Automatic feature-based global motion estimation in video sequences

Huang, J.-C.; Hsieh, Wen-Shyong

doi:10.1109/tce.2004.1341699

Cited by 23 publications

(1 citation statement)

References 7 publications

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“…Reference [29] proposes a method of edge video compression texture synthesis based on a generative adversarial network to obtain the most authentic texture information. Reference [30] proposes an affine parameter model that utilizes matching algorithms to discover and extract feature point pairs from edges within consecutive frames and selects the optimal set of three sets of point pairs to describe global motion. Reference [31] proposes linear applications of traditional intra-prediction modes based on a pattern correlation processing sequence, region-based template matching prediction methods, and neural-network-based intra-prediction modes.…”

Section: Related Workmentioning

confidence: 99%

A Fast Gradient Iterative Affine Motion Estimation Algorithm Based on Edge Detection for Versatile Video Coding

et al. 2023

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In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. The experimentalresults show that the encoding time of the affine motion estimation algorithm is about 15–35% lower than the overall encoding time of the anchor algorithm encoder, the average encoding time of the affine motion estimation part of the inter-frame prediction part is reduced by 24.79%, and the peak signal-to-noise ratio (PSNR) is only reduced by 0.04.

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Section: Related Workmentioning

confidence: 99%

A Fast Gradient Iterative Affine Motion Estimation Algorithm Based on Edge Detection for Versatile Video Coding

et al. 2023

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A Robust and Hierarchical Approach for Camera Motion Classification

Geng

Feng

et al. 2006

Lecture Notes in Computer Science

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Abstract. Camera motion classification is an important issue in content-based video retrieval. In this paper, a robust and hierarchical camera motion classification approach is proposed. As the Support Vector Machine (SVM) has a very good learning capacity with limited sample set and does not require any heuristic parameter, the SVM is first employed to classify camera motions into translation and non-translation motions in preliminary classification. In this step, four features are extracted as input of the SVM. Then, zoom and rotation motions are further classified by analyzing the motion vectors' distribution. And the directions of translation motions are also identified. The experimental results show that the proposed approach achieves a good performance.

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No-Reference Video Shakiness Quality Assessment

Cui

Jiang

2017

Computer Vision – ACCV 2016

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Automatic feature-based global motion estimation in video sequences

Cited by 23 publications

References 7 publications

A Fast Gradient Iterative Affine Motion Estimation Algorithm Based on Edge Detection for Versatile Video Coding

A Fast Gradient Iterative Affine Motion Estimation Algorithm Based on Edge Detection for Versatile Video Coding

A Robust and Hierarchical Approach for Camera Motion Classification

No-Reference Video Shakiness Quality Assessment

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