How to Resize an Image Without Losing Quality

What happens when you resize an image

When you resize an image, you are changing the number of pixels in the image. If you make the image smaller (downscaling), the tool must discard pixels and combine information from neighboring pixels. If you make the image larger (upscaling), the tool must create new pixels by interpolating between existing ones.

The algorithm that performs this pixel manipulation is called a resampling filter. Different filters produce different results in terms of sharpness, smoothness, and artifacts. Choosing the right filter is the key to resizing without losing quality.

Resampling algorithms explained

The most common resampling algorithms are nearest-neighbor, bilinear, bicubic, and Lanczos. Nearest-neighbor is the fastest but produces blocky, pixelated results. Bilinear is smoother but can blur edges. Bicubic offers a good balance of sharpness and smoothness and is the default in many image editors. Lanczos is the highest quality, producing sharp results with minimal artifacts, but it is computationally more expensive.

For downscaling, Lanczos or bicubic produces the best results. For upscaling, bicubic or Lanczos with sharpening can produce acceptable results, but upscaling always involves some quality loss because the tool must invent pixels that do not exist in the original.

Downscaling vs upscaling

Downscaling (making an image smaller) generally produces good results because the tool has more information than it needs and can discard excess pixels intelligently. A high-quality downscale with Lanczos resampling can produce a sharp, clean result that looks nearly identical to the original at the new size.

Upscaling (making an image larger) is inherently lossy because the tool must create new pixels that do not exist in the original. Modern upscaling algorithms can produce acceptable results for moderate enlargements (up to about 150 percent), but extreme upscaling always produces visible blurring or artifacts. For best results, start with the highest resolution original available.

Choosing the right format

The output format affects quality and file size. For photographs, JPG or WebP with a high quality setting preserves detail while keeping the file size reasonable. For images with sharp edges, text, or transparency, PNG preserves detail without compression artifacts. WebP offers the best of both worlds with superior compression for both photographic and graphic content.

When resizing for the web, consider using WebP, which provides better compression than JPG and PNG while maintaining quality. Most modern browsers support WebP, making it the best choice for web images.

Step-by-step: resizing an image

1. Open a browser-based image editor like Pixbench at pixbench.explorme.com.

2. Upload or select the image you want to resize.

3. Choose the resize operation and enter the target dimensions or percentage.

4. If the tool offers resampling options, choose Lanczos or bicubic for the best quality.

5. If the tool offers a quality setting for the output format, choose a high setting (80-95 for JPG or WebP).

6. Preview the result to check the quality before saving.

7. Download the resized image.

Batch resizing multiple images

If you need to resize multiple images to the same dimensions, use a tool that supports batch processing. This lets you apply the same resize operation to all images at once and download them together. Batch resizing is useful for preparing product images for an e-commerce site, creating thumbnails for a gallery, or standardizing image dimensions for a blog.

When batch resizing, make sure all source images are in the same orientation and have similar aspect ratios to avoid distortion. If the aspect ratios differ, look for a tool that can resize with cropping or padding to maintain consistent dimensions.