Bildkompressor

No. The WikiPlus Image Compressor runs entirely in your browser using WebAssembly-compiled encoding libraries. Every step of the compression pipeline — file reading, pixel decoding, quality resampling, and output encoding — happens on your own device inside your browser tab. Your images are never transmitted to WikiPlus servers or to any third-party service. There is no network request carrying image bytes at any point during processing. This architecture matters far more than many users realise. Think about the images you might compress before uploading: scanned documents containing medical records, financial statements, or legal contracts; product photographs of unreleased hardware still under NDA; screenshots of internal dashboards or confidential Slack conversations; ID cards or passports resized for an application form. Each of these categories carries serious privacy risk if processed through a cloud API where you cannot audit the storage policy, data retention window, encryption practices, or employee access controls. With WikiPlus, the risk does not exist because the server never receives the file. You can verify this claim yourself with a simple test: load the WikiPlus Image Compressor page in your browser, wait until it fully initialises, then disconnect your device from the internet completely — turn off Wi-Fi, or enable airplane mode. Now drop an image into the tool and compress it. The compression completes successfully and produces a downloadable output, proving conclusively that the entire pipeline runs locally with no server dependency after the initial page load.

The WikiPlus Image Compressor accepts JPEG, PNG, and WebP as input formats and outputs a compressed version in the same format as the input by default. This format-preserving behaviour is intentional: it keeps your existing workflow compatible with downstream tools, CMS upload requirements, email attachment size limits, and application form constraints that specify a particular format. Animated GIFs and HEIC files are not supported as compression input. AVIF input is not currently supported either. If your source image is in HEIC format — the default for iPhone camera photos when shot in High Efficiency mode — convert it to JPEG first using the WikiPlus Image Converter, then run it through the compressor. If you want to change format and compress simultaneously, the most efficient workflow is to compress first at the original format, then convert to the target format using the WikiPlus Image Converter. Converting JPEG to WebP after compression typically reduces file weight by an additional 25 to 35 percent compared to a quality-matched JPEG, with no perceptible visual difference in photographic content. PNG to WebP conversion yields similar savings. For logos and icons with transparency, converting compressed PNG to WebP preserves the alpha channel while cutting file size. SVG files should not be run through a raster image compressor — use a dedicated SVG minifier that operates on the XML markup instead. GIF animation frames should be exported individually as PNGs, compressed, and then either reassembled or converted to a more efficient animated format.

Lossy compression always involves a mathematical trade-off between file size and pixel accuracy, but the perceptual impact is highly dependent on the quality setting you choose and the content type of the image. For photographic content — portraits, product shots, landscapes, food photography — a quality setting of 75 to 85 on a scale of 0 to 100 produces output that is visually indistinguishable from the original to most viewers at normal screen viewing distances. At these settings, file size reduction typically falls between 40 and 70 percent compared to the original, depending on the source resolution, colour complexity, and the amount of fine detail in the scene. High-contrast content such as text overlays on images, screenshots with thin interface elements, diagrams, line art, and PNG images with transparency are more sensitive to quality reduction. These content types show compression artefacts — most visibly as colour banding near edges and blockiness in areas of uniform colour — at quality settings below 85. For this content, keep the quality slider at 88 to 92 to preserve clean edges. The WikiPlus compressor shows a side-by-side preview panel with zoom support before you commit to downloading. Always use the preview to inspect the most detail-critical areas of your image — fine text, logos, skin tones — at 100 percent zoom. This gives you direct visual evidence of quality at your chosen setting rather than relying on generalised advice. Google's PageSpeed guidelines recommend keeping web images under 200 kilobytes for optimal Core Web Vitals scores.

WikiPlus imposes no artificial file size cap, no session quota, and no per-day image limit on the compressor. There is no premium tier required to process larger files or higher volumes. The practical ceiling is determined entirely by the available free memory on your device, since the compression pipeline must decode each image into a full uncompressed pixel bitmap in RAM before the encoder can operate on it. This RAM requirement is larger than the file size: a 5 MB JPEG may decode into a 30 to 60 MB bitmap depending on its resolution. On a modern laptop or desktop with 8 GB or more of RAM, you can reliably handle images up to approximately 50 megapixels — roughly equivalent to a 24 MP full-frame DSLR photo, a large high-altitude drone shot, or a stitched panoramic image. On a smartphone with 3 to 4 GB of RAM the practical per-image limit is lower. Most standard smartphone photos in the 10 to 20 megapixel range compress smoothly. Very large raw-converted TIFFs or 100 MP medium-format images may exhaust browser memory on mobile and cause the tab to become unresponsive. If that happens, close all other browser tabs and applications to free RAM, reduce the source image resolution before importing, or switch to processing one image at a time instead of running a batch. For batch compression, the tool processes images sequentially rather than in parallel, which keeps peak memory usage manageable even with larger queues.