🎬 Morphogen.Video & Audio Encoding Specification v1.0

A declarative video encoding, audio/video filtering, and sync correction dialect built on the Morphogen kernel.

Inspired by ffmpeg, DaVinci Resolve, and modern media processing pipelines.

0. Overview

Morphogen.Video is a typed, declarative video and audio processing dialect layered on the Morphogen kernel. It provides deterministic semantics for video encoding, filtering, transcoding, audio leveling, and synchronization operations. It represents a natural extension of Morphogen's operator DAG paradigm to multimedia streams.

Why Video Belongs in Morphogen:

Video processing is fundamentally:

Stream-based — continuous data flows through operator pipelines
Operator-based — filters, codecs, and transformations as composable ops
Filter-based — ffmpeg-style filter graphs map directly to Morphogen DAGs
Parameterizable — every operation has typed parameters (CRF, bitrate, preset)
Batchable — apply pipelines to multiple files in parallel
GPU-accelerable — hardware-accelerated encoding/decoding fits naturally
Graph-representable — video = operator DAG on AV streams

This is literally Morphogen's native shape.

Morphogen = operator DAG on structured data
Video = operator DAG on AV streams

Video fits Morphogen as naturally as audio, fields, or physics — perhaps more naturally than any of them, because ffmpeg already behaves like a domain-specific operator graph with streams, filters, and codecs.

1. Language Philosophy

Principle	Meaning
Pipeline composition	Video/audio operations compose as declarative pipelines.
Deterministic processing	Same input + same pipeline = same output (bitwise identical).
Typed streams	Video streams, audio streams, and metadata streams are typed.
Multi-rate scheduling	Handle variable frame rates, audio sample rates, and sync drift.
GPU-aware execution	Automatically leverage hardware encoders (NVENC, QuickSync, AMF).
Cross-domain integration	Video ↔ Audio ↔ Vision ↔ Geometry (overlay, 3D rendering).
Unit safety	Frame rates (fps), bitrates (kbps), time codes (ms, frames).
Filter graph equivalence	ffmpeg filter graphs map one-to-one to Morphogen pipelines.

Key insight: Video processing pipelines are typed operator DAGs with temporal constraints (sync, frame rate, time alignment).

2. Core Types

All video/audio types are defined in the kernel's type system with explicit multimedia semantics.

Type	Description	Units	Examples
`VideoStream`	Video data stream (frames)	fps, resolution	1920×1080@30fps, 4K@60fps
`AudioStream`	Audio data stream (samples)	Hz, channels	48kHz stereo, 44.1kHz mono
`Frame`	Single video frame	pixels, time	RGB frame, YUV420p frame
`AudioBuffer`	Audio sample buffer	samples, time	1024 samples @ 48kHz
`Codec`	Video/audio codec configuration	bitrate, quality	H.264, H.265, ProRes, AAC
`Filter`	Video/audio filter operator	parameters	blur, sharpen, normalize
`TimeSeries<T>`	Time-aligned data	time, offset	Audio waveform, frame timestamps
`SyncMap`	Timing alignment function	ms, frames	Drift correction curve
`Pipeline`	Composition of operators	DAG	Decode → Filter → Encode
`Metadata`	Stream metadata	various	Color space, aspect ratio, LUFS

Temporal Units:

Frame rate: fps (frames per second)
Time: ms (milliseconds), frames, samples, timecode
Bitrate: kbps (kilobits per second), Mbps
Sample rate: Hz, kHz (44.1kHz, 48kHz, 96kHz)
Audio level: dB, LUFS (Loudness Units Full Scale)

Type safety: Prevents mixing incompatible streams (can't encode audio as video).

3. Proposed Domains

Morphogen.Video introduces four interconnected domains for comprehensive multimedia processing.

3.1 VideoDomain

Purpose: Structural operations on video streams (decoding, encoding, scaling, cropping, composition).

Status: 🔲 Planned

Operators:

Decoding & Encoding:

video.decode(path: String) -> VideoStream
video.encode(stream: VideoStream, codec: Codec, path: String) -> File

Transformation:

video.scale(stream: VideoStream, width: u32, height: u32) -> VideoStream
video.crop(stream: VideoStream, x: u32, y: u32, w: u32, h: u32) -> VideoStream
video.fps(stream: VideoStream, rate: f32) -> VideoStream
video.rotate(stream: VideoStream, degrees: f32) -> VideoStream

Composition:

video.concat(streams: List<VideoStream>) -> VideoStream
video.overlay(base: VideoStream, overlay: VideoStream, x: u32, y: u32) -> VideoStream
video.blend(a: VideoStream, b: VideoStream, mode: String, opacity: f32) -> VideoStream

Text & Graphics:

video.draw_text(stream: VideoStream, text: String, font: Font, pos: Vec2) -> VideoStream
video.draw_box(stream: VideoStream, rect: Rect, color: Color) -> VideoStream

Conversion:

video.to_audio(stream: VideoStream) -> AudioStream
video.from_frames(frames: List<Frame>) -> VideoStream
video.to_frames(stream: VideoStream) -> List<Frame>
video.color_convert(stream: VideoStream, format: String) -> VideoStream

Example:

# Decode, scale, crop, encode pipeline
pipeline:
  - input = video.decode("input.mp4")
  - scaled = video.scale(input, width=1920, height=1080)
  - cropped = video.crop(scaled, x=0, y=100, w=1920, h=880)
  - codec = codec.h264(crf=18, preset="fast")
  - video.encode(cropped, codec, "output.mp4")

3.2 AudioFilterDomain

Purpose: Audio processing operations commonly handled inside ffmpeg (normalization, leveling, delay, sync).

