Harnessing AI for Video Encoding: Workflow and Quality Gains

AI in video encoding is past the hype stage. Production encoding pipelines are using machine learning for per-title bitrate optimisation, scene-level complexity analysis, and quality prediction at scale. The results are measurable: 20-40% bitrate savings on top of what a well-tuned static encoding ladder already achieves, without visible quality degradation.

This guide covers where AI fits in a practical OTT encoding workflow, what gains are realistic, and where the limitations are.

Where AI fits in the encoding pipeline

A typical OTT encoding pipeline looks like:

1. Ingest — source file arrives (mezzanine quality)
2. Analysis — content complexity and scene structure are assessed
3. Encoding — the video is encoded into multiple ABR ladder rungs
4. Quality check — encoded output is verified against quality thresholds
5. Packaging — encoded segments are packaged into HLS and DASH formats
6. Delivery — packaged content is pushed to CDN origin

AI augments steps 2, 3, and 4. It does not replace the encoder (you still use x264, x265, SVT-AV1, or hardware encoders). Instead, it makes smarter decisions about how to configure the encoder for each piece of content.

Per-title encoding with ML

The problem with fixed ladders

A fixed ABR ladder uses the same bitrate for every piece of content at each resolution. A 1080p rung at 5 Mbps is correct for some content and wasteful or insufficient for others. A static talking-head interview needs far fewer bits than a fast-cut action sequence at the same resolution.

Per-title encoding analyses each title’s complexity and generates a custom ABR ladder. Simple content gets lower bitrates. Complex content gets higher bitrates. The result is better quality-per-bit across the catalog.

How ML improves per-title

Traditional per-title encoding runs a pre-analysis pass: encode the content at multiple bitrate/resolution combinations, measure quality (VMAF, SSIM, or PSNR), and select the Pareto-optimal points on the quality-bitrate curve.

This works but is computationally expensive. Encoding a feature film at 30+ bitrate/resolution combinations to find the optimal ladder can cost 10-50x the compute of a single encode.

ML-based per-title encoding replaces the brute-force search with a predictive model:

1. Feature extraction: analyse a sample of frames or scenes for complexity metrics (spatial information, temporal information, texture density, motion vectors)
2. Model prediction: a trained model predicts the quality-bitrate curve for the content without encoding every combination
3. Ladder generation: the predicted curve determines the optimal bitrate for each resolution in the ladder

The ML model is trained on thousands of previously encoded titles with measured quality. It learns the relationship between content features and encoding efficiency, and can predict the optimal ladder in seconds instead of hours.

Realistic gains

Per-title encoding with ML typically achieves:

• 20-30% bitrate reduction over a well-tuned fixed ladder, at equivalent visual quality
• 15-20% bitrate reduction over traditional (brute-force) per-title encoding, due to better prediction accuracy at the margins

The gains vary by content type. Highly variable content (a catalog with both animation and sports) sees larger gains because the fixed ladder is further from optimal for each title.

Scene-level encoding optimisation

Per-title encoding sets the ladder for an entire title. Scene-level (or shot-level) encoding goes further: it adjusts encoding parameters within a title based on scene complexity.

How it works

1. Scene detection: ML models or heuristic detectors identify scene boundaries (cuts, fades, dissolves)
2. Per-scene complexity estimation: each scene is analysed for spatial and temporal complexity
3. Bitrate allocation: the encoder allocates more bits to complex scenes and fewer bits to simple scenes, while maintaining the target average bitrate for the segment or title

This is conceptually similar to the encoder’s built-in rate control (VBR encoding already allocates more bits to complex frames), but scene-level AI goes further by operating at a higher level: it can redistribute bitrate across scenes in a way that the frame-level rate controller cannot.

Integration with encoders

Some encoders (including x265 and SVT-AV1) support external bitrate hints or zone-based encoding, where you can specify different quantizer or bitrate targets for different time ranges. AI scene analysis provides these hints.

