A classification centric quantizer for efficient encoding of predictive feature errors

We design a joint compression and classification system that optimizes visual fidelity and classification accuracy under a bit rate constraint. We propose a classification centric quantizer (CCQ) whose parameters are learned from labeled training data. We apply and evaluate the CCQ on a scene classification problem and compare the results to previous work.