plugins/ffmpeg-core/skills/ffmpeg-video-analysis/SKILL.md
Complete FFmpeg video analysis and quality control filters for automation and broadcast workflows. PROACTIVELY activate for: (1) Detecting black frames (blackdetect, blackframe), (2) Finding blurry/frozen frames (blurdetect, freezedetect), (3) Auto crop detection (cropdetect), (4) Scene change detection (scdet), (5) Interlace detection (idet), (6) Quality metrics (PSNR, SSIM, VMAF), (7) Signal analysis (signalstats), (8) Frame information logging (showinfo), (9) QC automation scripts, (10) Broadcast compliance checking. Provides: Detection filters, quality metrics, analysis commands, automation patterns.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace ffmpeg-video-analysisInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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| Task | Filter | Command Pattern |
|------|--------|-----------------|
| Detect black frames | blackdetect | -vf blackdetect=d=0.5:pic_th=0.98 |
| Detect frozen frames | freezedetect | -vf freezedetect=n=0.003:d=2 |
| Detect blur | blurdetect | -vf blurdetect=low=5:high=15 |
| Auto crop | cropdetect | -vf cropdetect=24:16:0 |
| Scene changes | scdet | -vf scdet=threshold=10 |
| Quality metrics | psnr, ssim | -lavfi "[0:v][1:v]psnr" -f null - |
| Frame info | showinfo | -vf showinfo |
Use for quality control and automation workflows:
Comprehensive guide to video analysis filters for quality control, automation, and professional workflows.
Detects video sequences that are completely black, useful for finding commercial breaks, scene boundaries, or encoding issues.
# Basic black detection
ffmpeg -i input.mp4 -vf "blackdetect=d=0.5:pic_th=0.98:pix_th=0.10" -f null -
# More sensitive detection (darker threshold)
ffmpeg -i input.mp4 -vf "blackdetect=d=0.1:pic_th=0.90" -f null -
# Save detection results to log
ffmpeg -i input.mp4 -vf "blackdetect=d=0.5" -f null - 2>&1 | grep blackdetect
# Detect and extract black segments
ffmpeg -i input.mp4 -vf "blackdetect=d=2.0" -f null - 2>&1 | \
grep -oP 'black_start:\K[0-9.]+|black_end:\K[0-9.]+'
Parameters:
| Parameter | Description | Default | Range |
|-----------|-------------|---------|-------|
| d | Minimum duration (seconds) | 2.0 | > 0 |
| pic_th | Picture black ratio threshold | 0.98 | 0-1 |
| pix_th | Pixel black threshold | 0.10 | 0-1 |
Output format:
[blackdetect @ 0x...] black_start:10.5 black_end:12.3 black_duration:1.8
Similar to blackdetect but reports individual frames and their blackness amount.
# Detect frames that are 98% black
ffmpeg -i input.mp4 -vf "blackframe=amount=98:threshold=32" -f null -
# Output includes frame number and percentage
ffmpeg -i input.mp4 -vf "blackframe=amount=90" -f null - 2>&1 | grep blackframe
Parameters:
| Parameter | Description | Default | Range |
|-----------|-------------|---------|-------|
| amount | Percentage threshold | 98 | 0-100 |
| threshold | Pixel brightness threshold | 32 | 0-255 |
Detects sequences where the video appears frozen (repeated frames).
# Basic freeze detection
ffmpeg -i input.mp4 -vf "freezedetect=n=0.003:d=2" -f null -
# More sensitive (detect shorter freezes)
ffmpeg -i input.mp4 -vf "freezedetect=n=0.001:d=0.5" -f null -
# Very strict (only perfectly identical frames)
ffmpeg -i input.mp4 -vf "freezedetect=n=0:d=1" -f null -
Parameters:
| Parameter | Description | Default | Range |
|-----------|-------------|---------|-------|
| n | Noise tolerance (frame diff) | 0.001 | 0-1 |
| d | Minimum freeze duration | 2.0 | > 0 |
Output format:
[freezedetect @ 0x...] freeze_start: 45.2
[freezedetect @ 0x...] freeze_duration: 3.5
[freezedetect @ 0x...] freeze_end: 48.7
Detects frames that are out of focus or motion blurred.
# Basic blur detection
ffmpeg -i input.mp4 -vf "blurdetect=low=5:high=15:radius=50" -f null -
# More sensitive detection
ffmpeg -i input.mp4 -vf "blurdetect=low=3:high=10" -f null -
# Output blur values per frame
ffmpeg -i input.mp4 -vf "blurdetect,metadata=print:file=blur.txt" -f null -
Parameters:
| Parameter | Description | Default | Range |
|-----------|-------------|---------|-------|
| low | Low edge threshold | 5 | 1-100 |
| high | High edge threshold | 15 | 1-100 |
| radius | Search radius | 50 | 1-100 |
| block_pct | Block percentage | 80 | 0-100 |
| block_width | Block width | - | > 0 |
| block_height | Block height | - | > 0 |
| planes | Planes to analyze | 1 | 0-15 |
Output metadata:
lavfi.blur - Blur value (lower = blurrier)Detects scene changes based on frame-to-frame differences.
