Pixel-by-Pixel Image Diff
Pixel-by-Pixel Image Diff compares a baseline image and a changed image locally in your browser so you can see exactly which pixels changed.
Use this image diff checker for visual regression image comparison, pixel difference checks, UI screenshot QA, alpha-aware RGBA changes, and downloadable diff heatmaps.
Baseline image
Drop or choose a JPG, PNG, WebP, GIF, BMP, or AVIF.
Changed image
Drop or choose a JPG, PNG, WebP, GIF, BMP, or AVIF.
Diff settings
Supported image types
The tool decodes both images locally, reads RGBA pixels, and compares same-size image pairs without uploading them.
Pixel diff appears here
Choose a baseline image and a changed image, then compare same-size pixels locally in your browser.
How to use the pixel image diff checker
What the comparison measures
The tool decodes both uploads into canvas ImageData and compares pixels in RGBA order. Each pixel has red, green, blue, and alpha channel values, so transparent and semi-transparent changes are counted instead of being hidden behind only RGB math.
For matching dimensions, the result summarizes total pixels, identical pixels, differing pixels, difference percentage, average channel delta, maximum channel delta, and an error metric such as RMSE or mean absolute error. These numbers make the result useful for both quick visual QA and repeatable screenshot review.
- Treat alpha as a first-class channel, not an afterthought.
- Report the selected tolerance so copied results can be reproduced.
- Keep exact comparison separate from tolerance-filtered comparison when possible.
Dimension mismatch is a normal result
A true pixel-by-pixel comparison requires both images to have the same width and height. If the baseline and changed image dimensions differ, the tool stops before scoring pixels and explains that the files are different sizes.
This is not presented as a decode failure. The result shows both dimensions, the width and height delta, and guidance to resize, crop, or export the screenshots from the same viewport before running an exact diff.
- Do not count access or paywall usage for a missing second image, unsupported file, decode error, or dimension-only mismatch.
- Show baseline dimensions and changed image dimensions side by side.
- Make the mismatch explanation visible enough for visual regression users who uploaded screenshots from different viewport sizes.
Tolerance and anti-aliasing noise
A tolerance slider helps users ignore tiny channel differences caused by anti-aliasing, browser rendering, compression, font smoothing, or color-management differences. The slider changes which pixels are marked as different without requiring a new upload.
For exact QA, users can set tolerance to zero. For noisy screenshots or compressed images, a small tolerance can make the heatmap focus on meaningful layout, text, color, and transparency changes.
- Use a clear numeric tolerance label, such as maximum per-channel delta or a normalized threshold.
- Recompute differing pixel counts, percentage, average delta, and preview when tolerance changes.
- Consider showing both exact differing pixels and tolerance-filtered differing pixels if the UI has room.
Diff preview and exports
The primary preview makes changed pixels obvious with a heatmap or high-contrast diff mask. Side-by-side mode is useful for comparing the original context, while a diff-only mode is best for finding small changes quickly.
The downloaded PNG is generated from the diff canvas with the same tolerance and view mode currently shown. The copy-all result button includes file names, dimensions, tolerance, counts, percentages, channel deltas, and the exported diff filename when available.
- Provide at least a diff heatmap view and a practical side-by-side view.
- Use canvas export as PNG for a stable downloadable artifact.
- Keep one copy-all button as the required copy path for the complete result.
Good fit and limits
This tool is best for same-dimension screenshots, design exports, icon variants, generated image checks, transparent PNG QA, and browser visual regression triage. It is intentionally separate from tools that inspect transparency, extract alpha, find average colors, generate palettes, or build histograms.
It does not promise semantic image understanding, object matching, automatic alignment, or perceptual scoring beyond the documented pixel and channel metrics. If the images are shifted by one pixel, many pixels may be reported as changed because the comparison is positional.
- Best for exact same-size image pairs.
- Not a replacement for layout-aware or AI visual review.
- Local browser processing is a strong privacy and speed benefit for screenshot and design QA workflows.
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