BAVE analyzes every scene individually and predicts optimal x264 parameters using a forward-surrogate ML model. Professional delivery presets: Theatrical, Streaming, Festival. No GPU required — all inference runs on CPU.
Every feature serves one goal: optimal quality per shot with predictable, repeatable results.
The forward-surrogate ML model evaluates ~500 parameter candidates per shot and selects the optimal x264 settings (CRF, preset, tune, AQ, psy-rd, bframes, subme, deblock, me_range, rc_lookahead, etc.) for each scene individually.
Fused AdaptiveDetector + ContentDetector cascade with adaptive thresholds. Catches hard cuts and gradual transitions. Precision guard drops boundaries where frame-MAD ≤ 2× baseline. No magic numbers — thresholds derived from content's own motion energy.
Theatrical: CRF unconstrained, VMAF 96 target, veryslow, grain rewarded.
Streaming: CRF+VBV, ~2500kbps target, VMAF 93, slow preset.
Festival: Two-pass bitrate, exact 1GB file size, slow preset.
INT8-quantized Vision Transformer computes whole-video visual diversity (cosine similarity of frame embeddings). Sampled 30–300 frames adaptively. Cached once per video, reused across all shots. Runs fully on CPU.
Auto-detects stereo/surround (5.1/7.1), downmix/upmix with libfdk_aac VBR5, 48kHz SOXR resampling. Single continuous audio track — no per-shot cuts, no clicks. Loudness measured via EBU R128.
HDR→SDR tonemap, deinterlace, scale to FHD 8-bit BT.709 in one FFmpeg filter chain. Lossless intermediate (qp=0, ultrafast). Blackbar detection via cropdetect at multiple sample points. All reuse cached media_info — no redundant ffprobe.
Shot-level state machine: pending → processing → encoded → concatenated → finalized. SQLite checkpoint DB survives power loss/OOM. Resume from any shot. Automatic 72-hour TTL cleanup.
Project folder: FilmName Year Director
Video: FilmName Year Director FHD STEREO THEATRICAL BAVE.mp4
Stills: FilmName Year Director/Stills/FilmName Year Director Stills 00h00m18s.png
Zero underscores. Metadata & copyright embedded in output and stills.
Premium dark theme with glassmorphism. Horizontal filmstrip with shot thumbnails, palette strips, per-shot parameter preview. Real-time resource gauges (CPU/RAM/disk I/O). Drag-drop import. Batch queue with multiple preset selection.
BAVE's encoding pipeline reuses every heavy computation exactly once. No redundant ffprobe, no re-analysis, no silent fallbacks.
Three CatBoost GBDT models exported to ONNX for CPU inference. Trained on 178,208 rows from 896 unique clips.
R² = 0.633 | MAE = 3591 kbps
Kendall τ = 0.635 | Spearman ρ = 0.823
R² = 0.530 | MAE = 1.79
Kendall τ = 0.592 | Spearman ρ = 0.775
R² = 0.757 | MAE = 7.00s
Kendall τ = 0.709 | Spearman ρ = 0.888
Each preset uses a different optimization objective, not just different settings.
Unconstrained quality maximization
Per-shot quality adaptation with VBV cap
Exact file size via two-pass bitrate
BAVE is CPU-only. All AI inference and encoding run locally on your CPU.
| OS | Windows 10/11 64-bit |
| RAM (min) | 8 GB |
| RAM (rec) | 16+ GB |
| CPU (min) | 4-core (i5-6400 / Ryzen 3 1200) |
| CPU (rec) | 8+ core (i7-8700K / Ryzen 7 2700X) |
| Disk | ~1.5 GB (includes FFmpeg + models) |
| GPU | Not Required |
Level 1: AI surrogate runs normally (all features valid, pred_VMAF ≥ 80)
│
├─► NaN/Inf features → _get_default_conservative_params (CRF 18/20, slow)
│
├─► pred_VMAF < 80 → _get_default_conservative_params (unreliable prediction)
│
├─► FFmpeg pipe decode fails → OpenCV fallback (semantic similarity)
│
├─► PySceneDetect fails → FFmpeg select=gt(scene,threshold) fallback
│
└─► Encode timeout → retry with faster preset (same CRF/quality)
Every fallback either logs a warning or raises. No silent degradation.
Per-seat perpetual license with 1 year updates. Offline activation — no subscription, no phone home.
1 workstation license
3 workstations + transfer
Unlimited + enterprise features
Free 7-day trial — full features, no credit card. Offline activation via machine config exchange.
Download TrialWe provide 100% free Studio licenses for student directors, indie filmmakers, and creators facing regional or financial hardship. Reviewed directly by Benyamin Khodabandeh.
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