# What is Bilinear Interpolation?

Bilinear interpolation estimates a new pixel by applying linear interpolation across the four nearest source pixels in two dimensions. It offers moderate smoothing with relatively low computational cost.

Source pixels

Image processing

Image derivative

Image processing maps source pixels and metadata into a derivative with deliberate dimensions and encoding. This diagram shows image broadly, not specifically Bilinear Interpolation.

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## How Bilinear Interpolation works

Bilinear interpolation locates an output sample within a source grid cell and blends the four corner samples according to horizontal and vertical distance. It performs a linear blend along one axis and then the other, although the combined surface includes an interaction between coordinates. The small neighborhood makes it fast and predictable but often softer during enlargement. Renderers use it for routine scaling, transforms, and texture sampling.

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## Key facts

1. 1The weights of the four source samples sum to one for an interior point, preserving a constant-color region while smoothly changing influence across a grid cell.
2. 2Bilinear filtering is separable, so implementations can interpolate horizontally and vertically in stages; this is useful for optimized image and texture pipelines.
3. 3Because it cannot reconstruct frequencies lost or absent in the sample grid, large upscales show blur, and downscales still need adequate low-pass filtering to avoid aliasing.

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## When Bilinear Interpolation matters

Use bilinear interpolation for fast resizing, texture sampling, or geometric transforms where moderate quality is sufficient. Fine edges may blur, so detailed artwork can benefit from a higher-quality filter.

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## Common use cases for image

These examples cover image broadly, not specifically Bilinear Interpolation.

* 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.

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## Working with image

This guidance covers image broadly, not just Bilinear Interpolation.

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

1. 1Test representative dimensions, transparency, color profiles, orientation, and animated inputs.
2. 2Compare visual quality at the actual display size, not only at 100% zoom.
3. 3Set explicit crop, fit, and upscaling rules so edge cases remain predictable.

[← Bicubic Interpolation](/glossary/bicubic-interpolation.md)[Binarization →](/glossary/binarization.md)

More in image

* [Aspect Ratio](/glossary/aspect-ratio.md)
* [Automatic Cropping](/glossary/automatic-cropping.md)
* [Bicubic Interpolation](/glossary/bicubic-interpolation.md)
* [Binarization](/glossary/binarization.md)
* [Binary Image](/glossary/binary-image.md)
* [Bit Depth](/glossary/bit-depth.md)

[All 505 terms](/glossary.md)

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