What is Image Interlacing?
Image interlacing stores scanlines or pixel passes in a reordered sequence so a coarse image can appear before all data arrives. Successive passes refine that preview until the complete image has been decoded.
How Image Interlacing works
Interlaced storage changes transmission order rather than the final decoded pixels. A decoder fills widely separated rows or samples first, producing a rough whole-frame impression, then replaces gaps as later passes arrive. This behavior is distinct from interlaced video fields and from ordinary top-to-bottom raster delivery. It is selected during encoding and matters mainly during incremental network display, while the completed asset retains its normal dimensions and color model.
Key facts
- 1PNG defines the Adam7 scheme, whose seven passes sample progressively denser positions in both axes; a non-interlaced PNG stores rows in normal order.
- 2GIF interlacing rearranges rows into four passes, so a streaming decoder can display the image height early without having received every intervening scanline.
- 3Interlacing can enlarge some files and adds pass bookkeeping; if the client waits for the complete response before decoding, its progressive-display advantage disappears.
When Image Interlacing matters
Enable interlacing when an early full-frame preview is valuable on slow transfers or uncertain connections. It adds encoding or decoding overhead and may provide little benefit when images are already small or delivered quickly.
Common use cases for image
These examples cover image broadly, not specifically Image Interlacing.
- Generating responsive website images, thumbnails, avatars, social cards, and product imagery.
- Standardizing user uploads to safe dimensions, formats, and metadata policies.
- Applying crops, overlays, watermarks, background operations, or visual analysis at scale.
Working with image
This guidance covers image broadly, not just Image Interlacing.
Image software decodes the source into pixels, applies spatial or color operations, and encodes the result. Resize filters, crop coordinates, operation order, and output settings determine both appearance and file size.
Image operations interact with resolution, aspect ratio, alpha, color profiles, orientation, and compression. Test the complete sequence because changing the order of resize, crop, sharpen, and encode operations can change the result.
What you gain
- One source can produce consistent variants for different layouts and devices.
- Automated optimization reduces bytes without requiring editors to prepare every derivative.
- Explicit transformation rules make crops, dimensions, and formats reproducible.
What it costs
- Smaller dimensions and stronger compression reduce transfer size but can remove useful detail.
- Automatic crops scale well but can cut off important subjects when detection or focal information is wrong.
- Wide-gamut, HDR, and transparent assets need an end-to-end path that preserves those properties.
Before production
- 1Test representative dimensions, transparency, color profiles, orientation, and animated inputs.
- 2Compare visual quality at the actual display size, not only at 100% zoom.
- 3Set explicit crop, fit, and upscaling rules so edge cases remain predictable.