Divide an image into sections, called blocks or neighborhoods, to reduce the memory needed to process the image.

A sliding neighborhood operation is performed one pixel at a time using information about the pixel’s neighborhood.

Distinct block processing divides an image into nonoverlapping rectangular sections that can be processed individually.

Using larger block sizes reduces overall computation time but requires more memory to process each block.

Reshape sliding neighborhoods and distinct blocks to reduce the execution time of processing an image.

To work with image data in file formats not supported by block processing functions, construct a class that manages files based on region.

If you have a Parallel Computing Toolbox™ license, you can take advantage of multiple processor cores on your machine to improve the performance of blockproc.