# Basic scene detection
ffmpeg -i input.mp4 -vf "scdet=threshold=10:sc_pass=1" -f null -
# More sensitive (detect more scene changes)
ffmpeg -i input.mp4 -vf "scdet=threshold=5" -f null -
# Output scene changes with timestamps
ffmpeg -i input.mp4 -vf "scdet=t=10,metadata=print:file=scenes.txt" -f null -
# Combined with select filter to extract scene thumbnails
ffmpeg -i input.mp4 -vf "scdet=threshold=10,select='gt(scene,0.4)',showinfo" \
-vsync vfr scene_%04d.jpg
Parameters:
| Parameter | Description | Default | Range |
|-----------|-------------|---------|-------|
| threshold / t | Scene change threshold | 10.0 | 0-100 |
| sc_pass | Pass scene score to output | 0 | 0-1 |
Output metadata:
lavfi.scd.score - Scene change score (0-1)lavfi.scd.mafd - Mean absolute frame differencelavfi.scd.time - Timestamp of scene changeAutomatically detects optimal crop values to remove black borders.
# Basic crop detection
ffmpeg -i input.mp4 -vf "cropdetect=24:16:0" -f null -
# More aggressive detection (lower threshold)
ffmpeg -i input.mp4 -vf "cropdetect=16:2:0" -f null -
# Detect and apply crop in one command
crop=$(ffmpeg -i input.mp4 -vf "cropdetect=24:16:0" -f null - 2>&1 | \
grep -oP 'crop=\K[0-9:]+' | tail -1)
ffmpeg -i input.mp4 -vf "crop=$crop" output.mp4
# Detect letterbox dimensions
ffmpeg -i input.mp4 -vf "cropdetect=round=2:reset=0" -f null - 2>&1 | grep crop
Parameters:
| Parameter | Description | Default | Range |
|-----------|-------------|---------|-------|
| limit | Threshold for black pixels | 24 | 0-255 |
| round | Round to nearest multiple | 16 | >= 2 |
| reset | Reset counter (frames) | 0 | >= 0 |
| skip | Skip initial frames | 0 | >= 0 |
Output format:
[cropdetect @ 0x...] x1:0 x2:1919 y1:140 y2:939 w:1920 h:800 x:0 y:140 crop=1920:800:0:140
Detects whether video is interlaced and identifies field order.
# Basic interlace detection
ffmpeg -i input.mp4 -vf "idet" -frames:v 500 -f null -
# Output detection summary
ffmpeg -i input.mp4 -vf "idet" -f null - 2>&1 | grep -A5 "Repeated Fields"
Output includes:
Compares two videos and outputs PSNR quality metric.
# Compare original vs encoded
ffmpeg -i original.mp4 -i encoded.mp4 \
-lavfi "[0:v][1:v]psnr" -f null -
# Output PSNR per frame to file
ffmpeg -i original.mp4 -i encoded.mp4 \
-lavfi "[0:v][1:v]psnr=stats_file=psnr.log" -f null -
# Get average PSNR only
ffmpeg -i original.mp4 -i encoded.mp4 \
-lavfi "[0:v][1:v]psnr" -f null - 2>&1 | grep "average"
Output format:
[Parsed_psnr_0 @ 0x...] PSNR y:45.123 u:48.456 v:49.789 average:46.234 min:35.123 max:inf
Quality guidelines: | PSNR (dB) | Quality | |-----------|---------| | > 40 | Excellent (indistinguishable) | | 35-40 | Good | | 30-35 | Fair | | < 30 | Poor |
More perceptually accurate than PSNR for quality comparison.
# Compare original vs encoded
ffmpeg -i original.mp4 -i encoded.mp4 \
-lavfi "[0:v][1:v]ssim" -f null -
# Output SSIM per frame to file
ffmpeg -i original.mp4 -i encoded.mp4 \
-lavfi "[0:v][1:v]ssim=stats_file=ssim.log" -f null -
Output format:
[Parsed_ssim_0 @ 0x...] SSIM Y:0.987 (18.87) U:0.992 (20.97) V:0.993 (21.55) All:0.989 (19.59)
Quality guidelines: | SSIM | Quality | |------|---------| | > 0.98 | Excellent | | 0.95-0.98 | Good | | 0.90-0.95 | Fair | | < 0.90 | Poor |
Calculates motion activity score used by VMAF.
# Calculate motion score
ffmpeg -i input.mp4 -vf "vmafmotion" -f null - 2>&1 | grep vmafmotion
# Get average motion
ffmpeg -i input.mp4 -vf "vmafmotion" -f null - 2>&1 | tail -1
Comprehensive signal analysis for broadcast QC.
