Short answer: use lossy compression when a controlled reduction in image information produces an acceptable result at the delivered size. Use lossless compression when every pixel matters, when transparency or exact graphics require it, or when visual defects are not acceptable.
What changes in a lossy file
Lossy encoders reduce bytes by representing detail less precisely. The trade is not automatically bad. At an appropriate display size, a well-chosen lossy output can look effectively unchanged while transferring far less data. But aggressive settings can produce ringing around text, banding in gradients, blockiness in low light, and smeared fine texture.
Quality settings are therefore not universal. A scenic photograph, a product fabric close-up, a user-interface screenshot, and a flat illustration react differently to the same encoder setting.
What lossless preserves
Lossless compression reconstructs the original pixel data exactly. It is useful for transparent graphics, screenshots that contain small text, pixel-art, source artwork, and any asset where a single changed edge is not acceptable. It can still reduce bytes, especially for simple graphics, but photographic content may remain heavy.
The useful distinction is not a format name. It is whether the visual task permits approximation. A logo on a transparent background has different needs from an editorial photo below the fold.
Compare at delivery size
Do not judge an image by opening it at 400%. Put it in the actual layout slot and inspect the details a reader is meant to use. Include difficult cases in the test set:
- saturated gradients and subtle shadows;
- small text and interface edges;
- faces, products, and fine hair;
- dark areas and texture;
- transparent graphics over real background colors.
Record the source dimensions, output dimensions, encoder settings, format, file size, and reviewer outcome. A repeatable record turns a subjective disagreement into a decision you can revisit.
A practical selection rule
Start with the visual job and solve unnecessary dimensions first. Then create lossy candidates for photography or other imagery that tolerates approximation. Retain a lossless candidate when the task calls for exact pixels, transparent detail, or a visual review rejects the lossy output.
Modern delivery can also select formats per browser. Cloudinary’s image optimization documentation describes automatic quality and format selection at delivery; that can reduce operational variation, but it does not remove the need to define visual acceptance criteria.
For the complete system around those choices, read the image compression guide and then compress images for the web.
Avoid false comparisons
Comparing a lossless image at full source dimensions with a lossy image that was also resized does not answer a format question. It combines two decisions: dimensions and encoding. First make candidates the same pixel dimensions. Then compare them at the same visual task and inspect the output at the same rendered size. That gives the encoder a fair test.
Likewise, do not declare a format bad from one difficult source image. An encoder can behave differently around a smooth gradient, a grainy low-light photograph, and black text on a flat background. A small representative corpus is more valuable than a perfect test image.
Use exceptions on purpose
A compression policy should allow an exception when the content requires it. A product image that must show material detail may need a less aggressive setting. A screenshot with interface text may need a lossless route. An image with a transparent edge may need a format fallback for older browsers. Record the reason for the exception and review it after a layout or delivery change.
That is better than silently setting every image to the highest possible quality. The team retains the visual standard while still learning where bytes can be removed safely.
Review the delivered result over time
Compression is not a one-time export task. A new responsive layout can expose a larger slot, a browser update can alter format support, and a revised asset can reveal a weakness that was invisible in the original test set. Keep a small recurring review: inspect the hardest assets at their delivered size, compare selected candidates to rendered dimensions, and check whether visual exceptions are becoming common. When the exception rate rises, the policy needs attention rather than another isolated adjustment.