What is Indexed Color?
Indexed color stores each pixel as a reference to an entry in a finite color palette rather than as full channel values. Its capacity is limited by the number of palette entries available to the image.
How Indexed Color works
An indexed raster separates image samples into an index plane and a color table. Quantization chooses representative palette entries and assigns every pixel to one of them; optional dithering distributes the resulting approximation error. Palette order is part of the encoded data because pixel values refer to positions, not colors directly. This model is selected during export for graphics with limited colors, compact archival assets, or formats and devices built around palettes.
Key facts
- 1GIF color tables contain at most 256 entries, and transparency identifies a palette index rather than storing a separate alpha value for every pixel.
- 2Indexed PNG uses a PLTE chunk for colors and can add per-entry transparency through tRNS; decoders expand these references when producing display pixels.
- 3Editing a palette entry changes every pixel that references that index, which enables cheap recoloring but prevents two such pixels from diverging without reassignment.
When Indexed Color matters
Choose indexed color for palette-based GIF or PNG-8 assets when compact size matters more than full color fidelity. Photographs and smooth gradients may show banding or dithering because many source colors must share palette entries.
Common use cases for image
These examples cover image broadly, not specifically Indexed Color.
- 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 Indexed Color.
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.