# Full signal analysis
ffmpeg -i input.mp4 -vf "signalstats=stat=tout+vrep+brng" -f null -
# Output to file
ffmpeg -i input.mp4 -vf "signalstats,metadata=print:file=stats.txt" -f null -
# Check for broadcast-safe levels
ffmpeg -i input.mp4 -vf "signalstats=stat=brng,metadata=print" -f null - 2>&1 | \
grep "lavfi.signalstats.BRNG"
Statistics available:
| Stat | Description |
|------|-------------|
| tout | Temporal outliers |
| vrep | Vertical line repetition |
| brng | Broadcast range violations |
Output metadata includes:
YMIN, YMAX - Luma rangeYAVG - Average lumaUMIN, UMAX, VMIN, VMAX - Chroma rangeSATMIN, SATMAX, SATAVG - SaturationHUEAVG - Average hueBRNG - Out of broadcast range pixel countOutputs detailed information about each frame.
# Show all frame info
ffmpeg -i input.mp4 -vf "showinfo" -f null -
# Show specific frames only
ffmpeg -i input.mp4 -vf "select='eq(n,0)+eq(n,100)',showinfo" -f null -
# Parse specific information
ffmpeg -i input.mp4 -vf "showinfo" -f null - 2>&1 | grep "pts_time"
Output includes:
[Parsed_showinfo_0 @ 0x...] n: 0 pts: 0 pts_time:0 duration: 1001
duration_time:0.0417083 fmt:yuv420p cl:left sar:1/1 s:1920x1080 i:P iskey:1
type:I checksum:12345678 plane_checksum:[AAAAAAAA BBBBBBBB CCCCCCCC]
Fields:
n - Frame numberpts - Presentation timestamppts_time - PTS in secondsduration - Frame durationfmt - Pixel formats - Size (resolution)i - Interlaced (P=progressive, T=top, B=bottom)iskey - Is keyframetype - Frame type (I/P/B)ITU-T P.910 compliant SI/TI calculation.
# Calculate SI/TI
ffmpeg -i input.mp4 -vf "siti" -f null - 2>&1 | grep siti
# Output to file
ffmpeg -i input.mp4 -vf "siti=print_summary=1" -f null -
Output:
SI - Spatial Information (edge/texture complexity)TI - Temporal Information (motion activity)Shell automation recipes for batch analysis, structured report generation, threshold checks, and CI-style validation around FFmpeg detection filters, quality metrics, and frame information live in references/automation-patterns.md. Load that reference when turning ad hoc analysis commands into repeatable pipelines.
-t to analyze portions firstmetadata=print:file= to save resultsThis guide covers video analysis filters for 2025. For encoding quality, see ffmpeg-fundamentals-2025. For hardware-accelerated analysis, check GPU filter support in ffmpeg-hardware-acceleration.
development
This skill should be used when the user asks to train, debug, scale, or improve ML models. PROACTIVELY activate for: (1) PyTorch, TensorFlow/Keras, JAX, Flax, Hugging Face Trainer/Accelerate training loops, (2) distributed training, DDP/FSDP/DeepSpeed, TPU/GPU setup, (3) mixed precision AMP/bf16, gradient accumulation, checkpointing, seeding, (4) overfitting, imbalance, loss functions, regularization, LR schedules, warmup, (5) memory optimization, gradient checkpointing, offloading, quantization-aware training. Provides: reproducible training best practices across deep learning and classical ML.
development
This skill should be used when the user asks to productionize, track, version, govern, monitor, or automate ML systems. PROACTIVELY activate for: (1) MLflow, Weights & Biases, Neptune, Comet, ClearML experiment tracking, (2) model registry, model versioning, artifact lineage, reproducibility, (3) Kubeflow, SageMaker Pipelines, Vertex AI Pipelines, Azure ML pipelines, Databricks workflows, (4) CI/CD, continuous training/evaluation, A/B tests, canary/shadow deployments, (5) drift detection, model monitoring, data validation, responsible AI governance. Provides: end-to-end MLOps architecture and operational safeguards.
development
This skill should be used when the user asks to optimize, export, serve, compress, or accelerate ML inference. PROACTIVELY activate for: (1) latency, throughput, p95/p99, batching, concurrency, KV cache, memory, or cost issues, (2) quantization INT8/INT4, GPTQ, AWQ, bitsandbytes, pruning, sparsity, distillation, (3) ONNX export, ONNX Runtime, TensorRT, TorchScript, torch.compile, XLA, OpenVINO, Core ML, TFLite, (4) Triton, TorchServe, TF Serving, BentoML, Seldon, KServe configuration, (5) edge deployment, CPU/GPU/TPU/Inferentia serving. Provides: hardware-aware inference optimization and safe benchmarking.
testing
This skill should be used when the user asks to tune hyperparameters, run sweeps, optimize search spaces, or use AutoML. PROACTIVELY activate for: (1) Optuna, Ray Tune, FLAML, AutoGluon, Hyperopt, Nevergrad, KerasTuner, W&B sweeps, (2) grid search, random search, Bayesian optimization, TPE, Gaussian processes, evolutionary search, (3) ASHA, Hyperband, successive halving, multi-fidelity optimization, population-based training, (4) learning-rate finder, batch-size search, early stopping, pruning, (5) reproducible sweep design and experiment analysis. Provides: budget-aware hyperparameter search strategy.