The phrase 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.
The PSNR is most commonly used as a measure of 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 it is used as an approximation to human perception of reconstruction quality, therefore in some cases one reconstruction may appear to be closer to the original than another, even though it has a lower PSNR (a higher PSNR would normally indicate that the reconstruction is of higher quality). 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.[1][2]
It is most easily defined via the mean squared error (MSE) which for two m×n monochrome images I and K where one of the images is considered a noisy approximation of the other is defined as:
![{\displaystyle {\mathit {MSE}}={\frac {1}{m\,n}}\sum _{i=0}^{m-1}\sum _{j=0}^{n-1}[I(i,j)-K(i,j)]^{2}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/3a34719b4f391dba26b3e8e4460b7595d62eece4)
The PSNR is defined as:
Here, MAXI 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, MAXI is 2B−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.[3][4][5]
Typical values for the PSNR in lossy image and video compression are between 30 and 50 dB, where higher is better.[6][7] Acceptable values for wireless transmission quality loss are considered to be about 20 dB to 25 dB.[8][9]
When the two images are identical, the MSE will be zero. For this value the PSNR is undefined (see Division by zero).
See also
editReferences
edit- ^ Huynh-Thu, Q.; Ghanbari, M. (2008). "Scope of validity of PSNR in image/video quality assessment". Electronics Letters. 44 (13): 800. doi:10.1049/el:20080522.
- ^ MIT.edu
- ^ Oriani, Emanuele. "qpsnr: A quick PSNR/SSIM analyzer for Linux". Retrieved 6 April 2011.
- ^ "Image Processing Science calculating RMSE and PSNR for color images". Retrieved 6 April 2011.
- ^ "pnmpsnr User Manual". Retrieved 6 April 2011.
- ^ Welstead, Stephen T. (1999). Fractal and wavelet image compression techniques. SPIE Publication. pp. 155–156. ISBN 978-0819435033.
- ^ Barni, Mauro, ed. (May 2006). "Fractal Image Compression". Document and Image Compression. 968. CRC Press: 168–169. ISBN 9781420018837. ISSN 9780849335563. Retrieved 5 April 2011.
{{cite journal}}: Check|issn=value (help); Unknown parameter|coauthors=ignored (|author=suggested) (help)CS1 maint: date and year (link) - ^ Thomos, N., Boulgouris, N. V., & Strintzis, M. G. (2006, January). Optimized Transmission of JPEG2000 Streams Over Wireless Channels. IEEE Transactions on Image Processing , 15 (1).
- ^ Xiangjun, L., & Jianfei, C. Robust transmission of JPEG2000 encoded images over packet loss channels. ICME 2007 (pp. 947-950). School of Computer Engineering, Nanyang Technological University.
Kategorija:Računalniška grafika Kategorija:Obdelava signalov Category:Noise en:Peak_signal-to-noise_ratio