Adaptive Transform Coding for Semantic Compression
Outperforms neural methods while preserving interpretability and flexibility.
A new paper from researchers Andriy Enttsel and Vincent Corlay introduces an adaptive transform-coding method for semantic-feature compression, shifting the focus from human-centered reconstruction to machine-oriented representation coding. The approach is motivated by the conditional rate-distortion function of a Gaussian mixture model, using mode-dependent transforms and quantizers selected based on the inferred source component to handle heterogeneous feature distributions more efficiently. This enables better compression of semantic embeddings for downstream inference tasks, such as image classification or object detection.
Evaluated on features from popular vision backbones and foundation models, the method outperforms or is competitive with state-of-the-art neural compression techniques while preserving flexibility and interpretability. The technique offers a balance of efficiency and transparency, making it suitable for real-world applications where both performance and understanding are critical, such as autonomous systems or edge computing.
- Uses mode-dependent transforms and quantizers from a Gaussian mixture model for efficient coding
- Outperforms or matches state-of-the-art neural compression on vision backbone features
- Preserves interpretability and flexibility unlike black-box neural methods
Why It Matters
Enables more efficient machine vision systems with transparent compression, crucial for edge AI and autonomous applications.