The proposed pattern clustering mechanism in the Offset-Aware Partition Transformer (OAPT) can be extended to handle multi-stage JPEG compression by generalizing the clustering approach to accommodate more than two compression stages. This can be achieved by analyzing the compression artifacts generated at each stage and identifying the unique patterns that emerge from multiple compressions.
Pattern Analysis: For each additional compression stage, the mechanism would need to analyze the pixel-level offsets and the resulting patterns within the 8 × 8 blocks. By extending the clustering algorithm to account for more than four patterns (as seen in double compression), the model can adapt to the increased complexity of multi-stage compression.
Adaptive Clustering: The clustering algorithm can be modified to dynamically adjust based on the number of compression stages. This could involve using a hierarchical clustering approach that groups similar patterns across all stages, allowing the model to learn from the cumulative effects of multiple compressions.
Enhanced Offset Prediction: The compression offset predictor could be expanded to estimate offsets for each compression stage, providing a more comprehensive understanding of how each stage affects the image quality. This would involve training the predictor on datasets that simulate multi-stage JPEG compression scenarios.
Integration with Hybrid Attention: The hybrid attention mechanism can be adapted to incorporate the additional patterns identified from multi-stage compression. By allowing the attention blocks to focus on these diverse patterns, the model can effectively restore images that have undergone multiple compressions, improving the overall restoration quality.