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Compress images in your browser — nothing uploaded

This image compressor shrinks JPG, PNG, WebP and AVIF files right inside your browser. Each image is decoded and re-encoded on an HTML <canvas> element on your own device, so unlike server-based tools such as TinyPNG, Compressor.io, iLoveIMG, and Kraken.io, your files are never uploaded — no queue, no account, and no size cap beyond your device's memory. Squoosh by Google also runs in-browser but is limited to one image at a time; this tool processes entire batches.

Drop in one image or a whole batch, tune the quality or set a target file size, and download the optimized results in seconds. It's the private, fast way to reduce image file size for websites, email attachments, app uploads, Discord sharing, and storage.

Hit an exact target size — under 2 MB, 200 KB, or any size

Need to compress an image to 2 MB, fit a 200 KB upload form, or reduce an image size in KB? Switch to Target size mode, type your limit, and the tool automatically binary-searches the encoder quality so the result lands just under your target at the highest quality possible. No trial and error — unlike the manual sliders on TinyPNG or Squoosh.

It's useful for upload forms, marketplace listings, government portals with strict caps, and chat apps — including a Discord image compressor workflow: set 10 MB to match Discord's free-tier limit and your image is guaranteed to fit. Kraken.io offers a similar target-size feature in its paid API; this tool provides it free, with no account.

Bulk compress and download as a ZIP

This is a true bulk image compressor. Select dozens of images at once and your quality, format, and resize settings apply to every one simultaneously. Each image shows its own before/after size and percentage saved, plus a running total for the whole batch. TinyPNG caps free batch uploads at 20 images; iLoveIMG processes batches server-side with daily limits — neither restriction exists here.

Download images individually, or grab the entire set as a single ZIP — the archive is assembled locally in your browser, so even bulk jobs never leave your device.

Choose the best format — or let Auto pick the smallest

Keep the original format or convert to WebP, JPEG, PNG, or AVIF. WebP and AVIF produce files 25–35% smaller than JPEG at the same visual quality, which is why they're the standard for fast websites. Squoosh popularized in-browser format comparison; this tool applies the same logic to entire batches.

Not sure which to use? Pick Auto (smallest) and the compressor encodes each image in several formats and keeps whichever is smallest — while respecting transparency, so logos and screenshots never get a flattened background. TinyPNG focuses only on PNG and WebP; this tool considers all four major formats including AVIF.

Resize and strip metadata for even smaller files

Photos from modern phones are often 4000+ pixels wide — far more than a web page or upload needs. Set an optional Max width or Max height to downscale them, and the file size drops sharply with no visible loss at normal display sizes. Combining resize with quality compression routinely cuts phone photos by 80–90%.

By default the tool also strips EXIF metadata — camera model, timestamps, and GPS location — which protects your privacy and trims a few extra kilobytes. Images you share online won't carry your location or device data. Prefer to keep it? Turn off Strip metadata and the original EXIF carries over when you save back to JPEG.

TinyPNG vs Squoosh vs Compressor.io vs iLoveIMG vs UtiloKit

TinyPNG is the go-to for PNG and WebP lossless compression — accurate and trusted. Its limits: files upload to TinyPNG's servers, free batches max out at 20 images, no AVIF support, no target-size mode, no bulk resize. Squoosh (Google) runs in-browser like this tool but has no batch mode, making it slow for large collections. Compressor.io is fast and has a clean interface but uploads files server-side and lacks batch or target-size features. iLoveIMG provides good batch compression but is server-based with daily free limits and no target-size option. Kraken.io is professional-grade but requires a paid plan for meaningful use.

UtiloKit's image compressor is the only free tool that combines: in-browser processing (no upload, like Squoosh), true bulk mode (like iLoveIMG), target-size compression (like Kraken.io's paid tier), AVIF support, and a before/after preview slider — at no cost, no account, full privacy. It's the strongest all-round alternative whether you're coming from TinyPNG, Squoosh, Compressor.io, iLoveIMG, or Kraken.io.

