Image Norms
Statistical Operations

Primitives for computing the norms of an image, the norms of difference, and the relative errors of two images. More...

## Modules

| Norm_Inf
| Primitives for computing the infinity norm of an image.
| Norm_L1
| Primitives for computing the L1 norm of an image.
| Norm_L2
| Primitives for computing the L2 norm of an image.
| NormDiff_Inf
| Primitives for computing the infinity norm of difference of pixels between two images.
| NormDiff_L1
| Primitives for computing the L1 norm of difference of pixels between two images.
| NormDiff_L2
| Primitives for computing the L2 norm of difference of pixels between two images.
| NormRel_Inf
| Primitives for computing the relative error of infinity norm between two images.
| NormRel_L1
| Primitives for computing the relative error of L1 norm between two images.
| NormRel_L2
| Primitives for computing the relative error of L2 norm between two images.

## Detailed Description

Primitives for computing the norms of an image, the norms of difference, and the relative errors of two images.

Given an image with width and height ,

1. The infinity norm (Norm_Inf) is defined as the largest absolute pixel value of the image.
2. The L1 norm (Norm_L1) is defined as the sum of the absolute pixel value of the image, i.e.,

   .
3. The L2 norm (Norm_L2) is defined as the square root of the sum of the squared absolute pixel value of the image, i.e.,

   .

Given two images and both with width and height ,

1. The infinity norm of differece (NormDiff_Inf) is defined as the largest absolute difference between pixels of two images.
2. The L1 norm of differece (NormDiff_L1) is defined as the sum of the absolute difference between pixels of two images, i.e.,

   .
3. The L2 norm of differece (NormDiff_L2) is defined as the squared root of the sum of the squared absolute difference between pixels of two images, i.e.,

   .

Given two images and both with width and height ,

1. The relative error for the infinity norm of differece (NormRel_Inf) is defined as NormDiff_Inf divided by the infinity norm of the second image, i.e.,

2. The relative error for the L1 norm of differece (NormRel_L1) is defined as NormDiff_L1 divided by the L1 norm of the second image, i.e.,

3. The relative error for the L2 norm of differece (NormRel_L2) is defined as NormDiff_L2 divided by the L2 norm of the second image, i.e.,

The norm functions require the addition device scratch buffer for the computations.

### Common parameters for nppiNorm functions include:

Parameters

  pSrc1 | Source-Image Pointer.
  nSrc1Step | Source-Image Line Step.
  pSrc2 | Source-Image Pointer.
  nSrc2Step | Source-Image Line Step.
  oSizeROI | Region-of-Interest (ROI).
  pMask | Mask-Image Pointer.
  nMaskStep | Mask-Image Line Step.
  nCOI | Channel_of_Interest Number.
  pNorm | Pointer to the norm value.
  pNormDiff | Pointer to the computed norm of differences.
  pNormRel | Pointer to the computed relative error for the infinity norm of two images.
  pDeviceBuffer | Pointer to the required device memory allocation, Scratch Buffer and Host Pointer. Use nppiNormInfGetBufferHostSize_XX_XXX to compute the required size (in bytes).

Returns
  Image Data Related Error Codes, ROI Related Error Codes, or NPP_COI_ERROR if an invalid channel of interest is specified, or NPP_NOT_EVEN_STEP_ERROR if an invalid floating-point image is specified.

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