Seam-based variable-step Bresenham blending method for real-time video mosaicking

Hongru Zhang; Zaifeng Shi; Ke Pang; Qingjie Cao; Tao Luo; Suying Yao

doi:10.1117/1.JEI.25.5.053008

16 September 2016 Seam-based variable-step Bresenham blending method for real-time video mosaicking

Hongru Zhang, Zaifeng Shi, Ke Pang, Qingjie Cao, Tao Luo, Suying Yao

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 5, 053008 (September 2016). https://doi.org/10.1117/1.JEI.25.5.053008

Abstract

Image blending plays an important role in video mosaicking, which has a high demand for real-time performance and visual quality. This paper proposes a fast blending method based on Bresenham algorithm, which realizes blending by controlling the storing addresses of source pixels. The starting storing location is accurately computed based on the coordinates of the middle pixel of the seam instead of the first pixel’s, reducing the accumulated error along the seam significantly. The other storing addresses are acquired using a variable-step Bresenham method, which takes advantage of burst mode operation of a dynamic memory and can achieve a good trade-off between the operation convenience and memory requirement. By the proposed method, complicated calculations of storing addresses are simplified into integer additions and subtractions, which is more suitable for hardware implementation. A hardware architecture based on field programmable gate array is presented to evaluate the proposed method with clock frequency analysis and resource assessment. The experimental results show that the proposed method achieves good performance of high image quality, low computational complexity, and low memory requirement.

Citation Download Citation

Hongru Zhang, Zaifeng Shi, Ke Pang, Qingjie Cao, Tao Luo, and Suying Yao "Seam-based variable-step Bresenham blending method for real-time video mosaicking," Journal of Electronic Imaging 25(5), 053008 (16 September 2016). https://doi.org/10.1117/1.JEI.25.5.053008

Published: 16 September 2016

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