Signaled Adaptive Loop Filter Classifier Selection

The patent divides the video content into different segments or blocks and applies different classifiers to different segments based on their characteristics. Specifically, the video picture is divided into multiple video blocks, and for each block, the encoder determines whether to use a Laplacian classifier or a band-based classifier depending on the block's content characteristics, thereby achieving segmented classification that improves filter selection accuracy without requiring all classifiers to be applied uniformly across the entire video.

The patent implements a dynamic classifier selection mechanism where the choice of classifier is not fixed but adapts based on the characteristics of each video block. The encoder dynamically determines which classifier to use for each block by analyzing content features, and this decision is signaled to the decoder through syntax elements in the bitstream. This dynamic adaptation allows the system to optimize filter selection for each block while maintaining computational efficiency through selective application.

The video stream is segmented into multiple blocks, and classifier selection is performed on a block-by-block basis rather than globally. This segmentation allows the system to apply complex multiple-classifier logic only where needed (when block characteristics indicate benefit) while using simpler single-classifier approaches for other blocks, thereby maintaining high decoded video quality for critical regions while preserving overall decoding throughput.

Different classifier strategies are applied to different local regions (blocks) of the video based on their specific characteristics. Blocks with characteristics that benefit from multiple classifiers receive enhanced processing, while other blocks use standard single-classifier processing. This local quality approach ensures high video quality where it matters most without uniformly increasing complexity across the entire video stream.

The system dynamically adapts its computational resources by selecting classifiers based on the actual content characteristics of each video block. Rather than always using multiple classifiers (which would maximize adaptability but also maximize energy consumption), the system activates additional classifiers only when content analysis indicates they will provide benefit, thereby achieving high adaptability where needed while conserving computational energy in other regions.

The system changes operational parameters (classifier selection) based on detected video content characteristics. By analyzing block-level features and adjusting the number and type of classifiers applied accordingly, the system achieves high adaptability to different content types while avoiding the constant high computational overhead that would result from always using the most complex classifier configuration.