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Scene Change Detection Method for MPEG Video

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BORANG PENGESAHAN STATUS TESIS

Universiti Teknologi Malaysia

Judul:

Sesi Pengajian:

Saya (HURUF BESAR)

mengaku membenarkan tesis (PSM/Sarjana/Doktor Falsafah) ini disimpan di Perpustakaan Universiti Teknologi Malaysia dengan syarat-syarat kegunaan seperti berikut:

1. Tesis adalah hakmilik Universiti Teknologi Malaysia.

2. Perpustakaan Universiti Teknologi Malaysia dibenarkan membuat salinan untuk tujuan pengajian sahaja.

3. Perpustakaan dibenarkan membuat salinan tesis ini sebagai bahan pertukaran antara institusi pengajian tinggi.

4. **Sila tandakan (4)

SULIT (Mengandungi maklumat yang berdarah keselamatan atau kepentingan Malaysia seperti di dalam AKTA RAHSIA RASMI 1972)

TERHAD (Mengandungi maklumat TERHAD yang telah ditentukan oleh organisasi/badan di mana penyelidikan dijalankan)

TIDAK TERHAD

Disahkan oleh

(TANDATANGAN PENULIS)

(TANDATANGAN PENYELIA)

Alamat Tetap:

Tarikh:

Nama Penyelia:

Tarikh:

Catatan:

* Potong yang tidak berkenaan.

** Jika tesis ini SULIT atau TERHAD, sila lampirkan surat daripada pihak berkuasa/organisasi/badan yang berkenaan sebagai sebab dan tempoh tesis ini perlu diklasifikasikan.

BAHAGIAN A – Pengesahan Kerjasama

Adalah disahkan bahawa projek penyelidikan tesis ini telah dilaksanakan melalui kerjasama antara dengan

Disahkan oleh:

Tandatangan:

Tarikh:

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(Cop rasmi)

* Jika penyelidikan tesis/projek melibatkan kerjasama.

BAHAGIAN B – Untuk Kegunaan Pejabat Sekolah Pengajian Siswazah

Tesis ini telah diperiksa dan diakui oleh:

Nama dan Alamat Pemeriksa Luar:

Nama dan Alamat Pemeriksa Dalam:

Nama Penyelia Lain (jika ada):

Disahkan oleh Penolong Pendaftar di SPS:

Tandatangan:

Tarikh:

Nama:

SCENE CHANGE DETECTION METHOD FOR MPEG VIDEO

A dissertation submitted in fulfillment of the requirements for the award of the degree of Master of Engineering (Electrical – Electronic & Telecommunication)

Faculty of Electrical Engineering

Universiti Teknologi Malaysia

APRIL, 2005

I declare that this dissertation entitled “Scene Change Detection Method for MPEG Video” is the result of my own research except as cited in the references. The dissertation has not been accepted for any degree and is not concurrently submitted in candidature of any other degree.

Signature:

Name:

Date:

To my beloved mother and father

ACKNOWLEDGEMENT

ABSTRACT

ABSTRAK

TABLE OF CONTENTS

LIST OF TABLES

LIST OF FIGURES

LIST OF APPENDICES

CHAPTER 1 – INTRODUCTION

CHAPTER 2 – THEORETICAL FOUNDATION

CHAPTER 3 – METHOD 1 – EDGE DETECTION

CHAPTER 4 – METHOD 2 – CHANGES OF GRAY SCALE LEVEL

CHAPTER 5 – RESULT AND DISCUSSION

CHAPTER 6 – CONCLUSION

REFERENCES

APPENDIX A – D

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What the Scene Change Detection Method for MPEG Video Is

The Scene Change Detection Method for MPEG Video is a reproducible technical procedure for locating abrupt and gradual scene boundaries inside MPEG-compressed streams. It inspects encoded signals such as macroblock change metrics, motion vectors, intra-frame frequency, and color histogram shifts across GOP boundaries, then applies configurable thresholds, temporal smoothing, and clustering to separate cuts from dissolves and wipes. Deliverables include frame-accurate timestamps, representative keyframes, and machine-readable metadata for indexing, editing, moderation, or downstream analytics. The method supports parameterization, test-set evaluation, and export in common metadata formats.

Why a Formal Scene Change Detection Method Matters

A documented method reduces manual review, improves consistency across encoders and bitrates, and produces reliable metadata for search, chaptering, and automated editing. Standardization shortens testing cycles and helps compare algorithm variants objectively.

Why a Formal Scene Change Detection Method Matters

Who Typically Uses This Method

Typical users include video engineers, research teams, QA groups, and content operations staff responsible for indexing, editing, or moderation.

  • Video engineers optimizing encoding-aware detection for real-time workflows and efficient keyframe selection.
  • Research labs validating detection accuracy on annotated MPEG datasets and algorithm benchmarks.
  • Media operations and QA teams using timestamps and metadata for logging, clipping, and compliance review.

Each team adapts thresholds and evaluation metrics to their tolerance for missed cuts, false positives, and downstream automation needs.

Step-by-Step: Implementing the Method

Follow these steps to prepare data, run detection, evaluate results, and produce final metadata.