Status: 🔲 Planned

Operators:

Loudness & Normalization:

audio.normalize(stream: AudioStream, target: f32) -> AudioStream
audio.loudnorm(stream: AudioStream, lufs: f32 = -14.0) -> AudioStream  # EBU R128
audio.measure_loudness(stream: AudioStream) -> f32 [LUFS]
audio.match_loudness(stream: AudioStream, reference: AudioStream) -> AudioStream

Dynamics:

audio.compress(stream: AudioStream, ratio: f32, threshold: f32) -> AudioStream
audio.limiter(stream: AudioStream, threshold: f32) -> AudioStream
audio.gate(stream: AudioStream, threshold: f32, ratio: f32) -> AudioStream

Timing:

audio.delay(stream: AudioStream, ms: f32) -> AudioStream
audio.trim(stream: AudioStream, start: f32, end: f32) -> AudioStream
audio.fade_in(stream: AudioStream, duration: f32) -> AudioStream
audio.fade_out(stream: AudioStream, duration: f32) -> AudioStream

Equalization:

audio.equalize(stream: AudioStream, bands: List<EQBand>) -> AudioStream
audio.bass_boost(stream: AudioStream, gain: f32) -> AudioStream
audio.treble_boost(stream: AudioStream, gain: f32) -> AudioStream

Conversion:

audio.resample(stream: AudioStream, rate: f32) -> AudioStream
audio.channel_mix(stream: AudioStream, layout: String) -> AudioStream  # stereo→mono, 5.1→stereo

Example:

# Normalize and compress audio track
pipeline:
  - input = audio.decode("dialogue.wav")
  - normalized = audio.loudnorm(input, lufs=-16.0)
  - compressed = audio.compress(normalized, ratio=4.0, threshold=-20.0)
  - audio.encode(compressed, "dialogue_processed.wav")

3.3 FilterDomain

Purpose: Visual filters equivalent to ffmpeg's -vf stack (blur, sharpen, color correction, denoise, stabilize).

Status: 🔲 Planned

Operators:

Spatial Filters:

filter.blur(stream: VideoStream, sigma: f32) -> VideoStream
filter.sharpen(stream: VideoStream, amount: f32) -> VideoStream
filter.unsharp(stream: VideoStream, amount: f32) -> VideoStream
filter.denoise(stream: VideoStream, method: String = "nlmeans") -> VideoStream

Color Correction:

filter.brightness(stream: VideoStream, amount: f32) -> VideoStream
filter.contrast(stream: VideoStream, amount: f32) -> VideoStream
filter.saturation(stream: VideoStream, amount: f32) -> VideoStream
filter.gamma(stream: VideoStream, amount: f32) -> VideoStream
filter.colorgrade(stream: VideoStream, lut: LUT) -> VideoStream
filter.white_balance(stream: VideoStream, mode: String = "auto") -> VideoStream

Artistic Effects:

filter.vignette(stream: VideoStream, intensity: f32) -> VideoStream
filter.bloom(stream: VideoStream, threshold: f32, radius: f32) -> VideoStream
filter.chromatic_aberration(stream: VideoStream, amount: f32) -> VideoStream

Temporal Effects:

filter.time_blend(stream: VideoStream, mode: String = "average") -> VideoStream
filter.deflicker(stream: VideoStream) -> VideoStream
filter.stabilize(stream: VideoStream, smoothness: f32 = 10.0) -> VideoStream

Quality:

filter.deband(stream: VideoStream) -> VideoStream
filter.deinterlace(stream: VideoStream) -> VideoStream
filter.upscale(stream: VideoStream, factor: f32, model: String = "lanczos") -> VideoStream

Example:

# ffmpeg equivalent: -vf "scale=1920:-1, unsharp=5:5:1.5"
pipeline:
  - input = video.decode("raw.mp4")
  - scaled = video.scale(input, width=1920, height=-1)  # preserve aspect
  - sharpened = filter.unsharp(scaled, amount=1.5)
  - video.encode(sharpened, codec.h264(crf=18), "output.mp4")

3.4 CodecDomain

Purpose: Expose codecs as typed operators with quality/performance parameters.

