Downscale & DCT Analysis
The source image is pre-scaled to ≤128px for sub-millisecond CPU efficiency. A 2D Discrete Cosine Transform separates luminance/chrominance into 1 DC (average) and AC (spatial gradient) components.
Zero-build technical demonstration of client-side Discrete Cosine Transform (DCT) image hashing. Placeholders decode instantly from short Base83 strings and smoothly transition to full-resolution photographs.
A compact representation of a placeholder image stored in 20–30 printable ASCII characters.
The source image is pre-scaled to ≤128px for sub-millisecond CPU efficiency. A 2D Discrete Cosine Transform separates luminance/chrominance into 1 DC (average) and AC (spatial gradient) components.
AC factors are scaled, non-linearly quantized, and packed into Base83 characters safe for HTML, JSON, and URLs. String length is strictly deterministic: 4 + 2 × X × Y characters.
The browser client decodes the string into a micro canvas (32×22px). The browser’s native bilinear texture filtering naturally interpolates the low-frequency gradients at zero memory cost.
Scales AC coefficient magnitude during decoding. Smaller values (<1.0) yield smoother, flatter tones; higher values (>1.0) intensify contrast and color variance across all placeholders.
Drop or select any local image to execute 100% in-browser DCT encoding via CDN blurhash@2.0.5. Images downscale to max 128px before encoding per performance guidelines.
Decoded from compact strings into 32×22 canvases. Real photos lazy-load via IntersectionObserver and smoothly fade in.