Marginal revenue

Peak signal-to-noise ratio, often abbreviated PSNR, is an engineering term for the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Because many signals have a very wide dynamic range, PSNR is usually expressed in terms of the logarithmic decibel scale.

PSNR is most commonly used to measure the quality of reconstruction of lossy compression codecs (e.g., for image compression). The signal in this case is the original data, and the noise is the error introduced by compression. When comparing compression codecs, PSNR is an approximation to human perception of reconstruction quality. Although a higher PSNR generally indicates that the reconstruction is of higher quality, in some cases it may not. One has to be extremely careful with the range of validity of this metric; it is only conclusively valid when it is used to compare results from the same codec (or codec type) and same content.

PSNR is most easily defined via the mean squared error (MSE). Given a noise-free m×n monochrome image I and its noisy approximation K, MSE is defined as:

$\mathit{M}\mathit{S}\mathit{E}=\frac{1}{mn}\sum _{i=0}^{m-1}\sum _{j=0}^{n-1}[I(i,j)-K(i,j){]}^2$

The PSNR is defined as:

$\begin{array}{rl}\mathit{P}\mathit{S}\mathit{N}\mathit{R}& =10\cdot {\log }_{10}\left(\frac{{\mathit{M}\mathit{A}\mathit{X}}_I^2}{\mathit{M}\mathit{S}\mathit{E}}\right)\\ & =20\cdot {\log }_{10}\left(\frac{{\mathit{M}\mathit{A}\mathit{X}}_I}{\sqrt{\mathit{M}\mathit{S}\mathit{E}}}\right)\\ & =20\cdot {\log }_{10}\left({\mathit{M}\mathit{A}\mathit{X}}_I\right)-10\cdot {\log }_{10}\left(\mathit{M}\mathit{S}\mathit{E}\right)\end{array}$

Here, MAX_I is the maximum possible pixel value of the image. When the pixels are represented using 8 bits per sample, this is 255. More generally, when samples are represented using linear PCM with B bits per sample, MAX_I is 2^B−1. For color images with three RGB values per pixel, the definition of PSNR is the same except the MSE is the sum over all squared value differences divided by image size and by three. Alternately, for color images the image is converted to a different color space and PSNR is reported against each channel of that color space, e.g., YCbCr or HSL.

Typical values for the PSNR in lossy image and video compression are between 30 and 50 dB, provided the bit depth is 8 Bit, where higher is better. For 16 Bit data typical values for the PSNR are between 60 and 80 dB. Acceptable values for wireless transmission quality loss are considered to be about 20 dB to 25 dB.

In the absence of noise, the two images I and K are identical, and thus the MSE is zero. In this case the PSNR is undefined (see Division by zero).

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See also

• Data compression ratio
• Mean square error
• Perceptual Evaluation of Video Quality (PEVQ)
• Signal-to-noise ratio
• Structural similarity (SSIM) index
• Subjective video quality
• Video quality

References

43 year old Petroleum Engineer Harry from Deep River, usually spends time with hobbies and interests like renting movies, property developers in singapore new condominium and vehicle racing. Constantly enjoys going to destinations like Camino Real de Tierra Adentro.