elara-visual 0.1.0

ELARA Protocol - Visual processing with keyframe encoding, prediction, and degradation
Documentation
  • Coverage
  • 61.78%
    215 out of 348 items documented0 out of 128 items with examples
  • Size
  • Source code size: 90.27 kB This is the summed size of all the files inside the crates.io package for this release.
  • Documentation size: 12.62 MB This is the summed size of all files generated by rustdoc for all configured targets
  • Ø build duration
  • this release: 21s Average build duration of successful builds.
  • all releases: 21s Average build duration of successful builds in releases after 2024-10-23.
  • Links
  • Homepage
  • rafaelsistems/ELARA-Protocol
    1 0 0
  • crates.io
  • Dependencies
  • Versions
  • Owners
  • Rafael2022-prog

elara-visual

Visual processing engine for the ELARA Protocol - featuring keyframe encoding, predictive algorithms, and graceful degradation for real-time video communication.

Features

  • Keyframe Encoding: Efficient video state representation
  • Predictive Algorithms: Temporal and spatial prediction
  • Graceful Degradation: Quality reduction under network stress
  • Face State Processing: Emotion and expression tracking
  • Pose Prediction: Body movement prediction and interpolation
  • Scene Reduction: Automatic detail level adjustment

Quick Start

use elara_visual::{VisualEncoder, VisualPredictor, VisualState};
use elara_core::StateId;

// Create visual encoder
let mut encoder = VisualEncoder::new(config);

// Create predictor
let mut predictor = VisualPredictor::new(prediction_config);

// Encode visual frame
let visual_state = encoder.encode_frame(frame_data)?;

// Predict next state
let predicted = predictor.predict(&current_state, &history)?;

Visual State Model

Keyframe Structure

pub struct VisualState {
    pub face: FaceState,        // Facial expressions and emotions
    pub pose: PoseState,        // Body position and movement
    pub scene: SceneState,      // Environmental context
    pub timestamp: StateTime,   // Temporal reference
}

Degradation Levels

Full Quality → Reduced Quality → Symbolic → Presence Only
     ↓              ↓              ↓           ↓
  Complete      Essential     Minimal     Existence
  Detail        Features      Info        Proof

Encoding Process

// High quality encoding
let high_quality = encoder.encode_with_quality(frame, Quality::High)?;

// Adaptive encoding based on network
let adaptive = encoder.encode_adaptive(frame, network_quality)?;

// Symbolic encoding for minimal bandwidth
let symbolic = encoder.encode_symbolic(frame)?;

Prediction Algorithms

Face Prediction

  • Emotion vector interpolation
  • Viseme generation from phonemes
  • Eye movement prediction

Pose Prediction

  • Joint position interpolation
  • Movement trajectory prediction
  • Constraint-based correction

Scene Prediction

  • Background stability detection
  • Lighting change prediction
  • Object persistence tracking

Network Adaptation

// Update based on network conditions
encoder.adapt_to_network(network_quality);

// Manual quality setting
encoder.set_quality_level(QualityLevel::Medium);

// Automatic degradation
encoder.enable_auto_degradation(true);

License

Licensed under either of:

at your option.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.