Multi-View Video Coding Block Type Determination

The patent segments the video picture into multiple blocks (e.g., 8x8, 16x16 macro blocks) and processes each block independently with different coding parameters. This allows flexible adaptation to different temporal and inter-view scalability requirements for different regions of the video, resolving the contradiction between basic compression and advanced scalability handling.

The patent introduces dynamic picture types (I-picture, P-picture, B-picture) and temporal scalability parameters that can be adjusted based on the specific multi-view video content and requirements. This dynamic adaptation enables the system to optimize coding efficiency while handling temporal and inter-view scalability as needed.

The patent applies different motion vector determination methods to different blocks based on their characteristics. For example, inter-view prediction is applied to blocks where it improves accuracy, while skip modes are used for blocks where motion is minimal or predictable. This local adaptation maintains prediction accuracy while reducing overall coding complexity.

The patent extracts and codes only the necessary motion vector information for blocks that require it, rather than processing all blocks uniformly. By identifying which blocks need full motion vector analysis and which can use simplified methods or skip modes, the system reduces processing complexity while maintaining accuracy where needed.

The patent codes additional information (such as chroma prediction modes, advanced inter-view prediction parameters) selectively for blocks where it provides the most benefit. By assessing the importance of each block and applying enhanced coding only where necessary, the system improves prediction quality for critical regions while minimizing overall coding overhead.

The patent applies partial coding of additional information rather than complete coding for all blocks. By using skip modes for blocks where additional information would not significantly improve prediction quality, the system achieves acceptable prediction accuracy with reduced coding overhead, avoiding the excessive action of coding everything at full detail.