Non-linear Video Sampling for 3D Depth Conversion

The patent applies different sampling densities to different regions of the video frame based on saliency detection. High-saliency regions (containing important objects) use higher sampling density to preserve shape accuracy, while low-saliency regions use lower sampling density to reduce computational complexity. This local differentiation resolves the contradiction by optimizing precision where needed without uniformly increasing complexity throughout the entire image.

The sampling density is dynamically adjusted based on detected saliency regions and depth information. The system adapts the sampling strategy in real-time during video conversion, switching between high and low sampling density regions dynamically. This dynamic adaptation allows the system to maintain object shape accuracy while controlling overall processing complexity through intelligent resource allocation.

The system identifies saliency regions and applies higher sampling density specifically to those areas where 3D information is most relevant for enhancing viewing experience. Background or less important regions use lower sampling density, maintaining processing efficiency. This localized approach enables effective 3D information utilization without uniformly sacrificing productivity across the entire frame.

The video frame is segmented into multiple regions based on saliency detection, with each region processed using appropriate sampling density. This segmentation allows the system to efficiently convert only the most important regions with high sampling density while processing less important regions with lower density, thus maintaining overall productivity while enhancing 3D viewing experience.

High sampling density is applied locally only to saliency regions containing important objects, while low-saliency regions use reduced sampling density. This selective high-precision sampling minimizes object shape distortion in critical areas without the computational overhead of uniformly high-density sampling across the entire frame, thus reducing overall processing time.

Instead of applying maximum sampling density to the entire frame (excessive action), the system applies high sampling density only partially to regions where it is most needed (saliency regions). This partial action approach achieves sufficient object shape accuracy in important areas while avoiding unnecessary computational resources and time consumption in less critical regions.