  • 01
    Ingest: Load MPEG stream, note codec and GOP parameters.
  • 02
    Feature Extraction: Extract macroblock diffs, motion vectors, and color histograms.
  • 03
    Detect: Apply metric thresholds, smoothing, and clustering to mark boundaries.
  • 04
    Export: Output timestamps, keyframes, and structured metadata files.

Core Components of a Professional Detection Method

A robust method combines encoded-feature extraction, statistical detection, and operational controls to handle diverse MPEG content and distribution workflows.

Frame Extraction

Efficiently read frames or encoded-layer summaries without full decode when possible; handle B/P/I frame boundaries and GOP headers for accurate temporal alignment and reduced CPU cost.

Motion Vector Analysis

Analyze motion vectors from compressed bitstream to distinguish camera motion from scene changes, reducing false positives caused by pan/tilt or object motion inside a scene.

Macroblock Difference

Compute per-macroblock change metrics (e.g., L1/L2 norms) aggregated across frame to detect abrupt visual discontinuities that often indicate true cuts.

Color Histogram Comparison

Use color histogram or perceptual hash differences to detect content shifts in gradual transitions and provide complementary evidence to motion-based signals.

Temporal Smoothing

Apply short-window smoothing and post-processing (merge near detections, suppress isolated spikes) to reduce noise while preserving temporal precision.

Output Annotation

Produce machine-readable outputs (JSON, CSV, or XML) with frame timestamps, keyframe images, confidence scores, and parameter metadata for auditability.

Export Formats and Supporting Documents

Choose interoperable export formats and include supporting artifacts for validation and downstream use.

Metadata Files

Export detection results as JSON or CSV with frame-level timestamps, scene IDs, confidence scores, and algorithm parameters for reproducibility.

Keyframes

Include representative keyframe images (JPEG/PNG) for each detected scene to aid manual review and quick previewing.

Evaluation Report

Provide a short report with precision/recall on annotated test sets, parameter values used, and sample failure cases to guide tuning.

Change Log

Record algorithm version, code commit hash, tested dataset, and run timestamp to maintain audit trails and traceability.

Required Identification and Metadata Fields

Algorithm Version: Semantic version or commit id
Author / Owner: Name and role of implementer
Parameters: Thresholds, window sizes
Test Dataset: Source and annotation reference
Evaluation Metrics: Precision / recall / F1
Signed Attestation: Digital signature or signer identity

Common Preparation and Implementation Mistakes

  • Ignoring GOP alignment which shifts timestamp references and causes off-by-one scene markers across encoders.
  • Using a single global threshold without content-specific tuning, producing excessive false positives on high-motion footage.
  • Failing to record algorithm parameters and dataset versions, preventing reproducibility and meaningful comparisons.
  • Exporting only visual markers without confidence scores, making automated downstream decisions unreliable.

Risks and Consequences of Incorrect Detection

False Positives: Extra manual review
Missed Cuts: Incomplete indexing
Metadata Drift: Search degradation
Processing Overhead: Increased compute cost
Compliance Exposure: Poor audit trails
Unreliable Deliverables: Downstream automation fails

Processing Flow and Where Outputs Go

A typical pipeline ingests MPEG sources, runs detection, then routes annotated outputs to storage, editors, or downstream analytics systems.

  • Ingest: Accept files or stream URIs, record source metadata.
  • Detect: Run extraction and scene-change algorithm at configured sampling.
  • Postprocess: Apply smoothing, merge close events, attach confidence.
  • Distribute: Publish JSON, keyframes, and logs to storage or editorial tools.

Sharing, Storage, and Integration Considerations

Outputs should be compatible with common cloud storage, media asset managers, and API-driven ingestion points for downstream tools.

  • Cloud Storage: Amazon S3, Google Cloud Storage supported
  • Asset Managers: Box, Egnyte, and MAM systems integrate via API
  • Document Formats: JSON, CSV, XML, and packaged ZIP outputs

Practical Examples of Method Use

Two concise example scenarios show common implementations and expected outcomes when the method is applied to production content.

Media Archive Indexing

A public broadcaster batches MPEG archives for automated chaptering and search.

  • Detection marks scenes at frame precision for thousands of hours.
  • The pipeline reduced manual logging time, improved retrieval accuracy for editors, and produced consistent chapter metadata consumed by the CMS and player.

Editorial Clip Generation

A post-production house needs cut points for highlights.

  • Real-time detection identifies candidate trims during ingest.
  • Editors review high-confidence markers only, accelerating turnaround and enabling automated assembly of highlight reels with attached keyframes and confidence scores.

eSignature Vendor Pricing and Capability Snapshot

Typical vendor starting prices and selected capability indicators for eSignature providers often used to sign attestations or workflows. Values summarize publicly available plan starting points and capability flags.

signNow DocuSign Adobe Sign PandaDoc HelloSign
Starting Price $8/user/mo $15/user/mo $14/user/mo $19/user/mo $15/user/mo
Free Trial 7-day free trial Varies by vendor Varies by vendor Varies by vendor Varies by vendor
Bulk Send Yes Yes Yes Yes No
Audit Trail Yes Yes Yes Yes Yes
HIPAA Compliant Yes Yes Yes No No
Envelope Cap No cap 100 envelopes/user/year Varies by plan Varies by plan Varies by plan

Frequently Asked Questions and Troubleshooting

Answers to common technical, operational, and e-signature questions about documenting and deploying the method.


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