Open main menu
Home
Random
Recent changes
Special pages
Community portal
Preferences
About Wikipedia
Disclaimers
Incubator escapee wiki
Search
User menu
Talk
Dark mode
Contributions
Create account
Log in
Editing
Peak signal-to-noise ratio
Warning:
You are not logged in. Your IP address will be publicly visible if you make any edits. If you
log in
or
create an account
, your edits will be attributed to your username, along with other benefits.
Anti-spam check. Do
not
fill this in!
{{Short description|Metric used to measure signal quality}} {{Broader|Signal-to-noise ratio}} '''Peak signal-to-noise ratio''' ('''PSNR''') is an engineering term for the ratio between the maximum possible power of a [[Signal (information theory)|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 as a [[logarithm]]ic quantity using the [[decibel]] scale. PSNR is commonly used to quantify reconstruction quality for images and video subject to [[lossy compression]]. == Definition == PSNR is most easily defined via the [[mean squared error]] (''MSE''). Given a noise-free ''m''Γ''n'' monochrome image ''I'' and its noisy approximation ''K'', ''MSE'' is defined as : <math>\mathit{MSE} = \frac{1}{m\,n}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1} [I(i,j) - K(i,j)]^2.</math> The PSNR (in [[decibel|dB]]) is defined as : <math>\begin{align} \mathit{PSNR} &= 10 \cdot \log_{10} \left( \frac{\mathit{MAX}_I^2}{\mathit{MSE}} \right) \\ &= 20 \cdot \log_{10} \left( \frac{\mathit{MAX}_I}{\sqrt{\mathit{MSE}}} \right) \\ &= 20 \cdot \log_{10}(\mathit{MAX}_I) - 10 \cdot \log_{10} (\mathit{MSE}). \end{align}</math> Here, ''MAX<sub>I</sub>'' 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 [[Pulse-code modulation|PCM]] with ''B'' bits per sample, ''MAX<sub>I</sub>'' is 2<sup><var>B</var></sup> β 1. === Application in color images === For [[color image]]s with three [[RGB]] values per pixel, the definition of PSNR is the same except that the MSE is the sum over all squared value differences (now for each color, i.e. three times as many differences as in a monochrome image) 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 and HSV|HSL]].<ref>{{cite web|last=Oriani|first=Emanuele|title=qpsnr: A quick PSNR/SSIM analyzer for Linux|url=http://qpsnr.youlink.org/|access-date=6 April 2011}}</ref><ref>{{cite web|title=pnmpsnr User Manual|url=http://netpbm.sourceforge.net/doc/pnmpsnr.html|access-date=6 April 2011}}</ref> == Quality estimation with PSNR == PSNR is most commonly used to measure the quality of reconstruction of lossy compression [[codec]]s (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. Typical values for the PSNR in [[lossy compression|lossy]] image and video compression are between 30 and 50 dB, provided the bit depth is 8 [[bit]]s, where higher is better. The processing quality of 12-bit images is considered high when the PSNR value is 60 dB or higher.<ref>{{Cite journal|last1=Faragallah|first1=Osama S.|last2=El-Hoseny|first2=Heba|last3=El-Shafai|first3=Walid|last4=El-Rahman|first4=Wael Abd|last5=El-Sayed|first5=Hala S.|last6=El-Rabaie|first6=El-Sayed M.|last7=El-Samie|first7=Fathi E. Abd|last8=Geweid|first8=Gamal G. N.|date=2021|title=A Comprehensive Survey Analysis for Present Solutions of Medical Image Fusion and Future Directions|journal=IEEE Access|volume=9|pages=11358β11371|doi=10.1109/ACCESS.2020.3048315|issn=2169-3536|quote=This paper presents a survey study of medical imaging modalities and their characteristics. In addition, different medical image fusion approaches and their appropriate quality metrics are presented.|doi-access=free|bibcode=2021IEEEA...911358F }}</ref><ref>{{Cite journal|last1=Chervyakov|first1=Nikolay|last2=Lyakhov|first2=Pavel|last3=Nagornov|first3=Nikolay|date=2020-02-11|title=Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging|journal=Applied Sciences|language=en|volume=10|issue=4|pages=1223|doi=10.3390/app10041223|issn=2076-3417|quote=The image processing quality is considered high if PSNR value is greater than 60 dB for images with 12 bits per color.|doi-access=free}}</ref> For 16-bit data typical values for the PSNR are between 60 and 80 dB.<ref>{{cite book|last=Welstead|first=Stephen T.|title=Fractal and wavelet image compression techniques|year=1999|publisher=SPIE Publication|isbn=978-0-8194-3503-3|pages=155β156|url=https://books.google.com/books?id=evGyv-mogukC&q=image%20compression%20acceptable%20PSNR&pg=PA156}}</ref><ref>{{cite book|author=Raouf Hamzaoui, Dietmar Saupe|title=Fractal Image Compression|journal=Document and Image Compression|date=May 2006|volume=968|pages=168β169|url=https://books.google.com/books?id=FmqPOsEYRsEC&q=image%20compression%20acceptable%20PSNR&pg=PA168|access-date=5 April 2011|editor1-first=Mauro|editor1-last=Barni|publisher=CRC Press|isbn=9780849335563}}</ref> Acceptable values for wireless transmission quality loss are considered to be about 20 dB to 25 dB.<ref>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).