Advanced image studio

Resize precisely.
Compress with control.

Choose a resampling filter, output size, format, and quality. Process up to 25 images in one batch.

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Image resizing, explained

Resize first. Encode for the way you use it.

An image file has two different properties that are easy to confuse: its pixel dimensions and the way those pixels are compressed. Resizing changes the image's pixel grid. Encoding stores the resulting grid as WebP, JPEG, or PNG. A good export starts by choosing the right dimensions for the job, then selecting a reconstruction filter and a format that fits the image content.

Compressy brings those decisions together in a free browser-based image resizer and compressor. Prepare product photos, article images, thumbnails, screenshots, illustrations, and other still raster images in batches of up to 25 files, without sending the image contents to a processing server.

25images per batch
5resampling filters
3export formats
40 MPper-image safety limit

The science behind resizing

What happens when an image is resized?

A raster image is a grid of samples

A digital photograph is made from a rectangular grid of pixels. Each pixel stores color information at a particular position. When you change the width or height, the software must map the old grid onto a new one. Most new pixel positions do not line up exactly with the old pixel centers, so their values have to be estimated from nearby samples. This process is called resampling or image interpolation.

The filter defines how neighboring samples contribute to each output pixel. Different filters produce different balances between sharpness, smoothness, edge definition, and artifacts. No conventional resizing filter can recover information that was absent from the original image.

Downscaling and upscaling are different problems

Downscaling maps many source pixels to fewer output pixels. Fine patterns can become unstable or form moiré if detail above the new sampling resolution is not adequately reduced. This is why anti-aliasing matters: high-frequency detail must be softened or filtered before it is represented on a coarser pixel grid.

Upscaling estimates additional pixels between the existing ones. A sharper filter can make the result look more defined, but it cannot recreate genuine texture or detail that the source never contained. Use the largest clean original available and enlarge only as much as the layout requires.

Useful distinction: resampling changes pixel dimensions; compression changes the number of bytes used to store the output. Reducing a 4000 × 3000 image to 2000 × 1500 cuts its pixel count by 75%, but the final file size depends on the format, quality setting, and image content.

Resampling filters

Five filters, five different visual trade-offs

A reconstruction filter is a mathematical weighting rule: it determines how nearby source pixels contribute to each new pixel. There is no universally best filter for every photograph, logo, texture, or illustration. Choose according to the edges and detail in the image, then inspect the result at its intended display size.

Lanczos-3 Fine detail and crisp downscaling

Lanczos uses a windowed sinc function, with a three-lobe window in the Lanczos-3 form. Its weighted reconstruction can retain fine texture and edge definition, making it a strong general-purpose choice for detailed photographs, architecture, landscapes, and product imagery. The trade-off is that sharp transitions can sometimes develop faint light or dark echoes called ringing. Inspect high-contrast edges, small text, and thin lines when using it.

Try it for: detailed photos, web hero images, product images, and substantial reductions where preserving texture matters.

Bicubic · Catmull–Rom Crisp cubic reconstruction

Catmull–Rom is an interpolating cubic filter: it reconstructs a smooth curve through the original samples and tends to give edges a crisp, defined appearance. It is useful for photographs and illustrations when a slightly more assertive result is desirable. That extra edge definition can also emphasize halos or overshoot near very sharp light-dark boundaries.

Try it for: illustrations, moderate photo resizing, and images where a crisp result looks better than a softer reconstruction.

Mitchell–Netravali Balanced cubic reconstruction

Mitchell–Netravali describes a family of cubic reconstruction filters. The balanced setting used here (B = 1/3, C = 1/3) aims for a compromise between sharpness and smoothness rather than maximizing either one. It is a good alternative when Lanczos or Catmull–Rom looks too edgy, or when a softer transition is more pleasing. It may appear slightly less crisp on tiny details.

Try it for: mixed image libraries, portraits, softer photographic detail, and high-contrast edges that look too pronounced with a sharper filter.

