Pixelation is fundamentally a downsampling and quantization process that reduces spatial resolution by replacing groups of pixels with a single uniform color value. The algorithm divides the image into blocks of a specified size (the pixel size parameter), computes the average color of all pixels within each block using area averaging, and then fills the entire block with that average color. This process discards the fine detail within each block while preserving the overall color distribution and large-scale structure of the image.

Area averaging, the method used to compute the representative color for each block, sums the red, green, and blue channel values of every pixel in the block and divides by the pixel count. For a 16x16 pixel block, 256 individual pixels are averaged into a single color. This weighted averaging acts as a low-pass filter, removing high-frequency spatial information (fine detail) while retaining low-frequency information (broad color regions and shapes). The larger the block size, the more detail is lost, and the more abstract the result becomes. At extreme block sizes, only the largest color regions of the original image remain recognizable.

The pixelation effect has deep connections to the ancient art of mosaics, which date back over 4,000 years to Mesopotamia. Roman mosaic artists created elaborate images using thousands of small colored tiles called tesserae, each typically 1-2 centimeters square. The principle is identical to digital pixelation: discrete colored units combine to represent a continuous image when viewed from sufficient distance. This relationship between viewing distance and perceived detail is formalized in the concept of visual acuity, where the human eye cannot resolve details smaller than about 1 arc minute of visual angle. Byzantine and Islamic mosaic traditions further refined the art, using smaller tesserae and more colors to achieve greater detail and subtlety.

Pixelation for privacy protection works because facial recognition, both human and algorithmic, depends on spatial relationships between facial features. When a face is pixelated with sufficiently large blocks (typically 16x16 pixels or larger relative to the face region), these relationships are destroyed. Research has shown that pixelation is generally more effective for privacy than Gaussian blur at equivalent levels of image degradation, because blur preserves some edge information that can be partially recovered through deconvolution, while the hard quantization of pixelation permanently destroys the sub-block detail.