A Novel Fast Multiple Reference Frames Selection Method for H.264/AVC

2009 ◽  
Author(s):  
Kan Chang ◽  
Aidong Men ◽  
Wenhao Zhang
Keyword(s):  
Reference Frames ◽  
Selection Method ◽  
Multiple Reference Frames ◽  
Multiple Reference

scholarly journals Flexible coding of object motion in multiple reference frames by parietal cortex neurons

2020 ◽  
Vol 23 (8) ◽  
pp. 1004-1015 ◽  
Author(s):  
Ryo Sasaki ◽  
Akiyuki Anzai ◽  
Dora E. Angelaki ◽  
Gregory C. DeAngelis
Keyword(s):  
Parietal Cortex ◽  
Reference Frames ◽  
Object Motion ◽  
Multiple Reference Frames ◽  
Multiple Reference ◽  
Flexible Coding

Multisensory integration in multiple reference frames in the posterior parietal cortex

2004 ◽  
Vol 5 (3) ◽  
Author(s):  
Marie Avillac ◽  
Etienne Olivier ◽  
Sophie Den�ve ◽  
Suliann Ben Hamed ◽  
Jean-Ren� Duhamel
Keyword(s):  
Multisensory Integration ◽  
Parietal Cortex ◽  
Posterior Parietal Cortex ◽  
Reference Frames ◽  
Posterior Parietal ◽  
Multiple Reference Frames ◽  
Multiple Reference

scholarly journals Scalable FPGA Hardware Acceleration for H.264 Motion Estimation

2021 ◽  
Author(s):  
Theepan Moorthy
Keyword(s):  
Motion Estimation ◽  
Real Time ◽  
Video Compression ◽  
Reference Frames ◽  
Hardware Acceleration ◽  
Compressed Video ◽  
Time Performance ◽  
Wide Range ◽  
Multiple Reference Frames ◽  
Multiple Reference

The H.264 video compression standard uses enhanced Motion Estimation (ME) features to improve both the compression ratio and the quality of compressed video. The two primary enhancements are the use of Variable Block Size Motion Estimation (VBSME) and multiple reference frames. These two additions greatly increase the computational complexity of the ME algorithm, to the point where a software based real-time (30 frames per second (fps)) implementation is not possible on present microprocessors. Thus hardware acceleration of the H.264 ME algorithm is necessary in order to achieve real-time performance for the implementation of the VBSME and multiple reference frames features. This thesis presents a scalable FPGA-based ME architecture that supports real-time H.264 ME for a wide range of video resolutions ─ from 640x480 VGA to 1920x1088 High Definition (HD). The architecture contains innovations in both the data-path design and memory organization to achieve scalability and real-time performance on FPGAs. At 37% FPGA device utilization, the architecture is able to achieve 31 fps performance for encoding full 1920x1088 progressive HDTV video.

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