Why AVIF Achieves Smaller File Sizes Than JPEG

The AV1 Image File Format (AVIF) delivers significantly smaller file sizes than JPEG at equivalent visual quality because it is built upon the modern AV1 video codec rather than thirty-year-old compression technology. By replacing rigid block structures with adaptive partitioning, introducing sophisticated directional intra-prediction, utilizing multiple transform algorithms, and applying advanced in-loop filtering, AVIF encodes image details with exceptional precision while discarding redundant spatial data far more effectively than legacy JPEG.

Decades of Algorithmic Evolution

JPEG was standardized in 1992, designed around the computational limits of that era. In contrast, AVIF is the still-image container for AV1, an open video codec finalized in 2018 by the Alliance for Open Media. AVIF inherits nearly three decades of advancements in video and image compression theory, enabling it to execute complex mathematical models that compress data far more aggressively without degrading perceived visual quality.

Adaptive Block Partitioning

JPEG divides an image into rigid, uniform blocks measuring 8x8 pixels. Every part of the image, whether it is a flat blue sky or intricate foliage, is processed using the exact same grid dimensions.

AVIF utilizes a highly flexible tree-structured partitioning system. Blocks can scale from 4x4 pixels up to 128x128 pixels and can be split into non-square shapes (such as 1:2, 2:1, 1:4, or 4:1 ratios). Smooth, low-detail areas like skies and backgrounds are processed using massive blocks that consume negligible data, while complex edges and textures receive smaller, targeted blocks. This prevents the wasted data overhead common in uniform grids.

Advanced Intra-Frame Prediction

JPEG encodes each block with minimal reference to its surroundings. AVIF heavily relies on advanced intra-prediction techniques, where the encoder predicts the contents of a block based on already-reconstructed adjacent pixels.

AVIF offers dozens of directional prediction angles, along with specialized modes like smooth prediction, DC prediction, and chroma-from-luma (CfL) prediction—which predicts color channels using structural luma details. Instead of encoding the entire block, AVIF only needs to encode the tiny "residual"—the difference between the prediction and the actual image. Because the predictions are remarkably accurate, the residual data is minimal, saving substantial file space.

Versatile Transform Coding

After spatial prediction, residual data is converted into frequency components via mathematical transforms. JPEG relies solely on the standard Discrete Cosine Transform (DCT).

AVIF dynamically selects between several transform families, including DCT, the Asymmetric Discrete Sine Transform (ADST), and identity transforms. Because certain mathematical functions represent sharp edges or directional gradients better than others, AVIF selects the optimal transform for each specific block, packing image energy into fewer coefficients and reducing the bits required to represent the scene.

In-Loop Deblocking and Restoration Filters

High compression in JPEG causes harsh block boundaries (pixelation) and ringing artifacts around high-contrast edges. To avoid this, JPEG files must maintain higher bitrates.

AVIF integrates multiple in-loop filters directly into the decoding pipeline:

Because these filters intelligently reconstruct clean visuals from aggressively compressed data, AVIF can tolerate much lower bitrates while looking crisp and natural to the human eye.

Native 10-Bit and 12-Bit Color Depth

Standard JPEG is limited to 8-bit color depth, which frequently leads to color banding in subtle gradients. To mask this banding, JPEG encoders must preserve fine noise, which demands higher bitrates. AVIF natively supports 10-bit and 12-bit color spaces, enabling smoother gradients with mathematically simpler data representations, eliminating both the visual banding and the file size penalties associated with mitigating it.