Adaptive Edge Detection Pre-Process Multiple Reference Frames Motion Estimation in H.264/AVC

2007 ◽  
Author(s):  
Yiqing Huang ◽  
Zhenyu Liu ◽  
S. Goto ◽  
T. Ikenaga
Keyword(s):  
Motion Estimation ◽  
Edge Detection ◽  
Reference Frames ◽  
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.

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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