Website speed determines whether visitors stay or leave. Images account for the largest portion of data transfer on most websites, making image format choice critical for performance. AVIF (AV1 Image File Format) represents the newest advancement in image compression technology, delivering dramatically smaller file sizes than JPEG or PNG while maintaining equal or better visual quality. Understanding how AVIF works helps web developers, photographers, and content creators make informed decisions about adopting this format.
AVIF leverages compression technology originally developed for video streaming to achieve superior efficiency for still images. The same algorithms that let you stream 4K video over home internet connections now compress photos to a fraction of their original size without visible quality loss. This technical innovation makes faster websites, reduced bandwidth costs, and improved user experiences possible across all devices.
How AVIF Compression Works
AVIF builds on the AV1 video codec, which uses sophisticated compression techniques designed by an alliance of technology companies including Google, Mozilla, Apple, and Netflix. AV1 compression analyzes images at multiple granularity levels, from individual pixels to large regions, identifying patterns and redundancies that can be stored more efficiently than raw pixel data.
The compression algorithm divides images into blocks of various sizes. Unlike JPEG's fixed 8×8 pixel blocks, AVIF uses adaptive block sizes ranging from 4×4 up to 128×128 pixels. Large uniform areas like skies or walls get compressed in big blocks requiring minimal data. Detailed regions with lots of texture and fine detail get divided into smaller blocks that preserve complexity. This adaptability alone improves compression efficiency significantly.
Predictive coding reduces redundancy by predicting pixel values based on surrounding pixels, then storing only the difference between predicted and actual values. If the prediction is accurate, the difference value is small and requires fewer bits to store. The decoder reverses this process, using the same prediction method plus stored difference values to reconstruct the original image.
Transform coding converts spatial pixel data into frequency domain representations using mathematical transforms. This process separates image information into low-frequency components (gradual color and brightness changes) and high-frequency components (sharp edges and fine details). Human vision is less sensitive to subtle changes in high-frequency information, allowing more aggressive compression of these components without noticeable quality impact.
Quantization selectively discards information based on perceptual importance. The algorithm identifies which data humans won't notice is missing, then eliminates it permanently. This lossy compression step provides the largest file size reductions. AVIF's advanced perceptual models understand human vision better than older formats, allowing more aggressive data elimination while preserving perceived quality.
AVIF vs JPEG Compression
JPEG compression, developed in 1992, divides images into 8×8 pixel blocks and applies discrete cosine transforms to each block independently. This approach works well but creates visible artifacts at higher compression levels—blocky patterns where block boundaries become obvious, and color banding where smooth gradients break into distinct steps.
AVIF's variable block sizes and more sophisticated transform methods avoid many JPEG artifacts. Smooth gradients compress cleanly without banding. Fine details remain sharp without generating the "mosquito noise" JPEG creates around high-contrast edges. At equivalent file sizes, AVIF images consistently look better than JPEG equivalents.
Real-world comparisons show AVIF achieving 30-50 percent smaller file sizes than JPEG at matching visual quality. A 100KB JPEG might compress to 50-70KB in AVIF with identical appearance. For websites serving millions of images, this difference translates to dramatically reduced bandwidth costs and faster page loads.
High Dynamic Range (HDR) support gives AVIF a significant advantage. JPEG is limited to 8 bits per color channel, restricting brightness and color range. AVIF handles up to 12 bits per channel, preserving subtle gradations in highlights and shadows that JPEG crushes to uniform black or white. This matters for professional photography and modern HDR displays.
AVIF vs WebP and PNG
WebP, developed by Google in 2010, improved upon JPEG by offering both lossy and lossless compression with better efficiency. For many years, WebP represented the best available image format for web use. AVIF surpasses WebP in compression efficiency by an additional 20-30 percent on average.
PNG excels at lossless compression for graphics, screenshots, and images requiring perfect pixel accuracy. PNG files contain exact original data without any quality loss. However, PNG files are much larger than compressed formats. AVIF offers both lossy compression (even more efficient than JPEG/WebP) and lossless compression (more efficient than PNG), making it versatile for different use cases.
Transparency support (alpha channels) works in AVIF, WebP, and PNG but not JPEG. When you need images with transparent backgrounds, all three transparency-supporting formats work, but AVIF compresses the transparency data more efficiently than alternatives.
Animation support exists in AVIF and WebP, making them potential replacements for GIFs. AVIF animations compress more efficiently than animated WebP, which already compresses far better than GIF. For simple animations or short video clips, AVIF animations can be dramatically smaller than equivalent GIFs while supporting full color and alpha transparency.
Browser Support and Compatibility
AVIF browser support launched in Chrome 85 (August 2020), Firefox 93 (October 2021), and Safari 16 (September 2022). As of 2025, over 90 percent of users worldwide browse with AVIF-capable browsers. This widespread support makes AVIF viable for production use with appropriate fallback strategies.
Progressive enhancement handles older browsers gracefully. Web developers serve AVIF to supporting browsers while automatically falling back to WebP or JPEG for older browsers. The picture element in HTML5 handles this automatically: list image sources from most to least efficient, and browsers download the first format they support.
