Module image analysis Future

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Image Analysis Module - Future Enhancements

Scope

• Computer vision backend extension: support for additional OCR engines and object detection models.
• Video frame analysis and streaming image ingestion.
• Custom model fine-tuning and deployment pipeline integration.
• Advanced feature extraction for multimodal similarity search.

Design Constraints

• All new backend plugins must implement the existing ImageAnalysisPlugin interface without breaking the frozen v1 public API in include/image_analysis/ (Target: ongoing)
• New model formats must be loaded via the ONNX Runtime or an equivalent safe inference path; direct native library linking must be explicitly approved (Target: ongoing)
• Feature vectors generated by new backends must be dimensionally compatible with the existing similarity index configuration or must include a migration path (Target: Q2 2027)
• Security and backend-isolation defaults must remain fail-closed; new backends must degrade gracefully when unavailable (Target: ongoing)
• Performance gates (OCR P99 ≤ 100 ms, detection P99 ≤ 200 ms) must remain met or explicitly re-baselined in PERFORMANCE_EXPECTATIONS.md before merge (Target: ongoing)

Required Interfaces

Interface	Consumer	Notes
ImageProcessor::processImage(path)	content indexing pipeline	frozen v1; new backends must not change signature
FeatureExtractor::extractFeatures(image)	similarity search index	embedding dimension and normalisation must remain stable
ImageCache::get/put	all backends	TTL and hash-key contract must not change
ImageAnalysisPlugin (virtual)	all backend plugins	new plugins implement this interface

Implementation Notes

Additional OCR Engine Support

Priority: Medium Target: Q2 2027

• Add PaddleOCR or EasyOCR as alternative backends via the existing plugin mechanism.
• Gate activation on HAVE_PADDLEOCR / HAVE_EASYOCR build flags, matching the existing HAVE_TESSERACT pattern.
• Ensure timeout and confidence-score contracts match the Tesseract plugin contract.
• Add plugin-selection configuration key to ImageProcessor configuration.

Video Frame Analysis

Priority: Low Target: Q3 2027

• Introduce VideoFrameProcessor component for periodic key-frame extraction and analysis.
• Delegate individual frame analysis to existing ImageProcessor.
• Define maximum frame extraction rate (default: ≤ 1 key frame/sec) to bound resource usage.
• Video support must remain decoupled from static image paths — no shared mutable state.

Custom Model Pipeline

Priority: Low Target: Q3 2027

• Define ModelRegistry interface for registering custom ONNX models with class manifests.
• Provide model validation (signature check, class count) before activation.
• Include model integrity verification (HMAC/SHA-256) to prevent tampered model loading.
• Document activation and rollback semantics.

Multimodal Embedding Improvement

Priority: Medium Target: Q2 2027

• Integrate CLIP-based embedding generation as an optional feature extraction backend alongside the current text+object feature composition.
• Target embedding dimension: configurable 512–1024 to match deployed vector index.
• Add parity tests comparing CLIP-based and composition-based vectors on benchmark datasets.

Async Processing Queue

Priority: Medium Target: Q4 2026

• Decouple image analysis from synchronous document ingestion via a configurable task queue.
• Define queue depth, back-pressure, and timeout-retry semantics.
• Observability: expose queue depth, processing latency, and error rate as metrics.

Test Strategy

• Plugin contract regression tests for every new backend using the existing mock plugin interface.
• Latency gate tests for new backends must be added to the benchmark suite before GA.
• Soak tests for long-running mixed OCR/detection/video workloads.
• Model integrity validation tests for custom model loading paths.
• Parity tests comparing new and existing feature extraction paths on shared benchmark datasets.

Performance Targets

• New OCR backends must meet or exceed the existing P99 ≤ 100 ms per image gate.
• Video frame extraction must not exceed 20% CPU overhead on the indexing pipeline thread pool.
• Custom model loading must complete within 5 seconds at startup; subsequent inference remains within existing gates.
• Multimodal embedding generation must not exceed P99 ≤ 150 ms per image on reference hardware.

Security / Reliability

• New backends must implement backend-unavailability graceful degradation matching the existing plugin lifecycle contract.
• Custom model loading must include integrity verification to prevent tampered or malicious model injection.
• Video processing must not expose path traversal risks; only pre-validated file paths must be accepted.
• All new backends must run in the same resource limits as existing backends (memory < 50 MB per image during processing).

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