Status: 🔲 Planned

Operators:

Video Codecs:

codec.h264(crf: f32 = 23, preset: String = "medium", profile: String = "high") -> Codec
codec.h265(crf: f32 = 28, preset: String = "medium", tune: String = "none") -> Codec
codec.av1(crf: f32 = 30, speed: u32 = 6) -> Codec
codec.vp9(crf: f32 = 31, speed: u32 = 1) -> Codec
codec.prores(profile: String = "standard") -> Codec  # proxy, lt, standard, hq, 4444
codec.dnxhd(profile: String = "1080p_36") -> Codec

Image Codecs:

codec.jpeg(quality: u32 = 90) -> Codec
codec.png(compression: u32 = 6) -> Codec
codec.webp(quality: u32 = 90, lossless: bool = false) -> Codec
codec.jpegxl(distance: f32 = 1.0, effort: u32 = 7) -> Codec
codec.gif(dither: String = "sierra2_4a") -> Codec

Audio Codecs:

codec.aac(bitrate: u32 = 192) -> Codec  # kbps
codec.opus(bitrate: u32 = 128) -> Codec
codec.mp3(bitrate: u32 = 320) -> Codec
codec.flac(compression: u32 = 5) -> Codec

Hardware Acceleration:

codec.h264_nvenc(crf: f32 = 23, preset: String = "p4") -> Codec  # Nvidia
codec.h265_nvenc(crf: f32 = 28, preset: String = "p4") -> Codec
codec.h264_qsv(crf: f32 = 23) -> Codec  # Intel QuickSync
codec.h264_amf(crf: f32 = 23) -> Codec  # AMD

Example:

# High-quality ProRes export
codec = codec.prores(profile="hq")
video.encode(stream, codec, "output.mov")

# GPU-accelerated H.265 with Nvidia
codec = codec.h265_nvenc(crf=20, preset="p7")  # p7 = slowest/best quality
video.encode(stream, codec, "output.mp4")

4. Audio/Video Synchronization (SyncDomain)

Morphogen's Sweet Spot: Time-domain alignment, signal processing, phase correction, and offset detection.

Morphogen already treats time domains, signals, phases, offsets, and transforms as first-class objects. This makes sync correction natural.

4.1 Common Sync Problems

Problem 1: Constant Offset Drift

Video lagging behind audio (or vice versa) by a fixed amount.

Detection methods:

Audio onset vs. video event detection (flash, clapboard)
Waveform correlation vs. visual mouth movement
Cross-spectrum analysis

Operators:

sync.detect_constant_offset(video: VideoStream, audio: AudioStream) -> f32 [ms]
sync.apply_offset(stream: AudioStream, offset: f32 [ms]) -> AudioStream

Example:

# Detect and fix constant sync drift
offset = sync.detect_constant_offset(video, audio)  # Returns: +143ms
audio_fixed = sync.apply_offset(audio, offset)

Problem 2: Variable Drift (Progressive Desync)

Sync gets worse over time due to:

Variable frame rate
Incorrect sample rate
Dropped frames
Bad capture hardware

Mathematical model:

offset(t) = a*t + b  (linear drift)
or
offset(t) = spline(t)  (nonlinear drift)

Operators:

sync.detect_drift(video: VideoStream, audio: AudioStream) -> SyncMap
sync.timewarp(stream: AudioStream, map: SyncMap) -> AudioStream
sync.resample_with_drift_compensation(stream: AudioStream, map: SyncMap) -> AudioStream

Example:

# Detect and fix progressive drift
drift_map = sync.detect_drift(video, audio)  # Returns: SyncMap(linear, a=0.02, b=100)
audio_fixed = sync.timewarp(audio, drift_map)

Problem 3: Clapboard Detection (Event Alignment)

Automatically align video flash with audio clap.

Operators:

vision.detect_flash(video: VideoStream) -> f32 [frames]
audio.detect_clap(audio: AudioStream) -> f32 [samples]
sync.align_events(visual_event: f32, audio_event: f32) -> f32 [ms]

Example:

# Automatic clapboard sync
flash_frame = vision.detect_flash(video)
clap_sample = audio.detect_clap(audio)
offset = sync.align_events(flash_frame, clap_sample)
audio_synced = sync.apply_offset(audio, offset)

Problem 4: Automatic Re-timing for Lip-Sync

Detect mouth movement and align with audio envelope.

Operators:

vision.detect_mouth_open(video: VideoStream) -> TimeSeries<bool>
audio.envelope(audio: AudioStream) -> TimeSeries<f32>
sync.align_signals(visual: TimeSeries<T>, audio: TimeSeries<U>) -> SyncMap

Example:

# Lip-sync alignment
mouth_events = vision.detect_mouth_open(video)
audio_env = audio.envelope(audio)
sync_map = sync.align_signals(mouth_events, audio_env)
audio_synced = sync.timewarp(audio, sync_map)

4.2 Audio Level Matching / Loudness Correction

ffmpeg supports EBU R128 loudness normalization, but it's cumbersome. Morphogen makes it first-class.

Operators:

audio.measure_loudness(stream: AudioStream) -> f32 [LUFS]
audio.loudnorm_to(stream: AudioStream, target: f32 [LUFS]) -> AudioStream
audio.match_loudness(stream: AudioStream, reference: AudioStream) -> AudioStream
audio.compress(stream: AudioStream, ratio: f32, threshold: f32) -> AudioStream
audio.auto_mix(streams: List<AudioStream>) -> AudioStream

Smart logic:

# Detect quiet dialogue and boost speech frequencies
dialogue = audio.detect_speech_regions(stream)
boosted = audio.equalize(dialogue, bands=[
    {freq: 2000, gain: 3.0, q: 1.0},  # presence boost
    {freq: 200, gain: -2.0, q: 0.7}   # mud reduction
])

# Duck background music when dialogue is present
music_ducked = audio.duck(music, dialogue, threshold=-30.0, ratio=0.3)

Example:

# Normalize all audio tracks to -14 LUFS (broadcast standard)
dialogue = audio.loudnorm_to(dialogue_raw, -14.0)
music = audio.loudnorm_to(music_raw, -14.0)
sfx = audio.loudnorm_to(sfx_raw, -14.0)

# Mix with automatic level balancing
mixed = audio.auto_mix([dialogue, music, sfx])

5. Filter Graphs as Morphogen Pipelines

ffmpeg filter graphs map one-to-one to Morphogen pipelines.

