Guide 05 E-commerce · Updated 2026-09-29

Batch-Processing Product Photos for Your Online Store

Product catalogs die by a thousand inconsistent photos: some 4000px, some 800px, mixed formats, nobody knows why the page loads slowly. Here's a repeatable pipeline — one width, one quality, one format — with the arithmetic that explains why it works.

The Pipeline

  1. Fix the delivery width: 1000–1200px covers every mainstream store's zoom view with retina headroom. Measured cost at 1200px, Q70: ≈104 KB per photo (vs ≈191 KB at 1600px).
  2. Resize everything to that width — the Resize tool uses the same high-quality browser downscaling we measured in our resizing study (0.54× file size going 1600→1200, fidelity preserved).
  3. Batch-compress at one quality: drop the whole folder into the compression tool — it queues multiple files — and set quality 70. Consistent settings make a catalog look professional; per-file improvisation makes it look like a yard sale.
  4. Format: WebP if your platform accepts it (measured ~31% smaller than JPEG at matched fidelity for photos — see the format study); JPEG otherwise.

The Arithmetic That Pays for Itself

Take 100 product photos from a modern phone/camera (≈4000px, ≈3 MB each):

State Per photo Catalog (×100)
As shot from camera≈3,000 KB≈300 MB
Resized 1200px + Q70≈104 KB≈10 MB

A 30× reduction, and the 1200px versions are the ones that actually match what shoppers see. Category pages that loaded 20 thumbnails now pay a fifth of a single original photo.

Two Extras Worth 5 Minutes

  • EXIF: re-encoding strips it (measured in our EXIF study) — so your studio's GPS coordinates don't ship with every product photo. If your platform takes original uploads, check one photo with the Image Info tool first.
  • Work locally, upload nothing: a 300 MB catalog batch is exactly the kind of payload upload-based tools choke on (and charge for). Browser-side processing runs at your CPU's speed and the photos never leave your machine — the architecture is measured in our network comparison study.

Run Your First Batch