Lossless Compression and Lossy Compression: A Practical Guide

How Lossless Compression Works

Lossless compression reduces a file's size without discarding any original data. This means you can restore the file to its exact original state with zero quality loss. The method works by finding more compact ways to represent the same information.

It identifies and encodes redundant data. For example, an image with a long row of identical blue pixels can be stored as "500x blue" instead of "blue, blue, blue..." repeated 500 times. The data is the same, just stored more efficiently. This makes lossless compression the required method when data integrity is critical.

Common Lossless Algorithms

Several algorithms achieve lossless compression by identifying patterns in the data:

• Run-Length Encoding (RLE) — A straightforward method that finds consecutive runs of identical data. It stores the data value and a count. This is effective for images with large areas of solid color, such as logos or diagrams.
• Huffman Coding — Assigns shorter binary codes to more frequent data patterns and longer codes to less frequent ones. This reduces the average number of bits needed to represent the data.
• Lempel-Ziv (LZ77/DEFLATE) — Builds a dictionary of data patterns within the file and replaces those patterns with shorter references. This technique is used in formats like PNG and ZIP archives.

Lossless PNG Compression with oxipng

When you enable lossless mode in our PNG compressor, Compress.FAST uses oxipng, a modern PNG optimizer written in Rust. It re-encodes the image data using better compression settings without changing a single pixel.

Typical lossless savings with oxipng are 5% to 15%. This is less than lossy compression achieves, but the trade-off is that every pixel remains identical to the original. For logos, icons, screenshots, and any image where visual accuracy is essential, this is the correct choice.

The core idea behind lossless compression is simple: find smarter ways to rewrite data more efficiently without throwing anything away. You can always get the original file back, bit for perfect bit.

Practical Use Cases for Lossless Compression

Lossless compression is critical where perfect accuracy is required:

• Logos and Icons — PNG is ideal for graphics with sharp lines and flat colors. Lossless compression ensures they remain crisp with no compression artifacts around edges or text.
• Medical and Scientific Imaging — In radiology, every pixel in an MRI or CT scan contains critical diagnostic data. Lossless compression is mandatory to ensure no information is altered.
• Archival and Master Files — Photographers archive original RAW files or save master edits as lossless TIFF or PNG. This creates a pristine source file for future use. From this master, they can generate smaller, lossy copies for web or client previews.
• Screenshots and UI Elements — Screenshots of text, code, or user interfaces need sharp edges. Lossy compression can blur text and create artifacts around UI elements.

How Lossy Compression Works

Lossy compression makes a trade-off to dramatically reduce file size. It permanently removes some of the original data. This process is not random; it discards information that human eyes and ears are least likely to perceive. This is sometimes called perceptual optimization.

The goal is to remove subtle details, such as minor color variations in smooth gradients or fine texture in complex areas. In exchange for a small, often imperceptible, drop in quality, you get a file size reduction of 50% to 80% or more.

Key Lossy Compression Techniques

Two common techniques in lossy image compression are the Discrete Cosine Transform (DCT) and chroma subsampling. They work together to reduce file size with minimal visible impact.

• Discrete Cosine Transform (DCT) — A mathematical function that converts image data from a grid of pixels into a map of frequencies. It separates important low-frequency data (smooth gradients, large color areas) from less noticeable high-frequency details (fine textures, noise). The algorithm then heavily compresses or discards the high-frequency data, which provides most of the file size savings.
• Chroma Subsampling — This technique leverages a quirk of human vision. Our eyes are more sensitive to changes in brightness (luma) than to changes in color (chroma). Chroma subsampling reduces the amount of color information in an image while preserving the brightness data, resulting in a smaller file with little visible difference.

These techniques make formats like JPG, WebP, and AVIF efficient for web and streaming media.

Lossy PNG Compression with libimagequant

By default, our PNG compressor uses lossy compression powered by libimagequant. This is the same core technology used by TinyPNG, the service that popularized web-based lossy PNG compression.

This approach typically achieves 50% to 80% file size reduction. It works exceptionally well for web graphics, UI elements, and even photographs where absolute pixel-perfect accuracy is not required. Compress.FAST also auto-detects whether your image is a graphic (logo, icon) or a photo, and adjusts the dithering settings automatically for the best results.

JPG Compression with MozJPEG

For JPG files, Compress.FAST uses MozJPEG, Mozilla's optimized JPG encoder. It produces files that are up to 30% smaller than standard JPG encoders at the same visual quality.

MozJPEG achieves this through several techniques: trellis quantization (finding the optimal balance of quality and size for each image block), progressive scan optimization (images that render progressively as they download), and tuned quantization tables that preserve important visual details. Learn more in our JPG compressor documentation.

The philosophy behind lossy compression is to work with human perception, not against it. By strategically cutting out data we are unlikely to miss, it delivers the small, nimble files that make modern digital life possible.

The Downside: Generational Loss

The main drawback of lossy compression is its irreversibility. Once data is removed, it cannot be recovered. This leads to a problem known as generational loss.

Generational loss occurs when a lossy file is repeatedly edited and re-saved. Each save operation applies the compression algorithm again, discarding more data. After several edits, the accumulated losses become noticeable as blocky artifacts, color banding, or fuzzy details.

