Smart Chunking

Smart Chunking is ImageChunker’s signature feature — an intelligent algorithm that analyzes your images to find optimal split points, avoiding awkward cuts through text, faces, or key elements.

How It Works

Unlike simple grid-based splitting, Smart Chunking:

1. Scans image content — Analyzes pixel patterns and content density
2. Detects boundaries — Identifies natural break points like blank spaces and scene changes
3. Avoids awkward cuts — Protects text, faces, and key visual elements
4. Optimizes distribution — Balances chunk sizes while respecting content

The algorithm uses GPU-accelerated Metal shaders for fast content analysis, with streaming processing to handle large images efficiently.

Settings

Sensitivity — Controls how aggressively Smart Chunking looks for boundaries:

Level	Behavior	Best For
Low	Fewer, larger chunks	Dense content
Medium	Balanced splitting	Most images
High	More, smaller chunks	Varied content

Minimum Chunk Height — Prevents chunks from becoming too small (default: 200px, range: 50–1000px).

Content Detection — Enable specific detection types:

• Text Detection — Avoid splitting mid-line
• Face Detection — Keep faces intact
• Object Detection — Preserve key subjects

When to Use Smart Chunking

Ideal for:

• Screenshots with mixed content (text sections, UI elements, images)
• Comic and manga pages (panel boundaries, dialogue bubbles)
• Infographics (section headers, natural breaks)
• Photos with subjects (portraits, group photos)

Consider Regular Mode instead for:

• Abstract art with no clear boundaries
• Uniform textures
• When exact dimensions are required
• Speed-critical workflows

Comparison

Aspect	Smart Chunking	Regular Mode
Speed	Slower (analysis)	Faster
Chunk sizes	Variable	Fixed
Content awareness	Yes	No
Text protection	Yes	No

Tips for Best Results

1. Use high-resolution sources — More pixels enable better analysis
2. Increase contrast — Clear boundaries are easier to detect
3. Preview first — Review chunk boundaries before exporting
4. Adjust sensitivity — Try different levels if initial results aren’t optimal