Bilinear Simple, smooth interpolation

Bilinear interpolation estimates a new pixel by blending the four nearby samples around its position, with weights based on distance. It is computationally simple and produces smooth transitions, but it can soften fine detail. Large reductions of repeating patterns or very detailed textures may show aliasing or loss of definition compared with filters that consider a wider sampling footprint.

Try it for: small adjustments, soft imagery, previews, and cases where a smooth, uncomplicated result is more important than maximum detail.

Nearest Neighbor Preserves hard pixel boundaries

Nearest Neighbor assigns each output position the value of its closest source sample. It does not blend neighboring colors, so every original pixel remains a distinct color block. This is exactly what pixel art and intentionally block-based graphics need. On photographs, however, it creates visibly jagged edges and can discard important detail when reducing dimensions.

Try it for: pixel art, retro game sprites, low-resolution UI assets, and graphics where exact color blocks matter more than smoothness.

Quick decision guide

Which resampling filter should you choose?

Image or goalStart withWhyWatch for
Detailed photographsLanczos-3Retains texture and crisp detailSubtle ringing on hard edges
Crisp illustrationsCatmull–RomDefined cubic reconstructionPossible edge overshoot
Portraits or softer imagesMitchell–NetravaliBalanced sharpness and smoothnessMay look less sharp on tiny details
Small, modest adjustmentsBilinearSimple and smooth interpolationFine detail can soften
Pixel art and spritesNearest NeighborKeeps original color blocks intactJagged appearance on photographs
Not sure where to startLanczos-3A versatile first comparison for photosCompare against Mitchell for edge halos

File formats and compression

WebP vs JPEG vs PNG: choose by image type

The file extension is not just a label. Each format makes different choices about compression, transparency, and visual fidelity. Compressy exports WebP, JPEG, and PNG; the best format depends on whether you are optimizing a photograph, a screenshot, a logo, or a transparent graphic.

Best all-round web choice

WebP

WebP supports both lossy and lossless encoding, transparency, and animation at the format level. It is widely supported in modern browsers and is often a compact choice for photographs and web graphics. Compressy's quality selector controls its lossy WebP export, so choose PNG when you specifically need lossless storage of the exported pixels.

  • Product photos and article imagery
  • Website thumbnails and card images
  • Graphics that need an alpha channel

Remember: file size and visual quality vary with the image. WebP is not guaranteed to be smaller for every source.

Photographs and compatibility

JPEG

JPEG uses lossy compression and is particularly suited to continuous-tone content such as photographs. It remains a practical exchange format for older software, forms, and publishing workflows. It does not support transparency, and repeated lossy re-encoding can accumulate artifacts.

  • Camera photographs and travel photos
  • Uploads to systems that specifically request JPG
  • Photo libraries where broad compatibility matters

Remember: JPEG cannot store transparent pixels. Transparent areas are flattened to a light background in the JPEG export.

Lossless graphics and alpha

PNG

PNG uses lossless compression and supports full alpha transparency. It is usually the safer option for screenshots, diagrams, interface captures, charts, logos, and text-heavy graphics where colored fringes or softened edges are noticeable. Photographic PNG files can be much larger than lossy alternatives.

  • Logos, icons, and transparent assets
  • Screenshots, charts, and diagrams
  • Graphics where exact output pixels matter

Remember: PNG preserves the pixels it receives; it cannot restore detail already lost in a JPEG source. The quality control is therefore disabled for PNG.

Lossy vs lossless: lossy compression makes files smaller by discarding some image information in a way intended to be visually acceptable. Lossless compression preserves the encoded pixel values exactly. Resizing is a separate operation: changing dimensions creates a new raster even when that raster is subsequently stored in a lossless PNG.

Practical use cases

Recommended settings for common image tasks

These are starting points, not universal presets. Image content, target display size, platform requirements, and the quality of the original all matter. Use the suggested filter and format as a first pass, then inspect the exported image before replacing the original.

Product photography for online stores

Filter: Lanczos-3. Format: WebP at 80–86% as a starting point. Scale: match the largest product image slot and its high-density display requirement. This combination is a useful balance for fabric, packaging, reflections, and fine product texture. Keep a higher-quality source for future crops.

