Online Efficient Bio‐Medical Video Transcoding on MPSoCs Through Content‐Aware Workload Allocation
Arman Iranfar1,a, Ali Pahlevan1,b, Marina Zapater1,c, Martin Žagar2,e, Mario Kovač2,f and David Atienza1,d
1Embedded Systems Laboratory (ESL), Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland
aarman.iranfar@epfl.ch
bali.pahlevan@epfl.ch
cmarina.zapater@epfl.ch
ddavid.atienza@epfl.ch
2Faculty of Electrical Engineering and Computing Univeristy of Zagreb, Croatia
emartin.zager@fer.hr
fmario.kovac@fer.hr
ABSTRACT
Bio‐medical image processing in the field of telemedicine, and in particular the definition of systems that allow medical diagnostics in a collaborative and distributed way is experiencing an undeniable growth. Due to the high quality of bio‐medical videos and the subsequent large volumes of data generated, to enable medical diagnosis on‐the‐go it is imperative to efficiently transcode and stream the stored videos on real time, without quality loss. However, online video transcoding is a high‐demanding computationally‐intensive task and its efficient management in Multiprocessor Systems‐on‐Chip (MPSoCs) poses an important challenge. In this work, we propose an efficient motion‐ and texture‐aware frame‐level parallelization approach to enable online medical imaging transcoding on MPSoCs for next generation video encoders. By exploiting the unique characteristics of bio‐medical videos and the medical procedure that enable diagnosis, we split frames into tiles based on their motion and texture, deciding the most adequate level of parallelization. Then, we employ the available encoding parameters to satisfy the required video quality and compression. Moreover, we propose a new fast motion search algorithm for bio‐medical videos that allows to drastically reduce the computational complexity of the encoder, thus achieving the frame rates required for online transcoding. Finally, we heuristically allocate the threads to the most appropriate available resources and set the operating frequency of each one. We evaluate our work on an enterprise multicore server achieving online medical imaging with 1.6x higher throughput and 44% less power consumption when compared to the state‐of‐the‐art techniques.
