AAL-Plus Image Quality Assessment
A lightweight model that predicts compression quality levels and detects artifacts across multiple image formats (JPEG, WebP, AVIF, JXL). Trained to identify subtle compression artifacts that indicate the quality level used during image encoding.
📊 Model Performance
Based on validation results from the final training epochs:
| Metric | Value |
|---|---|
| Overall Validation Accuracy | 97.1% |
| Overall Validation Loss | 0.0006 |
| Training Accuracy | 95.6% |
Per-Format Accuracy
| Format | Validation Acc | Training Acc | Quality Range |
|---|---|---|---|
| JPEG | 99.4% | 98.9% | 0-100 |
| WebP | 97.0% | 95.5% | 0-100 |
| AVIF | 97.1% | 95.3% | 0-100 |
| JXL | 94.8% | 92.6% | 0-100 |
Accuracy measured as predictions within ±5% range of actual quality values
🎯 Key Features
- Multi-format Support: Detects artifacts in JPEG, WebP, AVIF, and JXL formats
- Lightweight Architecture: Only ~2M parameters (~8MB model size)
- High Precision: 99.4% on JPEG
- Fast Inference: Optimized for real-time processing even on very weak HW using either CPU or GPU
- Format-agnostic: Single model handles all compression types
Limitations
- Resolution Dependency: Model trained on 512x512 crops; very small images may yield reduced accuracy
- Quality Range: Model assumes standard quality ranges; non-standard encoders may produce different results
- Image Resizing: Trying to detect compression artifacs in images which were compressed and than converted to lossless format and resized may fail or be much less accurate as these artifacts are washed by the resizing algorithms.
Environmental Impact
- Training: ~12 GPU hours on RTX 5090
- Model Size: 8MB (reduces storage and bandwidth costs)