Blog posts and editorial images

Filter: Lanczos-3 or Mitchell–Netravali. Format: WebP for a compact web image, JPEG when a CMS specifically requires it. Scale: export close to the maximum rendered width instead of serving a multi-megapixel original into a narrow text column.

Thumbnails, cards, and gallery previews

Filter: Lanczos-3 for photographic thumbnails; Mitchell for a slightly softer look. Format: WebP. Scale: reduce to the real card or preview dimensions. A smaller pixel grid often contributes more to file-size savings than pushing quality to its lowest setting.

Screenshots, diagrams, and text-heavy graphics

Filter: Mitchell–Netravali or Bilinear for a modest size adjustment; compare carefully around small text. Format: PNG when sharp edges and exact exported pixels matter. Avoid aggressive lossy compression, which can create blur, ringing, or colored blocks around letters and thin lines.

Transparent logos and cut-out images

Filter: Lanczos-3 for a detailed photographic cut-out; Mitchell for a gentler edge. Format: PNG for lossless transparency or WebP when a compact transparent web asset is preferred. Do not choose JPEG when the background must remain transparent.

Pixel art, sprites, and retro graphics

Filter: Nearest Neighbor. Format: PNG for lossless color blocks and transparency. Scale: use an integer enlargement where available, such as 200%. Avoid filters that blend adjacent colors, because they turn crisp pixel boundaries into soft edges.

Portraits and people photography

Filter: Mitchell–Netravali for a smoother starting point, or Lanczos-3 when fine hair and fabric detail should remain more pronounced. Format: WebP for websites, JPEG for broad compatibility. Check skin transitions and hair against the background at the final display size.

Images for email and documents

Filter: Lanczos-3 for photos, Mitchell for mixed graphics. Format: JPEG for photos in workflows that request it; PNG for diagrams, logos, and screenshots. Follow the receiving service's own dimension and file-size limits rather than assuming one export fits every platform.

Large originals for mobile layouts

Filter: Lanczos-3. Format: WebP is a practical web output. Scale: prepare a version close to the largest mobile display size needed. A responsive site may still benefit from separate image sizes for mobile and desktop instead of one oversized file for every screen.

Better conversion results

A practical guide to resizing and compressing well

01

Start with the cleanest original

Use the original photo or design export rather than a screenshot of an already compressed image. Every lossy re-encode can discard more information, and a larger source gives the resampling filter more real detail to work with.

02

Decide the actual display dimensions

Check the width and height at which the image will appear. For high-density displays, a 2× asset may be useful: a 600-pixel-wide layout slot could use an image around 1200 pixels wide. Do not upscale just to hit an arbitrary number; a larger file is not automatically a sharper source.

03

Pick the filter for the content

Begin with Lanczos-3 for detailed photos, Mitchell–Netravali for a smoother balance, Catmull–Rom for a crisper cubic result, and Nearest Neighbor for pixel art. If the output looks too soft or shows halos, compare another filter rather than immediately increasing compression quality.

04

Choose a format before chasing file size

Use WebP for compact web imagery, JPEG for compatible photographic uploads, and PNG for lossless graphics or transparency. A wrong format can undermine the result: for instance, JPEG is a poor choice for a transparent logo or fine screenshot text.

05

Adjust quality in small steps

For a first WebP or JPEG export, try around 80–86% and inspect the output. Lower settings can help when the file-size budget is strict; higher settings may preserve subtle texture but produce larger files. The number is not a direct measurement of file-size savings.

06

Inspect difficult areas at normal size

Look at hair, foliage, fabric, gradients, small labels, thin lines, and high-contrast edges. Pixel-peeping at extreme zoom can exaggerate harmless differences, while viewing only a tiny preview can hide artifacts. Check both the intended display size and a closer crop.

07

Compare the exported file size

Use the batch summary to compare original and output totals. If the file gets larger, reduce dimensions first when the image is oversized, or try a different lossy format and quality. Keep an original copy when archival fidelity or future editing matters.