For live encoding, scene-level AI operates in real time: it analyses incoming frames and adjusts encoder parameters on the fly. This requires low-latency inference, typically running the complexity model on GPU alongside the encoder.

AI-based quality assessment

Moving beyond PSNR

Traditional quality metrics (PSNR, SSIM) measure pixel-level differences between the source and encoded video. They correlate with perceived quality but have known weaknesses: PSNR over-penalises noise reduction (which viewers often prefer) and under-penalises some types of artifacts (banding, mosquito noise around text).

VMAF (Video Multimethod Assessment Fusion) improved on this by combining multiple quality features with a machine learning model trained on human quality ratings. VMAF is now the industry standard for OTT quality assessment.

AI-driven quality prediction

Instead of computing VMAF on every encoded frame (which requires a reference source and adds compute time), ML models can predict the VMAF score from encoder statistics:

• Quantiser values per frame
• Bits per pixel
• Motion estimation residuals
• Scene complexity metrics

These predictions are less accurate than full VMAF computation but are 100x faster and do not require access to the source video. This makes them suitable for:

• Real-time quality monitoring during live encoding
• Quality-aware ABR: the player selects the stream that maximises predicted quality, not just bitrate
• Automated quality gating: reject encodes that fall below a predicted quality threshold

Perceptual quality optimisation

Some encoding workflows now use AI quality models in the encoding loop itself: the encoder adjusts its decisions (quantiser, mode decisions, partitioning) based on a perceptual quality model’s feedback. This produces encodes that are optimised for how humans perceive quality, not just for pixel-level fidelity.

The tradeoff is encoding speed: running a quality model in the encoding loop adds significant compute. This approach is currently practical only for VOD encoding, not live.

Practical implementation

Build vs buy

Several vendors offer AI-powered encoding as a service:

• Cloud encoding services (AWS MediaConvert, Bitmovin, Mux, Harmonic) include per-title and content-aware encoding features
• Standalone tools (Netflix’s open-source per-title tools, Meta’s SVT-AV1 configurations) can be integrated into custom pipelines

Building your own ML-based encoding optimisation requires:

• Training data: thousands of source/encode pairs with quality measurements
• ML infrastructure: model training, serving, and monitoring
• Integration with your encoder and packaging pipeline

For most OTT services, using a vendor’s content-aware encoding feature is more practical than building from scratch. The quality gains from vendor solutions are well-validated.

Measuring the impact

To verify that AI encoding is actually improving your delivery:

1. A/B test with real viewers. Encode a set of titles with and without AI optimisation. Serve both to real viewers and compare rebuffering rates, quality switches, and engagement metrics.
2. Compare bitrate at equivalent quality. For each title, compare the bitrate required to achieve VMAF 93 (a common HD quality threshold) with and without AI optimisation.
3. Monitor CDN costs. If AI encoding reduces average bitrate by 25%, your CDN egress costs should decrease proportionally.

Limitations and caveats

AI encoding does not fix bad source material. If your mezzanine quality is poor (heavy compression artifacts from a previous encode, low resolution upscaled to HD), AI encoding cannot recover quality that was already lost.

Garbage in, garbage out for ML models. If the training data for your per-title model does not represent your actual content catalog (e.g., trained on drama and used for sports), the predictions will be suboptimal.

Encoding speed tradeoffs. More sophisticated AI analysis adds time to the encoding pipeline. For VOD, this is usually acceptable. For live encoding, latency constraints limit the complexity of AI that can run in real time.

Diminishing returns. The gap between a well-tuned static ladder and an AI-optimised ladder is smaller than the gap between a poorly tuned ladder and a well-tuned one. Get the basics right first — correct codec selection, proper segment duration, appropriate resolution and bitrate ranges — before investing in AI optimisation.

AI video encoding is a genuine improvement for OTT delivery efficiency, but it is an optimisation on top of sound video delivery fundamentals, not a replacement for them.