Efficient MPEG compressed video analysis using macroblock type information

Pei, Soo‐Chang; Chou, Yu-Zuong

doi:10.1109/6046.807952

Cited by 65 publications

(5 citation statements)

References 12 publications

(6 reference statements)

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“…Figure 3 shows the categorization of video shot segmentation methods. In the compressed domain, features extracted from video frames include motion vectors, macroblocks, DCT coefficients and motion compensation [6,7]. Shot boundary detection (SBD) algorithms in the compressed domain depend on the compression standard.…”

Section: Related Workmentioning

confidence: 99%

The Detection of Video Shot Transitions Based on Primary Segments Using the Adaptive Threshold of Colour-Based Histogram Differences and Candidate Segments Using the SURF Feature Descriptor

M.¹,

Kalaivani

Shanmugam

2022

Symmetry

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Aim: Advancements in multimedia technology have facilitated the uploading and processing of videos with substantial content. Automated tools and techniques help to manage vast volumes of video content. Video shot segmentation is the basic symmetry step underlying video processing techniques such as video indexing, content-based video retrieval, video summarization, and intelligent surveillance. Video shot boundary detection segments a video into temporal segments called shots and identifies the video frame in which a shot change occurs. The two types of shot transitions are cut and gradual. Illumination changes, camera motion, and fast-moving objects in videos reduce the detection accuracy of cut and gradual transitions. Materials and Methods: In this paper, a novel symmetry shot boundary detection system is proposed to maximize detection accuracy by analysing the transition behaviour of a video, segmenting it initially into primary segments and candidate segments by using the colour feature and the local adaptive threshold of each segment. Thereafter, the cut and gradual transitions are fine-tuned from the candidate segment using Speeded-Up Robust Features (SURF) extracted from the boundary frames to reduce the algorithmic complexity. The proposed symmetry method is evaluated using the TRECVID 2001 video dataset, and the results show an increase in detection accuracy. Result: The F1 score obtained for the detection of cut and gradual transitions is 98.7% and 90.8%, respectively. Conclusions: The proposed symmetry method surpasses recent state-of-the-art SBD methods, demonstrating increased accuracy for both cut and gradual transitions in videos.

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Section: Related Workmentioning

confidence: 99%

The Detection of Video Shot Transitions Based on Primary Segments Using the Adaptive Threshold of Colour-Based Histogram Differences and Candidate Segments Using the SURF Feature Descriptor

M.¹,

Kalaivani

Shanmugam

2022

Symmetry

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“…Research men at home and abroad have proposed many algorithm of directly detecting border of lens for MPEG video compression. It concluded method based on DCT coefficient [4], DC chart [5], movement vector [6], macro block type [7], etc.…”

Section: Introductionmentioning

confidence: 99%

System Design of Campus Remote Video Monitoring

Zhang

Wang

2012

AMR

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With the rapid development and the universal application of computer network communication technology, image compression processing technology and transmission technology, Developing steps of intelligent, network, information and digital about university becomes faster and faster. It is very important for university safety work to construct a intelligent network video monitoring system. A full function system based on H.264 standard and character of security-monitoring work is designed. real-time monitoring and dynamic video are realized for key region of campus in this system, at the same time remote monitoring is realized by means of monitoring platform established by internet.

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“…The purpose of using ME/MC is to reduce redundancy in temporal direction. To exploit temporal redundancy, MPEG algorithms compute an interframe difference called prediction error (Pei, 1999).…”

Section: Proposed Techniquementioning

confidence: 99%

Hybrid-Based Compressed Domain Video Fingerprinting Technique

Abbass¹,

Youssif²,

Ghalwash³

2012

CIS

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Video fingerprinting is a newer research area. It is also called "content-based video copy detection" or "content-based video identification" in literature. The goal is to locate videos with segments substantially identical to segments of a query video while tolerating common artifacts in video processing. Its value as a tool to curb piracy and legally monetize contents becomes more and more apparent in recent years with the wide spread of Internet videos through user generated content (UGC) sites like YouTube. Its practical applications to a certain extent overlap with those of digital watermarking, which requires adding artificial information into the contents. Fingerprints are compact content-based signature that summarizes a video signal or another media signal. Several video fingerprinting methods have been proposed for identifying video, in which fingerprints are extracted by analyzing video in both spatial and temporal dimension. However, these conventional methods have one resemblance, in which video decompression is still required for extracting the fingerprint from a compressed video. In practical, faster computational time can be achieved if fingerprint is extracted directly from the compressed domain. So far, too fewer methods are known to propose video fingerprinting in compressed domain. This paper presents a video fingerprinting technique that works directly in the compressed domain. Experimental results show that the proposed fingerprint is highly robust against most signal processing transformations.

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Efficient MPEG compressed video analysis using macroblock type information

Cited by 65 publications

References 12 publications

The Detection of Video Shot Transitions Based on Primary Segments Using the Adaptive Threshold of Colour-Based Histogram Differences and Candidate Segments Using the SURF Feature Descriptor

The Detection of Video Shot Transitions Based on Primary Segments Using the Adaptive Threshold of Colour-Based Histogram Differences and Candidate Segments Using the SURF Feature Descriptor

System Design of Campus Remote Video Monitoring

Hybrid-Based Compressed Domain Video Fingerprinting Technique

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