Image Warping With Staged Decimation and Interpolation

The patent divides the image warping process into two separate stages: decimation (subsampling) and interpolation (upsampling). Each stage is handled by dedicated circuitry operating at different clock rates. The decimator operates at a lower clock rate after reducing sample density, while the interpolator operates at a higher clock rate to reconstruct the final image. This segmentation eliminates the need for large memory buffers required by concurrent operations.

The decimation operation is performed first as a preliminary step before interpolation. By subsampling the image first and generating a decimated image, the system reduces the data volume that needs to be processed and stored. This preliminary action allows subsequent interpolation to work with fewer data points, reducing memory requirements while maintaining processing efficiency.

The patent segments the processing pipeline into distinct decimation and interpolation phases with dedicated hardware for each. The decimator circuitry processes input images at a reduced clock rate, then the interpolator circuitry processes the decimated images at a higher clock rate. This segmentation allows each stage to be optimized independently, reducing overall latency while maintaining throughput.

The system dynamically adjusts operating clock rates for different processing stages. The decimator operates at a lower clock rate appropriate for its reduced data volume, while the interpolator operates at a higher clock rate to meet throughput requirements. This dynamic rate adjustment optimizes both latency and productivity by matching processing speed to data volume at each stage.

The patent applies segmentation to handle extreme scaling factors by separating magnification and minification operations into distinct processing paths. The decimator handles minification by subsampling, while the interpolator handles magnification by upsampling. This segmentation allows each operation to be optimized for its specific function, reducing computational cost compared to applying general-purpose warping algorithms to all scaling scenarios.

The system applies different processing quality and complexity to different regions and operations. For minification, simple decimation is sufficient and computationally efficient. For magnification, interpolation is applied to the decimated image. This local quality approach ensures adequate image quality for each operation type while minimizing computational cost, particularly for extreme scaling factors where one operation dominates.