图像中像素选区存储的便捷数据结构选型问询
Great question! When dealing with pixel selections (especially those with holes and smaller than the full image), there are several convenient data structures tailored to different use cases. Let’s break them down based on your core needs: save/load, isolated view display, and algorithm integration.
1. Binary Mask Bitmap
This is the most straightforward and widely used option in image editing tools. You create a bitmap with the same dimensions as the selection’s bounding box, where each pixel is 1 (or white) if it’s part of the selection, and 0 (or black) otherwise—including holes marked as 0.
- Pros:
- Seamless integration with almost all image processing algorithms (e.g., convolution, fill, filtering) since most libraries natively support mask inputs.
- Super easy to render for isolated view display—just draw the mask directly, or overlay it on a transparent background.
- Saving/loading is trivial: use lossless formats like PNG (which compresses sparse masks very efficiently) or binary files for compact storage.
- Cons:
- Wastes space if the selection is extremely sparse (e.g., a few scattered pixels in a large bounding box). But for most practical cases, this is negligible given modern storage.
2. Set of Pixel Coordinates
Store the selection as a collection of (x, y) coordinate pairs (relative to the bounding box’s top-left corner, not the full image). You can use a hash set for O(1) lookups, or an ordered list if you need to preserve selection order.
- Pros:
- Ultra-space-efficient for sparse selections or those with lots of holes—you only store the pixels that are actually selected.
- Serialization for save/load is simple: use JSON, CSV, or a custom binary format to store the coordinate list.
- Perfect for algorithms that need to iterate over every selected pixel (e.g., applying a color tweak only to selected pixels).
- Cons:
- Less efficient for neighborhood-based operations (e.g., blur within the selection) since you’d need to check adjacent pixels manually, whereas a mask bitmap lets you use optimized library functions.
3. Run-Length Encoding (RLE)
For selections with large contiguous blocks (like rectangular or lasso-drawn regions with few holes), RLE compresses the data by storing consecutive runs of selected pixels per row. For example, for a row where pixels 5-12 and 15-20 are selected, you’d store [(5,8), (15,6)] (start x, length).
- Pros:
- More compact than a full mask bitmap for continuous selections, and faster to serialize than a coordinate set.
- Easy to render for isolated views: just draw horizontal line segments for each run.
- Works well with row-by-row image processing algorithms.
- Cons:
- Less efficient for highly fragmented or scattered selections, where the number of runs approaches the number of selected pixels.
4. Vector Path/Polygon Collection
If your selection was created using vector-based tools (like a pen tool or polygonal lasso), store it as a set of vector paths—including separate paths for holes (defined in reverse winding order, as is standard in graphics).
- Pros:
- Near-zero storage size, even for large selections.
- Preserves editability (you can resize, reshape the selection later without pixel loss).
- Ideal for isolated view display since vector renderers can draw crisp edges at any scale.
- Cons:
- Requires rasterization (converting to a mask bitmap) before use in pixel-level algorithms, which adds a processing step.
- Not suitable for selections created with tools like the magic wand, which produce arbitrary pixel patterns.
Practical Recommendations
- If you’re integrating with standard image editing workflows or pixel-level algorithms: go with a binary mask bitmap—it’s the most compatible and lowest-friction option.
- For sparse, scattered selections: use a coordinate set to save space.
- For continuous, blocky selections: RLE balances space efficiency and ease of use.
- If you need to retain the ability to edit the selection’s shape later: vector paths are the way to go.
内容的提问来源于stack exchange，提问作者Tom
