Distributed memory parallel approaches for HEVC encoder

Migallón, Héctor; Galiano, Vicente; Piñol, Pablo; López-Granado, Otoniel; Malumbres, Manuel P.

doi:10.1007/s11227-016-1666-2

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…A full description of the algorithm can be found in [13]. In the current paper, the DMG-AI algorithm is tested on an heterogeneous memory framework, managed by the Message Passing Interface (MPI) [17], consisting on N computing nodes (distributed memory architecture).…”

Section: Frame-based Parallel Algorithmsmentioning

confidence: 99%

“…In [12], the authors propose to change the HEVC deblocking filter processing order, obtaining time savings of up to 37.93% over manycore processors, with a negligible loss in coding performance. In [13], the authors present a coarse grain parallelization of the HEVC encoder based on Groups of Pictures (GOPs), especially suited for distributed memory platforms. In [14], the authors compare the encoding performance of slices and tiles in HEVC.…”

Section: Introductionmentioning

confidence: 99%

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Frame-Based and Subpicture-Based Parallelization Approaches of the HEVC Video Encoder

et al. 2018

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Abstract:The most recent video coding standard, High Efficiency Video Coding (HEVC), is able to significantly improve the compression performance at the expense of a huge computational complexity increase with respect to its predecessor, H.264/AVC. Parallel versions of the HEVC encoder may help to reduce the overall encoding time in order to make it more suitable for practical applications. In this work, we study two parallelization strategies. One of them follows a coarse-grain approach, where parallelization is based on frames, and the other one follows a fine-grain approach, where parallelization is performed at subpicture level. Two different frame-based approaches have been developed. The first one only uses MPI and the second one is a hybrid MPI/OpenMP algorithm. An exhaustive experimental test was carried out to study the performance of both approaches in order to find out the best setup in terms of parallel efficiency and coding performance. Both frame-based and subpicture-based approaches are compared under the same hardware platform. Although subpicture-based schemes provide an excellent performance with high-resolution video sequences, scalability is limited by resolution, and the coding performance worsens by increasing the number of processes. Conversely, the proposed frame-based approaches provide the best results with respect to both parallel performance (increasing scalability) and coding performance (not degrading the rate/distortion behavior).

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Section: Frame-based Parallel Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Frame-Based and Subpicture-Based Parallelization Approaches of the HEVC Video Encoder

et al. 2018

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“…Table 1 characterizes the existing parallelization approaches for HEVC and AVC. In [6]- [8], process level parallelization was implemented by distributing the video temporally to different encoder instances in group of pictures (GOPs). Thread level parallelization was achieved with tiles and slices in [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Parallelization Scheme for HEVC Encoding on Multi-Computer Systems

Mercat

Ahovainio

Vanne

2022

2022 IEEE International Conference on Image Processing (ICIP)

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High Efficiency Video Coding (HEVC) sets the scene for economic video transmission and storage, but its inherent computational complexity calls for efficient parallelization techniques. This paper introduces and compares three different parallelization strategies for HEVC encoding on multi-computer systems: 1) spatial parallelization scheme, where input video frames are divided into slices and distributed among available computers; 2) temporal parallelization scheme, where input video is distributed among computers in groups of consecutive frames; 3) spatiotemporal parallelization scheme that combines the proposed spatial and temporal approaches. All these three schemes were benchmarked as part of the practical Kvazaar opensource HEVC encoder. Our experimental results on 2-5 computer configurations show that using the spatial scheme gives 1.65×-2.90× speedup at the cost of 4.16%-13.09% bitrate loss over a single-computer setup. The respective speedup with temporal parallelization is 1.86×-3.26× without any coding overhead. The spatio-temporal scheme with 2 slices was shown to offer the best load-balancing with 1.81×-3.55× speedups and a constant coding loss of 4.16%.

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Shared Memory Tile-Based vs Hybrid Memory GOP-Based Parallel Algorithms for HEVC Encoder

Migallón

López-Granado

Galiano

et al. 2016

Algorithms and Architectures for Parallel Processing

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Distributed memory parallel approaches for HEVC encoder

Cited by 6 publications

References 10 publications

Frame-Based and Subpicture-Based Parallelization Approaches of the HEVC Video Encoder

Frame-Based and Subpicture-Based Parallelization Approaches of the HEVC Video Encoder

Spatio-Temporal Parallelization Scheme for HEVC Encoding on Multi-Computer Systems

Shared Memory Tile-Based vs Hybrid Memory GOP-Based Parallel Algorithms for HEVC Encoder

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