ML Dynamic Composer for SDR to HDR Conversion

The patent replaces manual color grading (mechanical human operation) with machine learning-based automatic composition algorithms. The system uses trained ML models to predict operational parameter values for backward reshaping mappings, automatically converting SDR content to HDR while preserving artistic intent and visual qualities without requiring manual intervention.

The system enables self-service by allowing the ML-based composition algorithm to automatically perform the color grading task that would otherwise require manual expert intervention. The trained models independently analyze SDR content and generate appropriate HDR transformations based on learned patterns from training image pairs, making the process autonomous and efficient.

The patent substitutes expensive manual color grading processes with automated machine learning algorithms. The ML models are trained on diverse image pairs to learn how to preserve artistic intent and visual qualities during SDR to HDR transitions, eliminating the need for costly manual expert intervention while maintaining high quality results.

The system performs preliminary action by pre-training machine learning models on extensive datasets of image pairs before deployment. This offline training phase captures artistic intent and visual quality patterns, enabling the models to automatically preserve these qualities during actual SDR to HDR conversions without requiring expensive manual review and adjustment for each content piece.