Baichuan Su

dblp:227/8072 · DBLP profile ↗
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2ranked-venue papers
2as first author
1since 2021 · last 2023
0000-0002-8323-8668ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 2 · 2 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
2 papers
Image and video coding · 100%
Computer architecture, parallel and distributed computing, and storage systems
2 papers
GPUs and heterogeneous computing · 62% Energy-efficient computing · 31% Embedded and real-time systems · 6%

Topics — the 6 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video coding › video compression
video codec
1.022023
GPU Based High Definition Parallel Video Codec Optimization in Mobile Device · IEEE Trans. Mob. Comput. 2023
Poster: GPU based High Definition Parallel Video Codec Optimization in Mobile Device · MobiCom 2018
Image and video coding › video coding standards
H.264/AVC
0.712023
GPU Based High Definition Parallel Video Codec Optimization in Mobile Device · IEEE Trans. Mob. Comput. 2023
GPUs and heterogeneous computing
GPU computing
0.712023
GPU Based High Definition Parallel Video Codec Optimization in Mobile Device · IEEE Trans. Mob. Comput. 2023
GPUs and heterogeneous computing › GPU computing › GPU video coding
GPU-accelerated video encoding
0.312018
Poster: GPU based High Definition Parallel Video Codec Optimization in Mobile Device · MobiCom 2018
Energy-efficient computing › power management
mobile device energy
0.322023
GPU Based High Definition Parallel Video Codec Optimization in Mobile Device · IEEE Trans. Mob. Comput. 2023
Poster: GPU based High Definition Parallel Video Codec Optimization in Mobile Device · MobiCom 2018
Energy-efficient computing
power management
0.212023
GPU Based High Definition Parallel Video Codec Optimization in Mobile Device · IEEE Trans. Mob. Comput. 2023

Methods — techniques the papers use, named apart from their topics

h.264 · 1.3GPU parallelization · 1.3parallelization · 0.7CPU-GPU cooperation · 0.7
YearPublicationVenuePosition
2023 GPU Based High Definition Parallel Video Codec Optimization in Mobile Device
abstract
With the explosive growth of various intelligent device and the rapid development of wireless network communication technology, most people prefer to use video applications on smart devices. However, the main challenges when using video codec technology on mobile devices are: 1) The explosive growth of multimedia applications has caused the allocation of computing resources to become an important issue; 2) high power consumption and limited battery power; 3) high cpu utilization causes the system to be unresponsive. In this paper, aGPU basedHigh DefinitionParallelVideoCodec (GHPVC) is proposed, which is a low energy consumption and high efficient video codec on mobile devices. First, Frame Data Management model and Prediction Model Selector model are proposed in order to get higher data transmission efficiency and parallel execution efficiency. Second, a GPU based Parallel ME module is proposed because the ME module is the most power-consuming and computationally intensive module in video codec. The GHPVC is proposed on the basis of conforming to the H.264 standard. Moreover and experimentally evaluated for different GPU devices on different mobile devices. Experimental results show that compared with the existing H.264 scheme, the proposed GHPVC not only has significant improvement in codec performance, but also effectively reduces energy consumption and CPU utilization.
Baichuan Su, Bo Cheng 0001, Junliang Chen 0001
IEEE Trans. Mob. Comput.1
2018 Poster: GPU based High Definition Parallel Video Codec Optimization in Mobile Device
abstract
With the advances in wireless communication and the growing popularity of mobile devices, it has become rather normal to watch videos using mobile devices. However, there are severely challenges to using video codec on mobile devices, because: 1) insufficient computing resources, there is poor performance using mobile device ; 2) the limited battery capacity on mobile device; 3) CPU utilization is too high when using traditional video codec. In this paper, we proposed a GPU based High Definition Parallel Video Codec on mobile devices, which is an efficient video codec with the cooperation of CPU and GPU. The video codec system is fully compliant with the video codec h264 standard. Compared with the scheme using existing X264, the presented experimental results evaluated the GPU based video codec achieves appreciable improvements in FPS(frames per second), the energy consumption and utilization of CPU are reduced properly at the same time.
Baichuan Su, Bo Cheng 0001, Ming Wang 0002, Junliang Chen 0001
MobiCom1