Image upload processing is the server-side pipeline that validates, transforms, and stores user-submitted images before they are used in AI workflows.
Pipeline Stages
Design Trade-offs
- Lossy recompression saves bandwidth but may harm vision-model accuracy.
- High-res originals kept in cold storage increase cost but enable future re-processing.
- Aggressive EXIF stripping loses camera diag info useful for model debugging.
Current Trends (2025)
- On-device HEIC to WebP conversion reduces 30 % upload size.
- GPU-accelerated resizing (CUDA cv2) handles 10 k imgs/s per A10G.
- Privacy laws push default redaction of face landmarks in public dataset ingestion.
Implementation Tips
- Process uploads asynchronously and return 202 Accepted for better UX.
- Generate perceptual hash (pHash) to deduplicate near-identical images.
- Version filenames with content hash to enable immutable caching.