5.1 ffmpeg → Morphogen Equivalence

ffmpeg:

ffmpeg -i input.mp4 \
  -vf "scale=1920:-1, unsharp=5:5:1.5, eq=brightness=0.1:contrast=1.2" \
  -c:v libx264 -crf 18 -preset fast \
  output.mp4

Morphogen:

pipeline:
  - input = video.decode("input.mp4")
  - scaled = video.scale(input, width=1920, height=-1)
  - sharpened = filter.unsharp(scaled, amount=1.5)
  - corrected = filter.brightness(sharpened, amount=0.1)
  - corrected = filter.contrast(corrected, amount=1.2)
  - codec = codec.h264(crf=18, preset="fast")
  - video.encode(corrected, codec, "output.mp4")

Cleaner. Composable. GPU-aware.

5.2 Complex Filter Graph Example

ffmpeg:

ffmpeg -i video.mp4 -i watermark.png \
  -filter_complex "[0:v]scale=1280:720[scaled]; \
                   [scaled][1:v]overlay=W-w-10:H-h-10[output]" \
  -map "[output]" output.mp4

Morphogen:

pipeline:
  - video = video.decode("video.mp4")
  - watermark = video.decode("watermark.png")
  - scaled = video.scale(video, width=1280, height=720)
  - output = video.overlay(scaled, watermark, x=-10, y=-10)  # relative to bottom-right
  - video.encode(output, codec.h264(crf=23), "output.mp4")

6. Batch Processing

Morphogen excels at batch pipelines with parallel execution.

6.1 Batch Operators

batch.apply_to_files(pattern: String, pipeline: Pipeline) -> List<File>
batch.parallel(n: u32, pipelines: List<Pipeline>) -> List<Result>
batch.map(files: List<File>, fn: (File) -> File) -> List<File>

6.2 Use Cases

Encode entire folder:

# Transcode all MP4s in a folder to H.265
batch.apply_to_files("videos/*.mp4", pipeline=[
    video.decode,
    video.encode(codec=codec.h265(crf=28), output="encoded/{name}.mp4")
])

Re-sync all videos:

# Detect and fix sync issues in all files
batch.map("footage/*.mp4", fn=(file) => {
    video = video.decode(file)
    audio = video.to_audio(video)
    offset = sync.detect_constant_offset(video, audio)
    audio_fixed = sync.apply_offset(audio, offset)
    video.encode(video, audio_fixed, "synced/{name}.mp4")
})

Replace audio tracks:

# Replace audio in all videos with processed versions
batch.parallel(n=8, [
    for file in glob("videos/*.mp4"):
        video = video.decode(file).strip_audio()
        audio = audio.decode("processed_audio/{name}.wav")
        combined = video.add_audio(video, audio)
        video.encode(combined, "output/{name}.mp4")
])

Normalize all loudness:

# Normalize all audio files to -16 LUFS
batch.apply_to_files("audio/*.wav", pipeline=[
    audio.decode,
    audio.loudnorm_to(-16.0),
    audio.encode(output="normalized/{name}.wav")
])

7. GPU Acceleration

Morphogen maps naturally to GPU-accelerated codecs.

7.1 GPU Operators

gpu.accelerate(codec: Codec, backend: String = "auto") -> Codec
gpu.filter(filter: Filter, backend: String = "auto") -> Filter

Backends:

"nvenc" — Nvidia hardware encoding (H.264, H.265, AV1)
"qsv" — Intel QuickSync
"amf" — AMD Advanced Media Framework
"videotoolbox" — Apple hardware encoding (macOS/iOS)
"auto" — Detect available GPU and use best backend

7.2 Example

# Automatically use GPU if available
codec = codec.h265(crf=23, preset="medium")
codec_gpu = gpu.accelerate(codec, backend="auto")
video.encode(stream, codec_gpu, "output.mp4")

# Explicit Nvidia encoding
codec = codec.h264_nvenc(crf=20, preset="p7")
video.encode(stream, codec, "output.mp4")

# GPU-accelerated denoise filter
denoised = gpu.filter(filter.denoise(stream, method="nlmeans"))

8. Magic "Fix My Video" Operator

Morphogen can build a high-level convenience operator that automatically fixes common issues.

