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Title
Abstract
FAQs
Introduction
Results and Discussions
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References
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Computer Science
Media Technology

Video Partitioning and Secured Keyframe Extraction of MPEG Video

Profile image of Dr. Kalpana ThakreDr. Kalpana Thakre

2016, Procedia Computer Science

https://doi.org/10.1016/J.PROCS.2016.02.058
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9 pages

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Abstract

Video partitioning and keyframe extraction (KFE) are the key foundations of video analysis and Content based video retrieval. The use of keyframes reduces the amount of data that is necessary in video indexing and provides the outline for dealing with the video content. In the last few years, many algorithms of keyframe extraction concentrated on the original compressed video stream. This can increase computational complexity when decompression is required before video processing. Keyframe is the frame, which can be a prototype of the salient content and information of the shot. The keyframes extracted must summarize the significant features of the video in time sequence. Therefore, there is a commensurate need of an efficient and secured keyframe selection technique in an efficient CBVR system. We propose an algorithm for keyframe extraction of compressed video shots using adaptive threshold method. Extensive computation on 200 plus video clips is performed and results are accurate and satisfactory.

FAQs

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What metrics were used for evaluating the proposed keyframe selection algorithm?add

The keyframe selection algorithm was evaluated using 200 video clips across various genres, and its results were compared against all-frame representation methods. Extracted keyframes were assessed for their ability to capture rich semantic content efficiently.

How does the adaptive keyframe selection algorithm improve content representation?add

The algorithm computes local minima and maxima within average mean values of frames, thereby selecting frames that best represent shot content. This approach contrasts with earlier methods that relied solely on the first or random frames.

What computational approach underpins the shot boundary detection in this study?add

The study employs a distance computation method to analyze transformed blocks of consecutive frames, determining shot boundaries when a distance condition is violated. This approach effectively segments video into meaningful shots for further analysis.

What are the limitations of existing keyframe extraction methods identified in the research?add

Existing methods often select static thresholds for similarity measurement, which can lead to poor extraction quality across different video types. Additionally, using only the first frame as a keyframe often results in loss of important visual information.

How does the proposed algorithm handle redundancy in frame selection?add

The proposed algorithm emphasizes selecting keyframes that minimize redundancy by focusing on representative frames that contain salient content. This filtering process ensures that each extracted keyframe conveys unique visual information relevant to the shot.

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