Sparse Coding Dictionary Priming for Video Encoding

The patent applies preliminary action by performing an initialization phase before the main dictionary training process. This initialization step prepares the dictionary with a set of codewords derived from training data, ensuring that subsequent training iterations start from a favorable state rather than random initialization, thereby reducing sensitivity to initialization and improving codeword selection accuracy.

The patent implements feedback mechanisms where the training process continuously evaluates the performance of selected codewords and adjusts the dictionary accordingly. The training iterations use feedback from reconstruction errors and codeword selection statistics to refine the dictionary, ensuring that codewords are selected based on actual performance rather than initial random assignments.

The patent applies dynamics by making the sampling index cardinality a dynamic parameter that changes during training iterations. The cardinality is adjusted based on training progress and performance metrics, allowing the system to start with lower complexity and progressively increase accuracy requirements, thereby balancing computational complexity with representation accuracy.

The patent uses partial action by selecting a subset of codewords (with cardinality less than the total dictionary size) for each training iteration. This partial selection approach reduces computational complexity compared to using all codewords, while still achieving good representation accuracy through iterative refinement of the selected subset.