How JPEG compression actually works: DCT, quantization, and entropy coding

Every JPEG encodes images through three consecutive stages, each engineered to discard what the human visual system is least likely to notice. First, the image is divided into non-overlapping 8×8 pixel blocks, and each block is fed through a Discrete Cosine Transform (DCT) — essentially a visual Fourier transform that converts the 64 pixel values into 64 frequency coefficients. The top-left coefficient (the DC term) encodes the average brightness of the block; the remaining 63 AC terms encode progressively finer spatial frequencies, from coarse horizontal and vertical stripes down to a checkerboard pattern of 8×8 alternating pixels. Most of a natural photograph's energy concentrates in the low-frequency coefficients; high-frequency terms are small or near zero.

The second stage — quantization — is where data is permanently and irreversibly lost. Each coefficient is divided by a value from a quantization table and then rounded to the nearest integer. The quality setting you choose determines the quantization table: a high quality setting uses small divisors (little rounding, big coefficients survive intact); a low setting uses large divisors (aggressive rounding drives many coefficients to zero). This is why lowering JPEG quality beyond roughly 60% creates visible blocking artifacts at sharp edges and ringing around fine detail — entire frequency terms have been zeroed out and the decoder reconstructs the block without them. Alongside quantization, JPEG typically applies chroma subsampling, storing the luma (brightness) channel at full pixel resolution but the two color-difference channels at half or quarter resolution in each direction. Human vision resolves color far less sharply than luminance, so 4:2:0 subsampling (2:1 in both axes) is nearly invisible and alone accounts for roughly one-third of a JPEG's file-size reduction compared to a raw image.

The final stage is entropy coding. After quantization, most high-frequency coefficients are zero; the encoder serializes the coefficients in a zig-zag order that clusters zeros together, then compresses the result with Huffman coding (or arithmetic coding in some implementations). Huffman coding assigns shorter bit patterns to values that appear frequently, squeezing the last redundancy out of the quantized data losslessly. The result is a JPEG file that typically weighs 5–20× less than the raw pixel data, with quality loss almost entirely attributable to the quantization step — not the DCT and not the entropy coding.

Lossless vs. lossy compression: choosing the right approach for each image type

Lossless compression — used by PNG, lossless WebP, and lossless AVIF — encodes an image so that every pixel is mathematically identical to the original after decoding. No data is discarded; the file is simply stored more efficiently using techniques like DEFLATE (the algorithm inside PNG's zlib layer), which replaces repeated byte sequences with shorter references. Because no information is thrown away, lossless files are always decodable back to the exact original, making them the right choice for screenshots, logos with text, diagrams, icons, and line art — anywhere where pixel-perfect accuracy matters or where the image will be edited and re-saved repeatedly. Every re-save of a lossy file re-applies quantization, compounding artifacts; with lossless formats, that degradation cannot occur.

Lossy compression — used by JPEG, lossy WebP, and lossy AVIF — permanently removes data the encoder predicts the eye will not miss. For photographs and continuous-tone images, the savings are dramatic: a raw camera image at 24 megapixels might weigh 70 MB uncompressed; a high-quality JPEG shrinks it to 5–10 MB; WebP or AVIF at equivalent quality often halves that further. The tradeoff is that once lost, the discarded data is gone — saving a lossy file again at the same quality introduces a second round of loss, so always keep a lossless master and export lossy copies as needed.

The standard scientific metric for measuring how much lossy compression has degraded an image is SSIM — Structural Similarity Index. SSIM compares luminance, contrast, and structural patterns between the compressed image and the original, producing a score from 0.0 (completely different) to 1.0 (bit-for-bit identical). Perceptual studies consistently show that images with SSIM above 0.95 are visually indistinguishable from the original to most observers under typical viewing conditions. A quality setting of 75–85% in JPEG or WebP usually achieves SSIM values in the 0.96–0.99 range for natural photographs while cutting file size by 60–80% — which is why this quality range is the industry default used by Google's Lighthouse audits, Squoosh's default preset, and professional CDN pipelines.