8.1 Operator

video.fix(input: String, output: String, options: FixOptions = {}) -> File

FixOptions:

struct FixOptions {
    detect_sync: bool = true
    detect_color_cast: bool = true
    denoise: bool = true
    stabilize: bool = true
    auto_white_balance: bool = true
    loudness_normalize: bool = true
    upscale_factor: f32 = 1.0
    upscale_model: String = "lanczos"
    output_codec: Codec = codec.h264(crf=18)
}

8.2 Implementation

fn video.fix(input: String, output: String, options: FixOptions) -> File {
    # Decode
    video = video.decode(input)
    audio = video.to_audio(video)

    # Detect and fix sync issues
    if options.detect_sync {
        offset = sync.detect_constant_offset(video, audio)
        audio = sync.apply_offset(audio, offset)
    }

    # Detect color cast
    if options.detect_color_cast {
        video = filter.auto_color_correct(video)
    }

    # Denoise
    if options.denoise {
        video = filter.denoise(video, method="nlmeans")
    }

    # Stabilize
    if options.stabilize {
        video = filter.stabilize(video, smoothness=10.0)
    }

    # Auto white balance
    if options.auto_white_balance {
        video = filter.white_balance(video, mode="auto")
    }

    # Loudness normalize
    if options.loudness_normalize {
        audio = audio.loudnorm_to(audio, -16.0)
    }

    # Upscale
    if options.upscale_factor > 1.0 {
        video = filter.upscale(video, factor=options.upscale_factor, model=options.upscale_model)
    }

    # Encode
    video = video.add_audio(video, audio)
    return video.encode(video, options.output_codec, output)
}

8.3 Usage

# One-liner to fix common issues
video.fix("raw_footage.mp4", "fixed_footage.mp4")

# Custom options
video.fix("raw_footage.mp4", "fixed_footage.mp4", options={
    denoise: true,
    stabilize: true,
    upscale_factor: 2.0,
    upscale_model: "esrgan",
    output_codec: codec.prores(profile="hq")
})

Equivalent to DaVinci Resolve's auto-magic, but scripted and deterministic.

9. What Morphogen Gains

9.1 New Major Domains

VideoDomain — Structural video operations (decode, encode, scale, crop, concat)
AudioFilterDomain — Audio processing (normalize, compress, delay, EQ)
FilterDomain — Visual filters (blur, sharpen, color correction, denoise)
CodecDomain — Codec configuration (H.264, H.265, ProRes, AAC)
SyncDomain — Time alignment (offset detection, drift correction, event sync)
BatchDomain — Parallel batch processing (encode folders, apply filters)
VisionDomain — Computer vision for video (flash detection, mouth tracking) [future]

9.2 New Operator Categories

Category	Operators
Encoding	decode, encode, transcode, remux
Decoding	decode video, decode audio, extract frames, extract metadata
Filtering	blur, sharpen, denoise, stabilize, color correction
Color Correction	brightness, contrast, saturation, gamma, LUT, white balance
Transformation	scale, crop, rotate, flip, pad, trim
Compositing	overlay, blend, concat, transition, alpha compositing
Audio Leveling	normalize, loudnorm, compress, limiter, gate
Time Alignment	offset detection, drift correction, timewarp, event sync
Stabilization	motion analysis, smoothing, rolling shutter correction
Upscaling	Lanczos, bicubic, ESRGAN, Real-ESRGAN, Waifu2x
Format Conversion	color space conversion, frame rate conversion, aspect ratio
Batch Processing	parallel encoding, folder processing, pipeline mapping
GPU Acceleration	NVENC, QuickSync, AMF, VideoToolbox

9.3 Cross-Domain Integration

Morphogen.Video naturally integrates with existing domains:

Video ↔ Audio:

audio = video.to_audio(video_stream)
video = video.add_audio(video_stream, audio_stream)

Video ↔ Vision:

frames = video.to_frames(video_stream)
analysis = vision.detect_objects(frames)
annotated = vision.draw_bboxes(frames, analysis)
video_out = video.from_frames(annotated)

Video ↔ Geometry (3D rendering):

# Render 3D scene to video frames
geometry = geometry.load("model.obj")
camera = camera.orbit(center=(0,0,0), radius=5.0, frames=300)
frames = render.frames(geometry, camera)
video = video.from_frames(frames)
video.encode(video, codec.h264(crf=18), "render.mp4")

Video ↔ Fields (Fluid overlay):

# Render fluid simulation as video overlay
@state vel : Field2D<Vec2<f32>> = zeros((1920, 1080))

flow(dt=0.01, steps=300) {
    vel = advect(vel, vel, dt)
    frame = visual.field_to_frame(vel, palette="viridis")
    output frame
}

video = video.from_frames(output_frames)
base = video.decode("background.mp4")
composited = video.overlay(base, video, x=0, y=0, opacity=0.5)

10. Implementation Priority

Phase 1: Core Video Operations (MVP)

VideoDomain basics: decode, encode, scale, crop
CodecDomain: H.264, H.265, ProRes
FilterDomain basics: blur, sharpen, brightness, contrast
Pipeline composition: Chain operators into DAGs

Phase 2: Audio Processing

AudioFilterDomain: normalize, loudnorm, compress, delay
Audio-video muxing: Combine audio + video streams
Basic sync: Constant offset detection and correction

Phase 3: Advanced Sync & Batch

SyncDomain: Drift detection, timewarp, event alignment
BatchDomain: Parallel processing, folder encoding
GPU acceleration: NVENC, QuickSync integration

Phase 4: Magic Operators & Vision

video.fix(): Auto-magic video correction
VisionDomain basics: Flash detection, object tracking
Advanced filters: Stabilization, upscaling (ESRGAN)

11. ffmpeg Integration Strategy

Morphogen doesn't need to reimplement ffmpeg — it can orchestrate ffmpeg as a backend.