Dimensions and file size

Why pixel dimensions matter so much

A raster image's width multiplied by its height gives its pixel count. Halving both dimensions does not halve the pixel count: it produces one quarter as many pixels. Conversely, doubling both dimensions creates four times as many output pixels. This is why choosing a sensible target size is often the most effective first step in preparing images for the web.

Original4000 × 300012 megapixels
50% scale2000 × 15003 megapixels · 75% fewer pixels
25% scale1000 × 7500.75 megapixels · 93.75% fewer pixels

These examples show pixel counts, not guaranteed file-size reductions. The final number of bytes also depends on image complexity, output format, and encoder quality.

Frequently asked questions

Image resizing and compression FAQ

Clear answers about image quality, resampling filters, WebP, JPEG, PNG, file size, and browser-based processing. Every answer is shown here—nothing is hidden behind a collapsed accordion.

What is the difference between image resizing and image compression?

Resizing changes the pixel dimensions of an image, for example from 3000 × 2000 to 1500 × 1000. Compression changes how the resulting pixel data is stored so the file can use fewer bytes. They solve different problems: resizing reduces the number of pixels, while encoding determines how efficiently those pixels are represented. Compressy lets you control both in one export.

Which resampling filter is best for photographs?

Lanczos-3 is a strong starting point for detailed photographs, especially when reducing dimensions. It can retain crisp texture, although very sharp, high-contrast edges may show faint ringing. Mitchell–Netravali is a useful alternative when you prefer a smoother result; Catmull–Rom can look a little sharper. Compare the actual output at its intended display size.

Which filter should I use for pixel art?

Use Nearest Neighbor. It selects existing source pixels instead of blending neighboring colors, keeping deliberately hard pixel boundaries intact. For the cleanest enlargement, use an integer enlargement where available, such as 200%. Bilinear, bicubic, and Lanczos filters blend neighboring samples and can soften pixel-art edges.

What is the difference between Catmull–Rom and Mitchell–Netravali?

Both are cubic reconstruction filters, but they make different trade-offs. Catmull–Rom is an interpolating cubic that tends to emphasize edge sharpness. Mitchell–Netravali uses a balanced cubic setting (B = 1/3, C = 1/3) intended to balance sharpness, smoothness, and artifacts. Choose Catmull–Rom for a crisper look and Mitchell when a slightly gentler reconstruction is preferable.

What is ringing or a halo around an image edge?

Ringing is a faint light or dark echo that can appear beside a sharp transition, such as dark text on a white background. Filters with stronger edge emphasis can produce this effect around high-contrast boundaries. It is not necessarily a defect in the original file; it can be a side effect of reconstruction. Try Mitchell–Netravali or Bilinear when a Lanczos or Catmull–Rom result looks too pronounced.

Does enlarging an image create real detail?

No. Upscaling creates additional output pixels by estimating values from the pixels already present. It can make an image fit a larger space, but it cannot recover facial detail, text, or texture that was never captured or has already been discarded. For the best result, start with the highest-resolution original available and avoid enlarging it more than necessary.

When should I choose WebP?

Choose WebP for many website images when you want a compact file and need to retain transparency. It is a strong default for product photos, article imagery, thumbnails, and general web graphics. In Compressy, the quality control is used for lossy WebP output; use PNG when you need lossless storage of the exported pixels or especially clean edges in text and diagrams.

When is JPEG still the right choice?

JPEG is a practical choice for photographs when broad compatibility with older software, upload forms, or publishing systems matters. It does not support an alpha transparency channel, so transparent areas are flattened against a light background in the JPEG export. JPEG is lossy, which means repeated editing and re-encoding can gradually introduce visible artifacts.

When should I keep an image as PNG?

Use PNG for screenshots, interface captures, diagrams, charts, logos, line art, and images that need transparency with lossless encoding. PNG preserves the pixel values supplied to its encoder, but converting an already-compressed JPEG to PNG cannot restore detail that the JPEG discarded. PNG files can also be larger than lossy photo formats.

Does 86% quality mean the file will be 14% smaller?

