.png){kind=icon}
Return
Video Block Motion Estimation Based on Gray-Code Kernels
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2009.2025559.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Motion in modern video coders is estimated using a block matching algorithm that calculates the distance and direction of motion on a block-by-block basis. In this paper, a novel fast block-based motion estimation algorithm is proposed. This algorithm uses an efficient projection framework that bounds the distance between a template block and candidate blocks. Fast projection is performed using a family of highly efficient filter kernels-the gray-code kernels-requiring only 2 operations per pixel per kernel. The projection framework is combined with a rejection scheme which allows rapid rejection of candidate blocks that are distant from the template block. The tradeoff between computational complexity and quality of results can be easily controlled in the proposed algorithm; thus, it enables adaptivity to image content to further improve the results. Experiments show that the proposed adaptive algorithm outperforms other popular fast motion estimation algorithms.
Keywords:
Block matching
gray-code kernels (GCK)
motion estimation
video coding
Walsh-Hadamard transform (WHT)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W
Organization
Cited Papers
.png){kind=icon}
ShareSaveComparison of the Measurement Performance of High Precision Multi-axis Metal Cutting Machine Tools
OAAI
OAAIShareSaveShareSaveShareSaveShareSave
View more