What is Image Representation?
Image representation describes how visual information is encoded, such as a pixel raster, vector geometry, frequency-domain coefficients, or a learned embedding. Each form preserves different properties and supports different operations.
How Image Representation works
Representation begins with choices about coordinates, sampling, channels, precision, and color interpretation, all of which determine what a pixel value means. Other forms describe paths and fills, transform blocks into frequency coefficients, or summarize visual content as feature vectors. Systems often convert among these forms for editing, compression, rendering, and analysis. The conversion boundary is important because some representations preserve appearance but discard editability, locality, or reconstructable detail.
Key facts
- A raster has a fixed sample grid, whereas vector geometry can be rasterized at multiple sizes; embedded raster content inside a vector file remains resolution-dependent.
- Frequency-domain coefficients encode combinations of spatial patterns rather than independent display pixels, enabling compact coding but requiring a transform to reconstruct.
- A learned embedding is meaningful only with its preprocessing, model, and distance rule; it is generally unsuitable as an archival substitute for the source image.
When Image Representation matters
Use vectors for resolution-independent geometry and rasters when individual sampled colors or photographic detail must be retained. An embedding can accelerate similarity search but cannot generally reconstruct every source detail.