11.1 Backend Architecture

Morphogen Pipeline → Graph IR → Backend Compiler → ffmpeg command

Example:

Morphogen code:

pipeline:
  - input = video.decode("input.mp4")
  - scaled = video.scale(input, width=1920, height=1080)
  - sharpened = filter.unsharp(scaled, amount=1.5)
  - video.encode(sharpened, codec.h264(crf=18), "output.mp4")

Compiled ffmpeg command:

ffmpeg -i input.mp4 \
  -vf "scale=1920:1080, unsharp=5:5:1.5" \
  -c:v libx264 -crf 18 \
  -y output.mp4

11.2 Advantages

No reimplementation: Leverage ffmpeg's 20+ years of codec/filter development
Type safety: Morphogen validates parameters at compile time
Composability: Pipelines are first-class objects
Determinism: Same Morphogen code → same ffmpeg command → same output
Optimization: Morphogen can optimize filter graphs before compilation
GPU awareness: Morphogen can auto-select hardware codecs based on system

11.3 Alternative Backends

For performance-critical or embedded use cases, Morphogen can also target:

Custom C++ backend: Direct codec/filter implementation
GStreamer: Alternative multimedia framework
GPU compute shaders: Direct GPU video processing
Hardware APIs: NVENC, VAAPI, VideoToolbox SDKs

12. Example Use Cases

12.1 YouTube Upload Pipeline

Problem: Prepare raw footage for YouTube upload (1080p, H.264, stereo audio, normalized loudness).

Morphogen solution:

pipeline:
  - video = video.decode("raw_footage.mov")
  - audio = video.to_audio(video)

  # Video processing
  - video = video.scale(video, width=1920, height=1080)
  - video = filter.denoise(video, method="nlmeans")
  - video = filter.sharpen(video, amount=1.2)

  # Audio processing
  - audio = audio.loudnorm_to(audio, -14.0)  # YouTube recommendation
  - audio = audio.compress(audio, ratio=3.0, threshold=-18.0)

  # Encode
  - video = video.add_audio(video, audio)
  - codec = codec.h264(crf=18, preset="slow", profile="high")
  - video.encode(video, codec, "youtube_upload.mp4")

12.2 Podcast Episode Processing

Problem: Normalize loudness, remove background noise, add intro/outro music.

Morphogen solution:

pipeline:
  - dialogue = audio.decode("raw_dialogue.wav")
  - intro = audio.decode("intro_music.wav")
  - outro = audio.decode("outro_music.wav")

  # Denoise dialogue
  - dialogue = audio.denoise(dialogue, method="spectral_subtraction")

  # Normalize loudness (podcast standard: -16 LUFS)
  - dialogue = audio.loudnorm_to(dialogue, -16.0)
  - intro = audio.loudnorm_to(intro, -16.0)
  - outro = audio.loudnorm_to(outro, -16.0)

  # Concat with fades
  - intro = audio.fade_out(intro, duration=2.0)
  - outro = audio.fade_in(outro, duration=2.0)
  - episode = audio.concat([intro, dialogue, outro])

  # Encode
  - audio.encode(episode, codec.aac(bitrate=192), "episode.m4a")

12.3 Multi-Camera Sync

Problem: Sync 3 camera angles from a concert (different start times, slight drift).

Morphogen solution:

pipeline:
  - cam1 = video.decode("cam1.mp4")
  - cam2 = video.decode("cam2.mp4")
  - cam3 = video.decode("cam3.mp4")

  # Detect flash event (light cue at start)
  - flash1 = vision.detect_flash(cam1)
  - flash2 = vision.detect_flash(cam2)
  - flash3 = vision.detect_flash(cam3)

  # Align to cam1 as reference
  - offset2 = flash2 - flash1
  - offset3 = flash3 - flash1

  - cam2 = sync.apply_offset(cam2, offset2)
  - cam3 = sync.apply_offset(cam3, offset3)

  # Detect and fix progressive drift
  - drift2 = sync.detect_drift(cam1, cam2)
  - drift3 = sync.detect_drift(cam1, cam3)

  - cam2 = sync.timewarp(cam2, drift2)
  - cam3 = sync.timewarp(cam3, drift3)

  # Encode synced videos
  - video.encode(cam1, codec.prores(profile="hq"), "cam1_synced.mov")
  - video.encode(cam2, codec.prores(profile="hq"), "cam2_synced.mov")
  - video.encode(cam3, codec.prores(profile="hq"), "cam3_synced.mov")

12.4 Batch Transcode for Archive

Problem: Convert 500 old MOV files (ProRes) to modern H.265 (HEVC) for storage.

Morphogen solution:

# Parallel batch processing (8 concurrent encodes)
batch.parallel(n=8,
  batch.map("archive/*.mov", fn=(file) => {
    video = video.decode(file)
    codec = codec.h265(crf=28, preset="slow")
    video.encode(video, codec, "h265_archive/{name}.mp4")
  })
)

12.5 AI Upscaling Pipeline

Problem: Upscale 720p footage to 4K using ESRGAN model.

