Peak signal-to-noise ratio

For broader coverage of this topic, see 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 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 logarithmic 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×n monochrome image I and its noisy approximation K, MSE is defined as

${\displaystyle {\mathit {MSE}}={\frac {1}{m\,n}}\sum _{i=0}^{m-1}\sum _{j=0}^{n-1}[I(i,j)-K(i,j)]^{2}.}$

The PSNR (in dB) is defined as

${\displaystyle {\begin{aligned}{\mathit {PSNR}}&=10\cdot \log _{10}\left({\frac {{\mathit {MAX}}_{I}^{2}}{\mathit {MSE}}}\right)\\&=20\cdot \log _{10}\left({\frac {{\mathit {MAX}}_{I}}{\sqrt {\mathit {MSE}}}}\right)\\&=20\cdot \log _{10}({\mathit {MAX}}_{I})-10\cdot \log _{10}({\mathit {MSE}}).\end{aligned}}}$

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.

Application in color images

For color images 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.^[1][2]

Quality estimation with PSNR

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.

Typical values for the PSNR in lossy image and video compression are between 30 and 50 dB, provided the bit depth is 8 bits, where higher is better. The processing quality of 12-bit images is considered high when the PSNR value is 60 dB or higher.^[3][4] For 16-bit data typical values for the PSNR are between 60 and 80 dB.^[5][6] Acceptable values for wireless transmission quality loss are considered to be about 20 dB to 25 dB.^[7][8]

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).^[9]

Original uncompressed image

Q=90, PSNR 45.53dB

Q=30, PSNR 36.81dB

Q=10, PSNR 31.45dB

Example luma PSNR values for a cjpeg compressed image at various quality levels.

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.^[10]

Generally, when it comes to estimating the quality of images and videos as perceived by humans, PSNR has been shown to perform very poorly compared to other quality metrics.^[10][11]

Variants

PSNR-HVS^[12] is an extension of PSNR that incorporates properties of the human visual system such as contrast perception.

PSNR-HVS-M improves on PSNR-HVS by additionally taking into account visual masking.^[13] In a 2007 study, it delivered better approximations of human visual quality judgements than PSNR and SSIM by large margin. It was also shown to have a distinct advantage over DCTune and PSNR-HVS.^[14]

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

1. Oriani, Emanuele. "qpsnr: A quick PSNR/SSIM analyzer for Linux". Retrieved 6 April 2011.
2. "pnmpsnr User Manual". Retrieved 6 April 2011.
3. Faragallah, Osama S.; El-Hoseny, Heba; El-Shafai, Walid; El-Rahman, Wael Abd; El-Sayed, Hala S.; El-Rabaie, El-Sayed M.; El-Samie, Fathi E. Abd; Geweid, Gamal G. N. (2021). "A Comprehensive Survey Analysis for Present Solutions of Medical Image Fusion and Future Directions". IEEE Access. 9: 11358–11371. Bibcode:2021IEEEA...911358F. doi:10.1109/ACCESS.2020.3048315. ISSN 2169-3536. 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.
4. Chervyakov, Nikolay; Lyakhov, Pavel; Nagornov, Nikolay (2020-02-11). "Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging". Applied Sciences. 10 (4): 1223. doi:10.3390/app10041223. ISSN 2076-3417. The image processing quality is considered high if PSNR value is greater than 60 dB for images with 12 bits per color.
5. Welstead, Stephen T. (1999). Fractal and wavelet image compression techniques. SPIE Publication. pp. 155–156. ISBN 978-0-8194-3503-3.
6. Raouf Hamzaoui, Dietmar Saupe (May 2006). Barni, Mauro (ed.). Fractal Image Compression. Vol. 968. CRC Press. pp. 168–169. ISBN 9780849335563. Retrieved 5 April 2011. {{cite book}}: |journal= ignored (help)
7. 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).
8. 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.
9. Salomon, David (2007). Data Compression: The Complete Reference (4 ed.). Springer. p. 281. ISBN 978-1846286025. Retrieved 26 July 2012.
10. Huynh-Thu, Q.; Ghanbari, M. (2008). "Scope of validity of PSNR in image/video quality assessment". Electronics Letters. 44 (13): 800. Bibcode:2008ElL....44..800H. doi:10.1049/el:20080522.
11. Huynh-Thu, Quan; Ghanbari, Mohammed (2012-01-01). "The accuracy of PSNR in predicting video quality for different video scenes and frame rates". Telecommunication Systems. 49 (1): 35–48. doi:10.1007/s11235-010-9351-x. ISSN 1018-4864. S2CID 43713764.
12. 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.
13. Ponomarenko, N.; Ieremeiev, O.; Lukin, V.; Egiazarian, K.; Carli, M. (February 2011). "Modified image visual quality metrics for contrast change and mean shift accounting". 2011 11th International Conference the Experience of Designing and Application of CAD Systems in Microelectronics (CADSM): 305–311.
14. Nikolay Ponomarenko; Flavia Silvestri; Karen Egiazarian; Marco Carli; Jaakko Astola; Vladimir Lukin, "On between-coefficient contrast masking of DCT basis functions" (PDF), CD-ROM Proceedings of the Third International Workshop on Video Processing and Quality Metrics for Consumer Electronics VPQM-07, 25.–26. Januar 2007 (in German), Scottsdale AZ