PSNR

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

  • Library:
  • Computer Vision Toolbox / Statistics

Description

The PSNR block computes the peak signal-to-noise ratio, in decibels, between two images. This ratio is 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 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:

MSE=M,N[I1(m,n)I2(m,n)]2M*N

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

PSNR=10log10(R2MSE)

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.

Computing PSNR for Color Images

Different approaches exist for computing the PSNR of a color image. Because the human eye is most sensitive to luma information, you can 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. Compute the PSNR only on the luma channel.

Ports

Input

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Input image, specified as scalar, vector, or matrix.

Data Types: single | double | int8 | int16 | int32 | uint8 | uint16 | uint32 | Boolean | fixed point

Input image, specified as scalar, vector, or matrix.

Data Types: single | double | int8 | int16 | int32 | uint8 | uint16 | uint32 | Boolean | fixed point

Output

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Peak signal-to-noise ratio between images, returned as a scalar.

Dependencies

If the input is a fixed-point or integer data type, the block output is double-precision floating point. Otherwise, the block input and output are the same data type.

Data Types: double

Block Characteristics

Data Types

double | fixed point | integer | single

Multidimensional Signals

no

Variable-Size Signals

yes

Extended Capabilities

Fixed-Point Conversion
Design and simulate fixed-point systems using Fixed-Point Designer™.

See Also

Introduced before R2006a