Transloadit

What is Bicubic Interpolation?

Bicubic interpolation estimates each resized pixel from a weighted neighborhood of source pixels. It generally produces smoother results than bilinear interpolation but requires more computation.

How Bicubic Interpolation works

Bicubic interpolation reconstructs a sample from a four-by-four neighborhood, applying cubic functions across both image axes. The wider support preserves smooth gradients and edge transitions better than simpler linear sampling in many photographic resizes, at higher computational cost. “Bicubic” describes a family rather than one exact kernel, so implementations can differ in sharpness and ringing. A transform pipeline chooses the variant alongside sharpening and color-space handling.

Key facts

  1. Each output sample normally depends on 16 nearby source samples, compared with four for bilinear interpolation, increasing arithmetic work and the area affected by an edge.
  2. Cubic kernel parameters control whether output looks softer or sharper; two libraries labeled “bicubic” can therefore produce visibly different pixels from the same source.
  3. High-contrast boundaries may develop halos or ringing from negative kernel lobes, while repeated resizing can compound blur and artifacts even with a high-quality filter.

When Bicubic Interpolation matters

Choose bicubic scaling for photographs or general-purpose resizing when visual quality matters more than minimum processing time. Sharpening artifacts or softness may still require another filter for specific images.

Turn media knowledge into a working pipeline

Connect uploads, processing, AI, storage, and delivery through one declarative API — with the encoding stack, scaling, and format churn handled for you.

Try Transloadit for free