior on average to those achieved with JPEG2000 implementations devised for CPUs, and approximately 4× superior to those achieved with hardwired solutions of the HEVC/H. 265 video compression standard. |
Grants: | Ministerio de Economía y Competitividad TIN2017-84553-C2-1-R Ministerio de Economía y Competitividad RTI2018-095287-B-I00 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-463 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-313 |
Rights: | Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. |
Language: | Anglès |
Document: | Article ; recerca ; Versió acceptada per publicar |
Subject: | Wavelet-based image coding ; High-throughput image coding ; JPEG2000 ; GPU ; CUDA |
Published in: | IEEE Access, Vol. 8 (April 2020) , p. 68474-68487, ISSN 2169-3536 |
Postprint 15 p, 550.1 KB |
14 p, 5.6 MB |