Content delivery networks (CDNs) increasingly offer automatic AVIF conversion. Upload JPEG or PNG images, and the CDN automatically generates AVIF versions, serves them to compatible browsers, and falls back to original formats for older browsers. This automation makes AVIF adoption effortless for many websites.
Mobile support matters enormously since mobile users typically have slower connections and data caps. Both iOS and Android support AVIF natively in recent versions. The bandwidth savings from AVIF particularly benefit mobile users on cellular connections where data costs money and speed varies.
Practical Applications and Use Cases
E-commerce product images benefit enormously from AVIF. Online stores display dozens or hundreds of product photos per page. Reducing each image by 40 percent creates dramatically faster page loads. Faster pages convert better—Amazon famously found that every 100ms of additional load time cost them 1 percent of sales. Converting product images to AVIF directly improves revenue.
Photography portfolios showcase high-quality images where detail matters. AVIF lets photographers display larger, higher-resolution images without proportionally larger file sizes. Visitors see more detail and better quality while pages still load quickly. The HDR support in AVIF particularly benefits professional photography where capturing full tonal range matters.
News and media sites publish hundreds of new images daily. Server costs and CDN bandwidth represent significant operating expenses for high-traffic publishers. AVIF reduces these costs directly—40 percent smaller images mean 40 percent less bandwidth consumed. At scale, this saves substantial money.
Social media platforms benefit from AVIF for user-uploaded content. With billions of images uploaded and viewed, even small per-image savings create enormous aggregate benefits. Faster image loads improve user experience and reduce infrastructure costs simultaneously.
Try our free AVIF converter to compress images using modern algorithms. Upload JPEG, PNG, or WebP files to generate optimized AVIF versions that maintain visual quality while dramatically reducing file size. Perfect for web developers and content creators.
Encoding Settings and Quality Optimization
Quality parameter determines compression aggressiveness. AVIF encoders typically use a scale from 0-100 or 0-63 depending on the encoder. Higher numbers mean better quality but larger files. Testing shows quality settings around 75-85 (on a 0-100 scale) often provide excellent visual quality while achieving maximum compression efficiency.
Speed vs compression efficiency tradeoff exists in AVIF encoding. Faster encoding produces slightly larger files. Slower encoding (more CPU time) finds additional compression opportunities for smaller final files. For production use where files are encoded once but delivered millions of times, slower encoding that achieves optimal compression makes sense. For user-generated content requiring real-time encoding, faster speeds may be necessary despite slightly larger files.
Chroma subsampling reduces color information to save space. Human vision is more sensitive to brightness (luma) than color (chroma) information. AVIF can store full-resolution brightness data while storing color at lower resolution without perceptible quality loss for most images. The 4:2:0 subsampling (half-resolution color) works well for photographs while 4:4:4 (full-resolution color) better preserves graphics and text.
Lossless mode preserves perfect quality when file size is less critical. Medical imaging, archival photography, and graphics requiring pixel-perfect accuracy benefit from lossless AVIF. While not as small as lossy compression, lossless AVIF still compresses significantly better than PNG.
Limitations and Tradeoffs
Encoding speed remains slower than JPEG or WebP. The sophisticated compression algorithms that make AVIF files small require more computational work. On modern hardware this rarely matters for static images compressed once, but real-time image processing or encoding thousands of images quickly can hit performance constraints. Encoder optimization continues improving speed.
Decoding complexity affects older or lower-powered devices. While modern processors decode AVIF efficiently, older smartphones or budget devices may decode AVIF slightly slower than JPEG. For most use cases this difference is negligible, but applications targeting very old devices might prioritize faster-decoding formats.
File size savings vary by image content. Photographs with natural scenes, gradients, and complex textures see the largest benefits from AVIF—often 40-50 percent smaller than JPEG. Simple graphics, screenshots with large uniform areas, or images already well-suited to PNG compression see smaller relative improvements.
Tooling and software support continues maturing. While all major image editing applications now support AVIF, some workflow automation tools, content management systems, and older software lack native support. This ecosystem gap is closing rapidly as AVIF adoption increases.
Future of Image Formats
AVIF adoption will continue accelerating as browser support reaches near-universality and developer awareness increases. The combination of superior compression, feature completeness (transparency, animation, HDR), and broad backing from major tech companies positions AVIF as the long-term successor to JPEG for lossy compression and PNG for lossless transparency.
JPEG XL represents an alternative next-generation format with some advantages over AVIF, particularly for lossless compression and handling existing JPEG files. The industry may ultimately support both formats with specific use cases where each excels, or one may become dominant through network effects and ecosystem support.
Progressive AVIF improvements will deliver better compression and faster encoding as the format matures. The AV1 codec continues advancing with newer versions offering incremental improvements. AVIF benefits from these advances as they're incorporated into encoding implementations.
Automatic format negotiation may eventually hide format complexity from developers entirely. CDNs and hosting platforms will automatically encode, store, and deliver images in whichever format each user's browser and network conditions make optimal, with developers simply uploading high-quality source images and letting infrastructure handle optimization.