To optimize the context model in JPEG-XL for better utilization of the proposed gradient-based prediction methods, several strategies can be implemented:
Adaptive Context Selection: Enhance the context model to dynamically adjust the context based on the characteristics of the image being compressed. This adaptive approach can help in selecting the most suitable predictor for each pixel, including the gradient-based predictors like GAP and GED.
Feedback Loop Integration: Integrate the gradient-based prediction methods into the feedback loop of the context model. This integration would allow the context model to learn from the performance of the predictors and adjust the context selection accordingly for future predictions.
Context Modeling Enhancements: Improve the context modeling techniques used in JPEG-XL to better capture the relationships between neighboring pixels. By refining the context modeling process, the context model can provide more accurate information for the gradient-based predictors to make informed predictions.
Threshold Optimization: Fine-tune the threshold values used in the gradient-based predictors like GAP and GED. By optimizing these thresholds based on the image content and characteristics, the predictors can achieve better performance in predicting pixel values, leading to improved compression ratios.
Parallel Processing: Implement parallel processing techniques within the context model to enhance the efficiency of utilizing the gradient-based prediction methods. By leveraging parallelization, the context model can process multiple predictors simultaneously, optimizing the prediction process for faster and more effective compression.