Digital Image Evaluation Using Quantization Error for Anomaly Detection

The predetermined model is divided into two distinct parts: a first part that extracts features from the digital image, and a second part that performs semantic segmentation based on those features. This segmentation allows the quantization to be applied selectively to the feature representation, simplifying the overall evaluation process while maintaining manageable model complexity through modular architecture.

A quantization layer is introduced as an intermediary component between the feature extraction part and the semantic segmentation part. This quantization layer converts continuous feature values into discrete quantized values, providing a simple evaluation score while managing complexity by isolating the quantization operation as a separate, standardized component that can be independently optimized.

The features are extracted and quantized in advance before the actual anomaly detection process. By performing feature extraction and quantization as preliminary steps, the computationally intensive operations are completed beforehand, allowing the anomaly detection to proceed efficiently by simply comparing the pre-computed quantization error against a threshold, thus improving accuracy without excessive real-time computational burden.

The quantization error calculation is extracted as a separate, dedicated evaluation metric from the main semantic segmentation process. This extraction allows the anomaly detection to focus specifically on the quantization error value rather than reprocessing the entire segmentation pipeline, reducing redundant computations while maintaining high detection accuracy through targeted error measurement.