Modern image format comparison: WebP, AVIF, JPEG XL, and PNG

WebP, released by Google in 2010, was the first widely deployed next-generation format and remains the most broadly compatible. Its lossy mode uses a block-prediction scheme derived from the VP8 video codec, achieving files roughly 25–34% smaller than JPEG at the same perceived quality. Its lossless mode uses a separate spatial prediction and entropy coding pipeline that typically produces files 20–30% smaller than PNG. WebP is now supported by every major browser — Chrome, Firefox, Safari 14+, Edge — making it the safe default for web images in 2024. When you export WebP at quality 80, you typically match the visual quality of JPEG 90 in a notably smaller package.

AVIF (AV1 Image File Format, standardized in 2019) takes compression efficiency further still: independent benchmarks consistently show AVIF producing files roughly 50% smaller than JPEG at equivalent perceived quality, and it outperforms WebP by 15–25% at low-to-medium quality settings where compression artifacts in WebP become apparent. AVIF also natively supports HDR (High Dynamic Range) and wide color gamut, film grain synthesis, and 10/12-bit depth — capabilities absent from JPEG and WebP. The practical tradeoff is encoding speed: AVIF encoding in a browser can take several seconds per image versus milliseconds for JPEG or WebP, and browser support, while growing (Chrome 85+, Firefox 93+, Safari 16+), still excludes some older mobile browsers. For new photography-heavy sites targeting modern audiences, AVIF is the best-performing choice.

JPEG XL (ISO/IEC 18181, finalized in 2022) introduces a capability unique among image codecs: lossless recompression of existing JPEG files with zero quality loss and roughly 20% smaller output, using a process called transcoding. For new encodes, JPEG XL achieves compression efficiency comparable to AVIF and adds progressive decoding (images load recognizably at low resolution before the full file arrives), very fast software decoding, and excellent support for HDR and animation. Its adoption has been slowed by limited browser support — Chrome removed its experimental flag in 2022, though Firefox and Safari 17+ have added it. For PNG files, pngquant palette quantization deserves separate mention: it reduces a 24-bit PNG to an 8-bit indexed palette, cutting file size 60–80% while maintaining near-lossless visual quality for most illustrations and graphics. Many users never need JPEG XL today, but for archival purposes or workflows that round-trip through JPEG, it is the only lossless path to a meaningfully smaller file.

Image optimization and web performance: Core Web Vitals, LCP, and next-gen formats

Google's Core Web Vitals measure the real-world experience of loading a page, and image weight is the single largest lever for two of the three metrics. Largest Contentful Paint (LCP) — which measures how quickly the biggest visible element (almost always a hero image or above-fold photo) becomes fully rendered — must occur within 2.5 seconds on a median mobile connection to score 'Good'. An unoptimized 3 MB hero JPEG routinely causes LCP failures on 4G connections. Compressing that image to 200–400 KB with WebP or AVIF, combined with a width and height attribute on the <img> tag to prevent layout shift, is often the highest-impact single change available to a site's performance score.

Serving images at the correct intrinsic dimensions matters as much as format choice. A photograph shot at 4000×3000 pixels displayed in a 400px-wide card on mobile is transferring roughly 12× the pixel data actually shown — the browser decodes all 12 megapixels and then downscales in GPU memory. Resizing the image to 800px wide (2× for retina screens) before serving it eliminates that wasted bandwidth. The correct pattern for modern websites is the <picture> element with source negotiation: offer an AVIF source first, then WebP, then JPEG as a fallback — the browser picks the most efficient format it supports. Pair this with srcset and sizes attributes so the browser also selects the appropriate resolution for the viewport, and you cover both format efficiency and dimensional correctness in a single markup block.

Lazy loading with loading="lazy" defers the download of every below-fold image until the user scrolls near it, directly reducing the data transferred on page load and freeing bandwidth for above-fold LCP assets. For images that are visible on arrival — particularly the LCP element — use fetchpriority="high" and avoid lazy loading, which would introduce an unnecessary delay. Image CDNs such as Cloudinary, Imgix, and Bunny Optimizer apply all of these optimizations automatically on the fly: they accept your full-resolution master, and serve appropriately sized WebP or AVIF in response to each browser request based on the Accept header and viewport hints. For high-traffic sites, an image CDN offloads the compression pipeline entirely; for smaller projects or one-off tasks, a browser-local compressor like this tool achieves the same results without monthly CDN costs or API integration.