Image Blurring via Curve Set Weight Functions

The patent segments the image processing into block-based operations, dividing the image into multiple blocks and processing each block independently. This allows the use of smaller local memory buffers for each block rather than requiring large line buffers for the entire image, thus reducing overall memory usage while maintaining edge preservation quality through local adaptive filtering.

The patent applies local quality by using adaptive filtering coefficients that are calculated based on local image characteristics (variance, gradient) within each block. This allows the filter to preserve edges locally where needed while using less memory globally, as each block is processed with its own optimized parameters rather than requiring extensive global memory storage.

The patent extracts only the essential local statistical features (mean, variance, gradient) from each block rather than storing complete block information. By taking out only the necessary statistical parameters needed for filtering calculations, the method maintains processing efficiency while dramatically reducing the memory required to store block information.

The patent changes the representation parameters from storing complete block pixel data to storing compressed statistical parameters (mean, variance, gradient metrics). This parameter transformation enables efficient processing through mathematical operations on compact representations while minimizing memory usage for block information storage.