No. The quality number is an encoder setting, not a promised percentage of file-size reduction or a universal visual-quality score. The resulting size depends on the image content, dimensions, format, and browser encoder. A detailed photograph, a flat illustration, and a screenshot can produce very different file sizes at the same setting.

Does 100% quality make JPEG or WebP lossless?

No. A 100% setting asks the lossy encoder for its highest quality setting; it does not turn JPEG or Compressy's quality-controlled WebP export into a lossless archive. For lossless output of the resized pixel data, choose PNG. Keep the original file as well when exact source preservation matters.

Will PNG use the quality setting?

No. PNG is encoded losslessly, so the quality selector is disabled when PNG is selected. Resizing can still change the pixel data because new dimensions require resampling, but PNG will store the resulting raster without adding lossy compression.

Does Compressy preserve transparent backgrounds?

WebP and PNG can preserve transparency. JPEG cannot store transparent pixels; Compressy flattens transparent areas onto a light background when producing JPEG. For logos, cut-outs, product images with transparent backgrounds, and interface assets, choose PNG or WebP instead of JPEG.

What happens when I select 100% resize?

The output keeps the source pixel dimensions, so no resampling is needed. The image is still exported in the selected output format, which means it may be re-encoded and its file size can change. Choose 100% when you want to convert or recompress without changing width and height.

How much smaller will an image become when I resize it to 50%?

At 50% scale, both width and height are halved. The output therefore contains about one quarter as many pixels, a 75% reduction in pixel count. File size will not necessarily fall by exactly 75%, because compression also depends on visual complexity, format, and quality.

What dimensions should I use for a website image?

Start from the largest size at which the image will actually be displayed, and account for high-density screens when needed. For example, an image shown at 600 CSS pixels may need around 1200 source pixels wide for a 2× display. Avoid uploading enormous originals when the layout only displays a small thumbnail, but do not upscale a small source just to reach an arbitrary number.

Can I process multiple images with the same settings?

Yes. Add up to 25 images, choose the resize percentage, resampling filter, output format, and quality once, then process the batch. Images are processed sequentially. Each source image and its output must stay within the 40-megapixel safety limit, and practical capacity also depends on the browser and available device memory.

Which image types can I convert?

The file picker accepts JPEG, PNG, WebP, AVIF, and BMP images, subject to what the browser can decode. The available export formats are WebP, JPEG, and PNG. Compressy is designed for still raster images rather than vector artwork, PDFs, or animated-image editing.

Are my images uploaded or stored?

No image contents are sent to a file-processing server. Decoding, resizing, and encoding happen locally in your browser. The page itself still downloads normal website assets, such as scripts, styles, and fonts. Compressy does not provide cloud storage for your files.

Why can the converted image be larger than the original?

A larger output is possible when you upscale, choose a high quality setting, convert a simple image to a format that represents it less efficiently, or re-encode an already compressed source. PNG can be especially large for photographic content. Compare the original and output totals, and try a smaller scale or a lossy WebP/JPEG setting when file size is the priority.

Does changing the format improve image quality?

Changing the format does not recreate missing detail. A lossy format can reduce file size by discarding information, while PNG stores the exported pixel data losslessly. The best choice depends on the image: photos usually tolerate lossy compression better than screenshots, diagrams, or text-heavy graphics.

Does resizing crop or stretch the image?

No. Compressy applies the selected scale to both dimensions, keeping the image's proportions instead of cropping it or forcing it into a new aspect ratio. The final width and height are rounded to whole pixels. Use a dedicated crop tool when you need to change the framing or aspect ratio.

Are DPI and pixel dimensions the same thing?

No. Pixel dimensions describe how many pixels an image contains; PPI or DPI describes a print-density relationship between those pixels and a physical size. A 2400-pixel-wide image printed at 300 pixels per inch is about 8 inches wide. Compressy is focused on raster pixel dimensions, so check the print provider's required dimensions and density for print work.

Further reading

Technical references

For readers who want to explore image sampling, reconstruction filters, and raster formats in more depth, these independent references explain the underlying concepts and standards.

Filter names describe families of mathematical reconstruction methods. The visible result also depends on the source image, the scale change, edge content, and the encoder used for the final format.