Files Compressor
Free, private, browser-based tools to shrink images, PDFs and video, or zip/unzip files. Nothing you upload here ever leaves your device — every tool below runs entirely in your browser.
Image Compressor
Shrink JPG, PNG or WebP images with adjustable quality and resize.
Open Image Compressor → 📷JPG Compressor
Convert and compress any image straight to an optimized JPG.
Open JPG Compressor → 📄PDF Compressor
Shrink PDF file size with a quick optimize or maximum compression mode.
Open PDF Compressor → 🎬Video Compressor
Re-encode video to a smaller file size right in your browser.
Open Video Compressor → 🗜️Zip & Unzip Files
Bundle files into a .zip archive, or extract files from an existing one.
Open Zip & Unzip →Lossless vs Lossy Compression: What Actually Happens to Your File
Every compression tool is exploiting redundancy — finding the parts of a file that are predictable or repeated, and representing them with fewer bits. How that redundancy is removed determines whether the process is lossless or lossy, and the distinction matters when you're picking which tool to use.
Lossless compression guarantees that decompressing a file returns exactly the original bytes, bit for bit. ZIP and PNG both rely on algorithms built around DEFLATE, which combines dictionary substitution — replacing repeated byte sequences with short references back to an earlier occurrence — with Huffman coding, which assigns shorter binary codes to frequent symbols. Because neither step discards information, only re-encodes it more efficiently, the original data can always be reconstructed exactly. This makes lossless compression the right choice for documents, spreadsheets, and any archive where a single altered bit could break something. The trade-off is that compression ratios are limited by how much genuine redundancy a file contains — a file that's already compressed, like an MP4 video, barely shrinks further under DEFLATE.
Lossy compression takes a different approach: it permanently discards data that's least likely to be noticed, in exchange for much smaller files. JPEG is the clearest example. It separates brightness from color information, then reduces color-channel detail more aggressively than brightness detail, since the eye is far more sensitive to changes in brightness. Fine detail that's less likely to be noticed gets rounded away during compression, and that loss is irreversible — which is why repeatedly re-saving the same JPEG gradually degrades it, while a single well-tuned export doesn't.
Neither approach is objectively better — they solve different problems. If you need the file to come back byte-for-byte identical (a document, a code archive), use lossless. If you're shrinking a photo or video for the web and a little quality loss is invisible in practice, lossy formats get you a much smaller file for the same visual result.
A Quick History of the Formats You're Using
ZIP was created by Phil Katz and released through his company, PKWARE, in the early 1990s, introducing the DEFLATE-based approach that became one of the most widely adopted compression methods in computing. Its combination of an open specification and strong compression ratios made it the de facto standard for archiving and file bundling across nearly every operating system that followed.
JPEG emerged from the Joint Photographic Experts Group, the joint committee of the ISO and ITU-T standards bodies that gives the format its name, with the standard finalized in the early 1990s. It was designed for photographic images, where its approach could deliver large size reductions with minimal perceptible quality loss — a tradeoff that made photography practical to store and transmit at a time when storage and bandwidth were far more limited than today.
PNG arrived a few years later in the mid-1990s, developed largely as a free, patent-unencumbered alternative to GIF, whose underlying compression was subject to licensing claims at the time. PNG's lossless approach made it well suited to graphics, screenshots, and images with sharp edges or transparency — cases where lossy blurring artifacts would be far more noticeable than in a photograph.
WebP is the newest of the group, developed by Google and released in the early 2010s to fill a gap the older formats left open: a single format offering both lossy and lossless modes, generally producing smaller files at comparable visual quality to JPEG or PNG.
| Format | Compression Type | Typical Use Case |
|---|---|---|
| ZIP | Lossless (DEFLATE) | Archiving and bundling files of any type |
| PNG | Lossless (DEFLATE + filtering) | Graphics, screenshots, images needing transparency |
| JPEG | Lossy (DCT + quantization) | Photographs and continuous-tone images |
| WebP | Lossy or lossless (selectable) | Web images needing smaller file sizes than JPEG/PNG |
| Video codecs (e.g. H.264/H.265) | Lossy (spatial + temporal) | Streaming and storing video efficiently |
| PDF (with image downsampling) | Mixed (lossless structure, lossy embedded images) | Documents combining text and images |
Frequently Asked Questions
Will compressing a JPEG multiple times keep degrading its quality?
Yes, to a degree. Each time a JPEG is decoded and re-encoded, the quantization step discards additional high-frequency detail, so repeated lossy compression cycles introduce cumulative artifacts. Compressing a file once from a high-quality source at a sensible quality setting avoids this; compressing an already-compressed JPEG repeatedly is where visible degradation builds up.
Why does a ZIP file sometimes barely shrink certain files?
DEFLATE relies on finding repeated patterns and predictable structure in the data. Files that are already compressed — JPEGs, MP4 videos, other ZIP archives — contain very little redundancy left to exploit, so zipping them typically yields only a small size reduction, sometimes none at all.
Is it safe to compress a PDF that contains important documents?
Compressing a PDF's structure and removing redundant embedded data is lossless and doesn't affect content. If the process also downsamples or re-encodes embedded images, that specific image data becomes lossy, though surrounding text and layout remain unaffected.
What's the difference between compressing a video and simply lowering its resolution?
Lowering resolution reduces the number of pixels the codec has to encode, while compression settings (bitrate, codec, quantization level) determine how efficiently those pixels are encoded. The two are independent levers that can be combined depending on how much size reduction is needed.
Does processing files in the browser instead of uploading them to a server change how compression works?
No — the underlying algorithms (DEFLATE, DCT-based JPEG encoding, and so on) are identical regardless of where they run. Modern browsers execute this code locally using JavaScript and WebAssembly, so the process matches a server-side tool; the difference is that file data never has to leave the device.