Detection of repeated segments in video sequences

Paschalakis, Stavros; Giani, A.; Mishima, H.

doi:10.1109/icce.2010.5418921

Cited by 2 publications

(2 citation statements)

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“…In general, abrupt transitions are much more common than gradual transitions, accounting for over 99% of all transitions [16]. For this reason, we focus on abrupt cut detection.…”

Section: Video Cut Detectionmentioning

confidence: 99%

GOP Adaptation Coding of H.264/SVC Based on Precise Positions of Video Cuts

2014

KSII TIIS

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Hierarchical B-frame coding was introduced into H.264/SVC to provide temporal scalability and improve coding performance. A content analysis-based adaptive group of picture structure (AGS) can further improve the coding efficiency, but damages the inter-frame correlation and temporal scalability of hierarchical B-frame to different degrees. In this paper, we propose a group of pictures (GOP) adaptation coding method based on the positions of video cuts. First, the cut positions are accurately detected by the combination of motion coherence (MC) and mutual information (MI); then the GOP is adaptively and proportionately set by the analysis of MC in one scene. In addition, we propose a binary tree algorithm to achieve the temporal scalability of any size of GOP. The results for test sequences and real videos show that the proposed method reduces the bit rate by up to about 15%, achieves a performance gain of about 0.28-1.67 dB over a fixed GOP, and has the advantages of better transmission resilience and video summaries.

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“…In general, abrupt transitions are much more common than gradual transitions, accounting for over 99% of all transitions [16]. For this reason, we focus on abrupt cut detection.…”

Section: Video Cut Detectionmentioning

confidence: 99%

GOP Adaptation Coding of H.264/SVC Based on Precise Positions of Video Cuts

2014

KSII TIIS

View full text Add to dashboard Cite

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“…The major techniques used for the shot boundary detection are pixel differences, statistical differences, histogram comparisons [5], edge differences, compression differences and motion vectors [6,7,8]. In general, abrupt transitions are much more common than gradual transitions, accounting for over 99% of all transitions found in a video [9]. Therefore, the correct detection of abrupt shot transitions is a very important task in the video segmentation and this paper is only focused on the detection of an abrupt cut.…”

Section: Introductionmentioning

confidence: 99%

Video Analysis Based on Mutual Information

Krulikovská

Mardiak

Pavlovic

et al. 2010

Computer Vision and Graphics

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Abstract. In this paper we present the methods used for the analysis of video based on mutual information. We propose a novel method of abrupt cut detection and a novel objective method for measuring the quality of video. In the field of abrupt cut detection we improve the existing method based on mutual information. The novelty of our method is in combining the motion prediction and the mutual information. Our approach provides higher robustness to object and camera motion. According to the objective method for measuring the quality of video, it is based on calculation the mutual information between the frame from the original sequence and the corresponding frame from the test sequence. We compare results of the proposed method with commonly used objective methods for measuring the video quality. Results show that our method correlates with the standardized method and the distance metric, so it is possible to replace a more complex method with our simpler method.

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Detection of repeated segments in video sequences

Cited by 2 publications

References 1 publication

GOP Adaptation Coding of H.264/SVC Based on Precise Positions of Video Cuts

GOP Adaptation Coding of H.264/SVC Based on Precise Positions of Video Cuts

Video Analysis Based on Mutual Information

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