Video stabilization based on human visual system

Lee, Yun Gu

doi:10.1117/1.jei.23.5.053009

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…Karpendo et al proposed video stabilization and rolling shutter correction using gyroscopes [26]. Lee proposed video stabilization based on human visual system [27].…”

Section: Camera Motion Estimationmentioning

confidence: 99%

“…The contentpreserving warp relaxes the constraint for a physically correct reconstruction while preserving the perceptual quality. Some studies employ a purely rotational motion model without considering translational camera motion, in order to avoid image artifacts from view interpolation [26,27].…”

Section: Image Synthesismentioning

confidence: 99%

“…Therefore, the performance of real-time video stabilization is very crucial in consumer digital cameras. Fast and efficient video stabilization methods for real-time applications have been studied [5,12,24,27,37,38]. However, these methods did not consider camera path planning in depth.…”

Section: Real-time Processingmentioning

confidence: 99%

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A novel camera path planning algorithm for real-time video stabilization

Lee

2019

J Image Video Proc.

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In video stabilization, a steady camera path plan is as important as accurate camera motion prediction. While several camera path planning algorithms have been studied, most algorithms are used for post-processing video stabilization. Since all of the frames are accessible in post-processing video stabilization, a camera path planning method can generate a good camera path, considering the camera movements at all of the frames. Meanwhile, the number of accessible frames in real-time video stabilization is limited. Thus, smooth camera path planning is a challenging issue in real-time video stabilization. For example, a camera path planner does not know in advance the locations of sudden camera motions, so it is not easy to compensate a sudden camera movement. Therefore, this paper proposes a novel camera path planning algorithm for real-time video stabilization. A camera path planning method should fully utilize the given image margin to provide steady camera paths. In contrast, it should retain the certain amount of an unused image margin for use in frames with dynamic or sudden camera movements. To resolve this problem, the proposed algorithm uses two terms related to the steady camera path and the amount of image margin, and it cross-optimizes the two terms to provide a new camera path on-the-fly. Experimental results show that the proposed algorithm provides excellent performance in real-time video stabilization while requiring negligible computation.

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“…Karpendo et al proposed video stabilization and rolling shutter correction using gyroscopes [26]. Lee proposed video stabilization based on human visual system [27].…”

Section: Camera Motion Estimationmentioning

confidence: 99%

Section: Image Synthesismentioning

confidence: 99%

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A novel camera path planning algorithm for real-time video stabilization

Lee

2019

J Image Video Proc.

Self Cite

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“…Many algorithms have been developed to compensate the geometric distortion from the rolling shutter effect and stabilize the shaky video [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. A simple two-dimensional model was proposed to remove perceived camera motion [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…Grundmann proposed calibration-free video stabilization and rolling shutter compensation based on a mixture model of homographies [ 15 ]. Lee proposed video stabilization based on the human visual system [ 16 ]. Guilluy recently reviewed more than 120 articles related to digital video stabilization [ 20 ] in depth in terms of cutting edges and gaps.…”

Section: Introductionmentioning

confidence: 99%

Low Memory Access Video Stabilization for Low-Cost Camera SoC

Lee

2022

Sensors

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Video stabilization is one of the most important features in consumer cameras. Even simple video stabilization algorithms may need to access the frames several times to generate a stabilized output image, which places a significant burden on the camera hardware. This high-memory-access requirement makes it difficult to implement video stabilization in real time on low-cost camera SoC. Reduction of the memory usage is a critical issue in camera hardware. This paper presents a structure and layout method to efficiently implement video stabilization for low-end hardware devices in terms of shared memory access amount. The proposed method places sub-components of video stabilization in a parasitic form in other processing blocks, and the sub-components reuse data read from other processing blocks without directly accessing data in the shared memory. Although the proposed method is not superior to the state-of-the-art methods applied in post-processing in terms of video quality, it provides sufficient performance to lower the cost of camera hardware for the development of real-time devices. According to my analysis, the proposed one reduces the memory access amount by 21.1 times compared to the straightforward method.

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Novel video stabilization for real-time optical character recognition applications

Lee

2017

Journal of Visual Communication and Image Representation

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Video stabilization based on human visual system

Cited by 5 publications

References 13 publications

A novel camera path planning algorithm for real-time video stabilization

A novel camera path planning algorithm for real-time video stabilization

Low Memory Access Video Stabilization for Low-Cost Camera SoC

Novel video stabilization for real-time optical character recognition applications

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