How Compress.FAST Implements Both Methods

Compress.FAST gives you control over the lossless vs. lossy trade-off. Our PNG compressor provides a toggle so you can choose the right approach for each image. Here is exactly what happens under the hood.

PNG Compressor: Two Modes

When you use lossy mode, Compress.FAST also auto-detects your image type. If the image has transparency or low color variation (like a logo), it disables dithering to keep edges crisp. If the image appears to be a photograph with smooth gradients, it enables full dithering to avoid color banding. This happens automatically without any configuration.

JPG Compressor: Optimized Lossy

Our JPG compressor uses MozJPEG exclusively (JPG is inherently a lossy format). It applies:

• Trellis Quantization — Analyzes each 8x8 pixel block to find the optimal quality/size balance, resulting in better visual quality at smaller file sizes than standard encoders.
• Progressive Encoding — Creates images that render progressively (blurry to sharp) as they download, improving perceived performance.
• Optimized Huffman Tables — Generates file-specific compression tables instead of using generic ones, squeezing out additional savings.

Security and Privacy

Regardless of which compression method you choose, all processing happens on encrypted EU-based servers. Files are protected with TLS 1.3 during transfer and AES-256 at rest. We permanently delete all files within one hour of processing. For details, see our encryption and privacy documentation.

When to Choose Lossy or Lossless Compression

Deciding between lossy and lossless compression is about matching the tool to the task. The choice depends on whether you need perfect file integrity or practical performance.

The decision rests on one question: can you afford to lose any data? If the answer is no, lossless is your only option. If a small, often imperceptible, loss of data is acceptable for a much smaller file, then lossy is the correct choice.

For Photographers and Designers

Creative professionals must balance image quality and file size. Your choice between lossless and lossy compression affects everything from archiving to final delivery.

The standard rule is to always maintain a lossless master file. This acts as your digital negative, preserving 100% of the image data. You can then create other versions from this master.

• Archiving Master Files — Use lossless formats like TIFF, PNG, or your camera's original RAW files. This is non-negotiable for preserving a perfect source for future edits or prints.
• Client Previews — Sending large TIFF files for review is inefficient. A high-quality JPG or WebP is much smaller, making it faster for clients to download. A 70-80% reduction in file size is common and practical.
• Web Portfolios — For online portfolios, performance is critical. Modern lossy formats like WebP or AVIF provide good visual quality at a small fraction of a lossless PNG's file size.

The professional workflow is clear: edit and archive in lossless formats to prevent generational loss. Export to optimized lossy formats only as the final step for delivery or web use.

For Web Developers and Site Administrators

For a website, page speed is a primary concern. Images are often the largest assets on a page. Your compression choice directly impacts load time, user experience, and search rankings.

The goal is to deliver the best visual quality at the smallest possible file size. This almost always means using modern, efficient lossy formats for photographs and complex images.

• Hero Images and Photos — Use a modern lossy format like WebP or AVIF. AVIF can be up to 50% smaller than an equivalent JPG, a significant performance gain.
• Logos, Icons, and Line Art — For graphics with sharp lines or transparency, lossless PNG is often still the correct choice. Lossy compression can create fuzzy artifacts around sharp edges, but PNG keeps them crisp. Alternatively, use our PNG compressor in lossy mode with auto-detection, which preserves sharp edges for graphics.
• User-Generated Content — If your site allows users to upload images, use an automated lossy compression process. A smart tool can apply the right amount of compression to reduce file sizes without noticeable quality degradation.

For details on different image formats and their trade-offs, see our image formats guide.

For Business and Office Users

In an office environment, file compression makes communication and storage more efficient. Smaller files simplify collaboration and avoid email attachment limits.

• Email Attachments — Run images through a lossy compressor before attaching them to emails. A 60% reduction in an image's file size can prevent delivery failures due to attachment size limits. See our guide on compressing files for email.
• Internal Documents — Use smart compression tools for PDFs or presentations. These tools often apply lossless compression to text and lossy compression to embedded images.
• Digital Archives — For long-term storage of official records where data integrity is critical, use lossless compression. This ensures retrieved files are identical to the originals.

Technical Format Comparison

Different file formats are built for different purposes. This table compares popular image formats by their compression type, typical size reduction, and best use cases.

There is no single "best" format. The right choice depends on your goal, whether it is maximum quality or maximum performance. WebP has become the standard for most web use cases, offering the flexibility of both compression types with excellent browser support.

The Rise of Hybrid Approaches

Modern formats and tools are blurring the line between lossy and lossless. A "near-lossless" or hybrid approach uses advanced lossy algorithms at very high-quality settings. This achieves significant file size reductions with no visible loss of detail.

This balanced method is practical for many professionals. It delivers the performance benefits of smaller files without the artifacts of aggressive lossy settings. A pragmatic solution for photographers delivering client galleries, teams managing web assets, or anyone who wants the best of both worlds.

A hybrid compression strategy aims for the best of both worlds: the substantial file size savings of lossy methods while maintaining visual quality so high that it is effectively indistinguishable from a lossless original.

Your Compression Questions Answered

Here are direct answers to common questions about lossless and lossy compression.