Morphogen solution:

pipeline:
  - video = video.decode("720p_source.mp4")
  - frames = video.to_frames(video)

  # AI upscale (4x)
  - upscaled_frames = frames.map(|frame| {
      filter.upscale(frame, factor=4.0, model="realesrgan")
  })

  - upscaled_video = video.from_frames(upscaled_frames)
  - codec = codec.h265(crf=18, preset="slow")
  - video.encode(upscaled_video, codec, "4k_upscaled.mp4")

13. Performance Characteristics

13.1 Determinism Guarantees

Operation	Determinism Level	Notes
Decode	Bitwise identical	Same file → same frames
Encode (lossless)	Bitwise identical	Same input → same output
Encode (lossy)	Deterministic*	Same parameters → same bitstream (if encoder is deterministic)
Filters (spatial)	Bitwise identical	Same input → same output
Filters (temporal)	Bitwise identical	Deterministic frame order
Sync detection	Reproducible	May vary with algorithm parameters
Batch processing	Order-independent	Parallel execution, deterministic results

* Note: Some encoders (e.g., x264, x265) are deterministic if run single-threaded. Multi-threaded encoding may introduce non-determinism. Morphogen can enforce single-threaded mode for strict determinism.

13.2 Performance Optimization

Pipeline fusion:

# Before fusion (3 passes):
scaled = video.scale(input, width=1920, height=1080)
sharpened = filter.unsharp(scaled, amount=1.5)
brightened = filter.brightness(sharpened, amount=0.1)

# After fusion (1 pass):
# Morphogen optimizer merges filters into single pass
output = video.apply_filters(input, [
    scale(1920, 1080),
    unsharp(1.5),
    brightness(0.1)
])

GPU offloading:

# Automatically detect GPU and offload heavy operations
config = gpu.auto_detect()  # Returns: {backend: "nvenc", available: true}

if config.available {
    codec = codec.h265_nvenc(crf=23)
} else {
    codec = codec.h265(crf=23)
}

Parallel batch processing:

# Process 100 videos using all CPU cores
batch.parallel(n=cpu.cores(),
    batch.map("videos/*.mp4", encode_pipeline)
)

14. Integration with Existing Morphogen Domains

14.1 Audio Domain

Already implemented in v0.5.0 and v0.6.0!

Morphogen.Video extends the existing Audio domain with filtering and sync operations.

Existing operators:

audio.play() — Real-time playback
audio.save() — WAV/FLAC export
audio.load() — Load audio files
audio.record() — Microphone recording

New operators (Morphogen.Video):

audio.loudnorm() — EBU R128 loudness normalization
audio.compress() — Dynamics compression
audio.delay() — Time delay
audio.sync_to() — Sync to video stream

Cross-domain example:

# Load video, process audio with existing Audio domain
video = video.decode("concert.mp4")
audio = video.to_audio(video)

# Use Morphogen.Audio operators
audio = audio |> reverb(mix=0.2) |> limiter(threshold=-1.0)

# Add back to video
video = video.add_audio(video, audio)
video.encode(video, codec.h264(crf=18), "concert_processed.mp4")

14.2 Visual Domain

Already implemented in v0.6.0!

Morphogen.Video extends the Visual domain to export video instead of static images.

Existing operators:

visual.save() — PNG/JPEG export
visual.show() — Interactive display
visual.video() — MP4/GIF export (NEW in v0.6.0!)

New operators (Morphogen.Video):

visual.to_video_stream() — Convert frame generator to VideoStream
visual.from_video_stream() — Convert VideoStream to frames

Cross-domain example:

# Render field simulation as video
@state temp : Field2D<f32> = random_normal(seed=42, shape=(512, 512))

flow(dt=0.01, steps=300) {
    temp = diffuse(temp, rate=0.1, dt)
    frame = colorize(temp, palette="fire")
    output frame
}

# Export as video (existing v0.6.0 feature)
visual.video(output_frames, "heat_diffusion.mp4", fps=30)

# Or use new Morphogen.Video operators
video = visual.to_video_stream(output_frames, fps=30)
video = filter.sharpen(video, amount=1.2)  # Apply video filter
video.encode(video, codec.h265(crf=20), "heat_diffusion_hq.mp4")

14.3 Transform Domain

Already implemented!

Morphogen.Video can use Transform domain for audio/video analysis.

Cross-domain example:

# Detect sync using cross-correlation (FFT-based)
audio1_fft = fft(audio1.samples)
audio2_fft = fft(audio2.samples)
cross_corr = ifft(audio1_fft * conj(audio2_fft))
offset = argmax(cross_corr)  # Peak = offset in samples

audio2_synced = sync.apply_offset(audio2, offset)

14.4 Agent Domain (Future)

Example: Particle overlay on video

# Simulate particles and render onto video
@state particles : Agents<Particle> = alloc(count=1000, init=spawn_particle)

flow(dt=0.01, steps=300) {
    particles = particles.map(|p| {
        vel: p.vel + gravity * dt,
        pos: p.pos + p.vel * dt
    })

    # Render particles to frame
    frame = visual.agents(particles, width=1920, height=1080, size=3.0)
    output frame
}

# Composite onto video
base_video = video.decode("background.mp4")
particle_video = visual.to_video_stream(output_frames, fps=30)
composited = video.overlay(base_video, particle_video, x=0, y=0, opacity=0.8)
video.encode(composited, codec.h264(crf=18), "particles_overlay.mp4")

