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Compute peak signal-to-noise ratio (PSNR) between images

The PSNR block computes the peak signal-to-noise ratio, in decibels, between two images. This ratio is often used as a quality measurement between the original and a compressed image. The higher the PSNR, the better the quality of the compressed, or reconstructed image.

The *Mean Square Error (MSE)* and the *Peak
Signal to Noise Ratio (PSNR)* are the two error metrics
used to compare image compression quality. The MSE represents the
cumulative squared error between the compressed and the original image,
whereas PSNR represents a measure of the peak error. The lower the
value of MSE, the lower the error.

To compute the PSNR, the block first calculates the mean-squared error using the following equation:

In the previous equation, *M* and *N* are
the number of rows and columns in the input images, respectively.
Then the block computes the PSNR using the following equation:

In the previous equation, *R* is the maximum
fluctuation in the input image data type. For example, if the input
image has a double-precision floating-point data type, then *R* is
1. If it has an 8-bit unsigned integer data type, *R* is
255, etc.

Different approaches exist for computing the PSNR of a color image. Because the human eye is most sensitive to luma information, compute the PSNR for color images by converting the image to a color space that separates the intensity (luma) channel, such as YCbCr. The Y (luma), in YCbCr represents a weighted average of R, G, and B. G is given the most weight, again because the human eye perceives it most easily. With this consideration, compute the PSNR only on the luma channel.

Port | Output | Supported Data Types | Complex Values Supported |
---|---|---|---|

I1 | Scalar, vector, or matrix of intensity values | Double-precision floating point Single-precision floating point Fixed point 8-, 16-, and 32-bit signed integer 8-, 16-, and 32-bit unsigned integer
| No |

I2 | Scalar, vector, or matrix of intensity values | Same as I1 port | No |

Output | Scalar value that represents the PSNR | Double-precision floating point
For fixed-point or integer input, the block output is double-precision floating point. Otherwise, the block input and output are the same data type. | No |

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