Transloadit

What is Dithering?

Dithering adds controlled noise or pixel patterns to randomize quantization error when reducing a signal’s precision. In images, it approximates unavailable colors or gradients and helps suppress visible banding.

How Dithering works

Dither deliberately redistributes rounding error before samples are mapped to a smaller set of output values. Instead of allowing correlated error to form broad contours, it turns that error into finer spatial variation that vision often perceives as a smoother tone. Ordered matrices, error diffusion, and noise-shaped patterns distribute the variation differently. In an image pipeline it is applied near the precision-reduction step, after major tonal operations and before final encoding.

Key facts

  1. Dither must be added before the target quantization; adding noise after values have already collapsed into palette entries cannot recover the missing transition levels.
  2. Ordered dithering is deterministic and parallel-friendly, while error diffusion propagates residuals to neighbors and can preserve local tone at the cost of directional texture and serial dependencies.
  3. The extra high-frequency variation can reduce lossless compression efficiency, and later lossy compression, resizing, or denoising may erase or reorganize the intended pattern.

When Dithering matters

Apply dithering when reducing color depth would otherwise produce contouring or abrupt tonal steps. It preserves perceived gradients at the cost of added texture, larger encoded output, or less compressible patterns.

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