A vector encoding scheme is a contract between numbers and visuals. It specifies which index controls what, how values are scaled, and how to handle missing or out-of-range values. If the vector is the genome, the encoding scheme is the genetic code.
You can design the scheme to be simple or sophisticated. The simplest approach uses fixed positions: index 0 controls vertical split, index 1 controls horizontal split, indices 2–13 define four colors, and so on. This approach is easy to implement and easy to reason about.
But as systems grow, you need clarity and stability. An encoding scheme should be explicit, documented, and versioned, because changing the mapping changes the meaning of every stored vector.
Fixed vs. Flexible Schemes
A fixed scheme is stable and predictable. You always know what index 17 means, and it never changes. This is ideal when you want reproducibility or when vectors are stored and shared long-term.
A flexible scheme allows optional features. For instance, you might allow indices beyond 30 to define additional shapes. If those indices are missing, you fall back to defaults. This makes the scheme forward-compatible, but it also requires careful handling to avoid ambiguity.
Scaling and Normalization
Most schemes assume values are normalized between 0 and 1. This makes it easier to map them to sizes, colors, and counts. When values are normalized, you can scale them into ranges that make sense for your canvas:
- Multiply by canvas width for positions
- Multiply by a max size for shapes
- Map to palette indices via rounding
If values are not normalized, you need to clamp or wrap them to avoid visual breakage.
Encoding Colors
Colors are often encoded either as:
- Direct RGB triplets
- Indices into a palette
- Parameters for an algorithmic palette
Direct RGB values give maximum freedom but can lead to incoherent palettes. Palette indices produce more cohesive output and are easier to manage, especially when you want a brand identity. Algorithmic palettes allow gradual transitions across a space of colors.
Encoding Geometry
Geometry encoding includes splits, grids, and patterns. For example:
- A split ratio defines quadrant sizes
- A grid count defines how many rows and columns
- A pattern index selects between grid, radial, or scatter
You can also encode layout constraints, like keeping shapes inside quadrant boundaries or spacing them with a fixed gap.
Handling Length and Defaults
Vectors can be shorter than expected. A robust scheme should handle this gracefully:
- Use defaults for missing values
- Log warnings for missing segments
- Allow partial rendering rather than failure
This makes the system more resilient and easier to evolve.
Versioning the Scheme
As your system evolves, the scheme will likely change. If you store vectors long-term, you need a version number. The version tells your renderer which decoding logic to use. This prevents old vectors from rendering incorrectly under a new schema.
Designing for Search
If you plan to search or cluster vectors, the scheme matters. For example, if index 0 is always a split ratio, then similarity in index 0 corresponds to similarity in layout. This makes vector distance meaningful. If you randomize indices or overload them with different meanings, similarity search becomes less reliable.
Practical Example
Imagine you want a future-proof scheme for quadrant-based graphics:
- Indices 0–1: splits
- 2–13: background colors
- 14–17: shape types
- 18–25: shape sizes
- 26–29: shape counts
- 30–33: pattern modes
- 34–49: offsets for positions
- 50+: experimental features
You can evolve this scheme by appending new features, while preserving the meaning of earlier indices.
A well-designed encoding scheme transforms a random list of numbers into a stable, interpretable visual language. It is the backbone of any vector-driven graphics system.