</ref><ref>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]].</ref> The maximum PSNR for 8 bit is 48.131, for 10 bit is 60.198, for 12 bit is 72.245. 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 infinite (or undefined, see [[Division by zero]]).<ref>{{cite book|last=Salomon|first=David|title=Data Compression: The Complete Reference|year=2007|publisher=Springer|isbn=978-1846286025|url=https://books.google.com/books?id=ujnQogzx_2EC&q=PSNR%20infinite&pg=PA281|edition=4|access-date=26 July 2012|page=281}}</ref> {{multiple image | width = 200 | align = center | footer = Example [[Luma (video)|luma]] PSNR values for a [[libjpeg|cjpeg]] compressed image at various quality levels. | image1 = PSNR-example-base.png | caption1 = Original uncompressed image | image2 = PSNR-example-comp-90.jpg | caption2 = Q=90, PSNR 45.53dB | image3 = PSNR-example-comp-30.jpg | caption3 = Q=30, PSNR 36.81dB | image4 = PSNR-example-comp-10.jpg | caption4 = Q=10, PSNR 31.45dB }} == Performance comparison == Although a higher PSNR generally correlates with a higher quality reconstruction, in many 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.<ref name=":0">{{Cite journal|last1=Huynh-Thu|first1=Q.|last2=Ghanbari|first2=M.|year=2008|title=Scope of validity of PSNR in image/video quality assessment|journal=Electronics Letters|volume=44|issue=13|pages=800|doi=10.1049/el:20080522|bibcode=2008ElL....44..800H}}</ref> Generally, when it comes to estimating the [[Image quality|quality of images]] and [[Video quality|videos]] as perceived by humans, PSNR has been shown to perform very poorly compared to other quality metrics.<ref name=":0" /><ref>{{Cite journal|last1=Huynh-Thu|first1=Quan|last2=Ghanbari|first2=Mohammed|date=2012-01-01|title=The accuracy of PSNR in predicting video quality for different video scenes and frame rates|journal=Telecommunication Systems|language=en|volume=49|issue=1|pages=35β48|doi=10.1007/s11235-010-9351-x|s2cid=43713764|issn=1018-4864}}</ref> == Variants == PSNR-HVS<ref>Egiazarian, Karen, Jaakko Astola, Nikolay Ponomarenko, Vladimir Lukin, Federica Battisti, and Marco Carli (2006). "New full-reference quality metrics based on HVS." In Proceedings of the Second International Workshop on Video Processing and Quality Metrics, vol. 4.</ref> is an extension of PSNR that incorporates properties of the human visual system such as [[Contrast (vision)|contrast perception]]. PSNR-HVS-M improves on PSNR-HVS by additionally taking into account [[visual masking]].<ref>{{Cite journal|last1=Ponomarenko|first1=N.|last2=Ieremeiev|first2=O.|last3=Lukin|first3=V.|last4=Egiazarian|first4=K.|last5=Carli|first5=M.|date=February 2011|title=Modified image visual quality metrics for contrast change and mean shift accounting|url=https://ieeexplore.ieee.org/document/5744476|journal=2011 11th International Conference the Experience of Designing and Application of CAD Systems in Microelectronics (CADSM)|pages=305β311}}</ref> In a 2007 study, it delivered better approximations of human visual quality judgements than PSNR and [[structural similarity index measure|SSIM]] by large margin. It was also shown to have a distinct advantage over [[DCTune]] and PSNR-HVS.<ref name="PSNR-HVS-M">{{citation|surname1=Nikolay Ponomarenko|surname2= Flavia Silvestri|surname3= Karen Egiazarian|surname4= Marco Carli|surname5= Jaakko Astola|surname6= Vladimir Lukin|periodical=CD-ROM Proceedings of the Third International Workshop on Video Processing and Quality Metrics for Consumer Electronics VPQM-07, 25.β26. Januar 2007|title=On between-coefficient contrast masking of DCT basis functions |location=Scottsdale AZ|language=de|url=http://ponomarenko.info/vpqm07_p.pdf}}</ref> ==See also== * [[Czenakowski distance]] * [[Data compression ratio]] * [[Perceptual Evaluation of Video Quality]] (PEVQ) * [[Structural similarity index measure]] (SSIM) * [[Subjective video quality]] * [[Video Multimethod Assessment Fusion]] * [[Video quality]] ==References== {{Reflist}} {{Noise}} {{Machine learning evaluation metrics}} [[Category:Image compression]] [[Category:Noise (graphics)]] [[Category:Film and video technology]] [[Category:Digital television]] [[Category:Engineering ratios]]
Edit summary
(Briefly describe your changes)
By publishing changes, you agree to the
Terms of Use
, and you irrevocably agree to release your contribution under the
CC BY-SA 4.0 License
and the
GFDL
. You agree that a hyperlink or URL is sufficient attribution under the Creative Commons license.
Cancel
Editing help
(opens in new window)
Pages transcluded onto the current version of this page
(
help
)
:
Template:Broader
(
edit
)
Template:Citation
(
edit
)
Template:Cite book
(
edit
)
Template:Cite journal
(
edit
)
Template:Cite web
(
edit
)
Template:Machine learning evaluation metrics
(
edit
)
Template:Multiple image
(
edit
)
Template:Noise
(
edit
)
Template:Reflist
(
edit
)
Template:Short description
(
edit
)