Libavif Encoding Memory Footprint Explained

This article analyzes the memory footprint characteristics of a standard libavif encoding session. It examines the key factors influencing RAM usage during AVIF image compression—including image resolution, tile configurations, thread counts, and speed presets—offering developers and systems integrators actionable insights to optimize memory allocation in production environments.

Underlying Codec Dependency

The memory footprint of libavif is heavily dependent on the underlying AV1 encoder interface used (typically libaom, rav1e, or SVT-AV1). In most standard implementations, libaom is the default choice. Consequently, libavif acts as a wrapper, and the vast majority of memory allocation occurs within the chosen AV1 codec library during the pixel analysis and compression phases.

Image Resolution and Bit Depth

The baseline memory requirement scales directly with the input image’s dimensions and color depth. Standard 8-bit YUV 420 images require less memory than 10-bit or 12-bit High Dynamic Range (HDR) inputs. The encoder must allocate buffers for the original image, downsampled chroma planes, and reconstructed reference frames used for prediction. Encoding a 4K image demands significantly more memory than a standard 1080p image due to the exponential increase in pixel data stored in the working buffers.

Encoding Speed and Quality Presets

The speed parameter (ranging from 0 to 10 in libaom) is one of the most critical factors governing memory usage. * Low Speed (0–3): Achieves the highest compression efficiency but requires a massive memory footprint. The encoder performs exhaustive search algorithms, keeps multiple reference frames in memory, and utilizes complex partition sizes. * High Speed (6–10): Optimizes for fast processing. It reduces the search space, limits reference frames, and simplifies partition decisions, resulting in a much lighter memory footprint.

Multithreading and Tile Configuration

To speed up encoding, libavif supports multithreading and image tiling. * Thread Count: Increasing the number of threads allocates separate working contexts and buffers for each thread. This leads to a near-linear scaling of memory usage relative to the thread count. * Tiling: Splitting an image into rows and columns of tiles allows parallel encoding. However, each tile column requires its own encoder state, adding an overhead to the total RAM consumption.

General RAM Expectations

For a typical 1080p image encoding session using libaom at default settings (speed 6, medium effort): * Single-threaded: Memory usage generally ranges between 50 MB to 150 MB. * Multi-threaded (4–8 threads): Memory usage can scale to 300 MB to 600 MB. * Extreme compression (speed 0, multi-threaded): RAM consumption can easily exceed 1 GB to 2 GB, especially for high-resolution images.