Principle:NVIDIA DALI Video Data Preparation
| Knowledge Sources | |
|---|---|
| Domains | Video_Processing, GPU_Computing, Data_Preparation |
| Last Updated | 2026-02-08 00:00 GMT |
Overview
Video data preparation is the process of transforming raw, full-length video recordings into structured, scene-segmented, and resolution-standardized clips suitable for consumption by deep learning training pipelines.
Description
Video Data Preparation encompasses the end-to-end workflow of converting a single raw source video file into a curated dataset of individually addressable video scenes at multiple target resolutions. In the context of video super-resolution, the training pipeline requires matched pairs of low-resolution and high-resolution video sequences. This necessitates a two-stage preparation process:
Stage 1 -- Scene Splitting: The raw source video (typically a long-form 4K recording) is segmented into discrete scenes based on externally provided timestamp boundaries. Each scene is extracted as an independent MP4 container using stream copy (no re-encoding), preserving the original codec and quality. Scenes are partitioned into training and validation subsets based on their ordinal index, establishing a deterministic train/val split.
Stage 2 -- Multi-Resolution Transcoding: Each split scene is transcoded to one or more target resolutions (540p, 720p, 1080p, 4K) using configurable codec parameters. The transcoding process employs bilinear downscaling for sub-4K resolutions and applies controlled compression via CRF (Constant Rate Factor) values and keyframe intervals. This produces resolution-matched scene directories that the DALI video reader can directly consume.
The entire workflow is orchestrated by a single shell script that sequentially invokes the scene splitter followed by multiple transcoding passes, one per target resolution.
Usage
Use video data preparation when you need to convert raw video footage into a structured, multi-resolution dataset for training video super-resolution models. This is the mandatory first step before any DALI-based video data loading can occur. It is particularly relevant when:
- The source material is a single continuous recording that must be split into addressable scenes
- Multiple resolution variants of the same scenes are required for paired training
- Deterministic train/validation splits must be established at the data level
- The output format must be compatible with DALI's fn.readers.video GPU decoder
Theoretical Basis
Video data preparation for super-resolution is grounded in the principle of controlled degradation. Rather than synthesizing low-resolution frames through artificial downsampling during training, the preparation stage creates physically realistic low-resolution versions using proper video encoding pipelines. The use of bilinear interpolation for spatial downscaling and CRF-based compression introduces artifacts that more closely match real-world low-resolution video, improving the generalization of the trained model.
The scene-splitting approach leverages the temporal coherence within scenes: frames within a single scene share consistent lighting, motion patterns, and visual content, making them suitable as coherent training sequences. Cross-scene boundaries would introduce discontinuities that could confuse temporal models.
The keyframe interval parameter (keyint) is critical for DALI compatibility. DALI's GPU video decoder requires frequent keyframes to enable random-access seeking within video containers. A short keyframe interval (e.g., 4 frames) trades a small increase in file size for the ability to efficiently read arbitrary frame sequences during training.