15. Why This Matters

Morphogen becomes the only platform that unifies:

✅ Audio synthesis (oscillators, filters, effects, physical modeling) ✅ Video encoding (codecs, filters, transcoding, batch processing) ✅ Audio/video sync (drift correction, event alignment, lip-sync) ✅ Field simulation (fluids, reaction-diffusion, heat transfer) ✅ Agent simulation (particles, boids, N-body) ✅ Geometry (parametric CAD, mesh operations) ✅ Circuit simulation (analog audio, PCB layout) ✅ Optimization (design discovery, parameter tuning)

All domains share the same:

Type system
Scheduler
MLIR compilation
Deterministic execution model
Cross-domain operators

This positions Morphogen as:

🎬 Universal multimedia processing platform (ffmpeg + DaVinci Resolve + Audacity) 🎛️ Creative computation kernel (generative art, music, video) 🔬 Multi-physics simulation engine (engineering, research, education) 🎨 Parametric design system (CAD, PCB, 3D modeling)

No other platform offers this level of integration.

16. Roadmap Summary

Phase	Features	Timeline
Phase 1: MVP	VideoDomain basics (decode, encode, scale, crop), CodecDomain (H.264, H.265), FilterDomain basics (blur, sharpen, color correction)	Q1 2026
Phase 2: Audio	AudioFilterDomain (normalize, compress, delay), audio-video muxing, basic sync (constant offset)	Q2 2026
Phase 3: Advanced	SyncDomain (drift detection, timewarp), BatchDomain (parallel processing), GPU acceleration (NVENC, QuickSync)	Q3 2026
Phase 4: Magic	video.fix() auto-magic operator, VisionDomain basics (flash detection), advanced filters (stabilization, AI upscaling)	Q4 2026

Dependencies:

MLIR integration (v0.7.0) ✅ Complete
Audio domain (v0.5.0, v0.6.0) ✅ Complete
Visual domain (v0.6.0) ✅ Complete
Transform domain ✅ Complete
Geometry domain (v0.9.0+) 🔲 Planned

17. Conclusion

Video encoding, audio/video filtering, sync correction, and ffmpeg-style pipelines fit Morphogen perfectly.

In fact, they map onto Morphogen's architecture better than audio or physics, because ffmpeg already behaves like a domain-specific operator graph with streams, filters, and codecs.

Morphogen = operator DAG on structured data Video = operator DAG on AV streams

By adding VideoDomain, AudioFilterDomain, FilterDomain, CodecDomain, SyncDomain, and BatchDomain, Morphogen becomes:

✅ Cleaner than ffmpeg (typed operators, composable pipelines) ✅ More powerful than ffmpeg (GPU-aware, cross-domain integration, AI upscaling) ✅ More deterministic than ffmpeg (same code → same output, always) ✅ More accessible than DaVinci Resolve (scripted, batchable, version-controllable)

This is a huge new slice of capability — but one that fits perfectly with Morphogen's core architecture.

Video belongs in Morphogen. Let's build it.

Version: 1.0 Status: Specification (Ready for Implementation) Last Updated: 2025-11-15 Author: Morphogen Architecture Team Related Specs: transform.md, circuit.md, timbre-extraction.md, ../architecture/domain-architecture.md

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🎬 Morphogen.Video & Audio Encoding Specification v1.0

0. Overview

1. Language Philosophy

2. Core Types

3. Proposed Domains

3.1 VideoDomain

3.2 AudioFilterDomain

3.3 FilterDomain

3.4 CodecDomain

4. Audio/Video Synchronization (SyncDomain)

4.1 Common Sync Problems

4.2 Audio Level Matching / Loudness Correction

5. Filter Graphs as Morphogen Pipelines

5.1 ffmpeg → Morphogen Equivalence

5.2 Complex Filter Graph Example

6. Batch Processing

6.1 Batch Operators

6.2 Use Cases

7. GPU Acceleration

7.1 GPU Operators

7.2 Example

8. Magic "Fix My Video" Operator

8.1 Operator

8.2 Implementation

8.3 Usage

9. What Morphogen Gains

9.1 New Major Domains

9.2 New Operator Categories

9.3 Cross-Domain Integration

10. Implementation Priority

Phase 1: Core Video Operations (MVP)

Phase 2: Audio Processing

Phase 3: Advanced Sync & Batch

Phase 4: Magic Operators & Vision

11. ffmpeg Integration Strategy

11.1 Backend Architecture

11.2 Advantages

11.3 Alternative Backends

12. Example Use Cases

12.1 YouTube Upload Pipeline

12.2 Podcast Episode Processing

12.3 Multi-Camera Sync

12.4 Batch Transcode for Archive

12.5 AI Upscaling Pipeline

13. Performance Characteristics

13.1 Determinism Guarantees

13.2 Performance Optimization

14. Integration with Existing Morphogen Domains

14.1 Audio Domain

14.2 Visual Domain

14.3 Transform Domain

14.4 Agent Domain (Future)

15. Why This Matters

16. Roadmap Summary

17. Conclusion