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Imatest Documentation Noise in photographic images Introduction Noise is a random variation of image density, visible as grain in film and pixel level variations in digital images. It is a key image quality factor; nearly as important as sharpness. Since it arises from basic physics— the photon nature of light and the thermal energy of heat— it will always be there. The good news is noise can be extremely low— often impreceptably low— in digital cameras, particularly DSLRs with large pixels (5 microns square or larger). But noise can get ugly in compact digital cameras with tiny pixels, especially at high ISO speeds. In most cases noise is perceived as a degradation in quality. But some Black & White photographers like its graphic effect: Many favor 35mm 35mm Tri-X film. The pointillist painters, most notably George Seurat, created "noise" (specks of color) by hand; a task that can be accomplished in seconds today with Photoshop plugins. But by and large, the majority of photographers, especially color and large-format photographers, dislike noise with good reason. Noise is measured by several Imatest modules. Stepchart produces the most detailed results, but noise is also measured in Colorcheck, SFR, and Light Falloff. Appearance The appearance of noise is illustrated in the stepchart images on the right. Noise is usually measured as an RMS (root mean square) voltage. The mathematics of noise is presented in a green box at the bottom of this page. The stepcharts in columns (A)-(C) are simulated. They are assumed to have a minimum density of 0.05 and density steps of 0.1, identical to the Kodak Q-13 and Q-14. They have been encoded with gamma = 1/2.2 for optimum viewing at gamma = 2.2 (the Windows/Internet standard). Strong noise— more than you'd find in most digital cameras— has been added to columns (A) and (B). Column (C) is noiseless. The fourth column (D) contains an actual Q-13 stepchart image taken with the Canon EOS-10D at ISO 1600: a very ISO high speed. Noise is visible, but admirably low for such a high ISO speed (thanks, no doubt, to software noise reduction). The noise in (A) is constant inside the sensor, i.e., before gamma encoding. When it is encoded with gamma = 1/2.2, contrast, and hence noise, is boosted in dark areas and reduced in light areas. The Kodak publication, CCD Image Sensor Noise Sources, indicates that this is not a realistic case. Sensor noise tends to increase with brightness. The noise in (B) is uniform in the image file, i.e., its value measured in pixels is constant. This noise must therefore increase with brightness inside the sensor (prior to gamma encoding), and hence is closer to real sensor behaviour than (A). Noise appears relatively constant except for the darkest zones, where it's not clearly visible. Noise is often lower in the lightest zones, where a tonal response "S" curve superimposed on the gamma curve (or saturation) reduces contrast, hence noise. For this reason the middle zones— where noise is most visible— are used to calculate the average noise: a single number used to characterize overall noise performance. We omit zones where the density of the original chart (hence display density in an unmanipulated image) is greater than 1.5 or less than 0.1. Noise measurements Noise measurements should ideally (A) Const. sensor noise (B) Const. pixel noise (C) No noise (D) Canon EOS-10D ISO 1600 18 of 451
Imatest Documentation correlate with perceived appearance, (in many cases) be referenced to the original scene so the measurement is not affected by the tonal response (contrast) of the camera or RAW converter, be detailed enough to allow accurate assessment of sensor and camera performance, and be simple enough to interpret without difficulty. Since noise is only meaningful in relationship to a signal, a Signal-to Noise Ratio (SNR or S/N) is often calculated. SNR can be defined in many ways, depending on how Signal S is defined. For example, S can be an individual patch pixel level or the pixel difference corresponding to a specified scene density range (1.45 or 1.5 are often used for this purpose). It is important to know precisely how SNR is defined whenever it is discussed. SNR can be expresed as a simple ratio (S/N) or in decibels (dB), where SNR (dB) = 20 log10(S/N). Doubling S/N corresponds to increasing SNR (dB) by 6.02 dB. Since these requirements can be somewhat contradictory, Imatest modules have several noise and SNR measurements, some simple and some detailed. Available displays in Stepchart and Colorcheck include: Noise a function of pixel level or exposure, expressed in Pixels, normalized to the pixel difference corresponding to a scene density range of 1.5 for Stepchart (comparable to the density range of 1.45 for the GretagMacbeth ColorChecker) or the White - Black zones in the ColorChecker (row 3, patches 1 - 6; density range = 1.45). Without this normalization, noise is a function of RAW converter response curve; it increases with converter contrast. Pixels, normalized to the maximum pixel value: 255 for 8/24-bit files. Pixels (maximum of 255 for 8/24-bit files) f-stops (or EV or zones; a factor of two in exposure), i.e., referenced to the original scene. Noise measured in f-stops corresponds closely to human vision. See f-stop noise, below. Noise as a simple (average) number A good single number for describing noise is the average noise of the Y (Luminance) channel, measured in pixels (normalized scene density difference of 1.5), shown in the lower plot in the figures, below. The lightest and darkest zones, representing chart densities greater than 1.5 or less than 0.1 are excluded from the average. S/N or SNR (dB) as a function of pixel level or exposure, where S is the pixel level of the individual test chart patch. S/N or SNR (dB) as a simple (average) number. A good single number for defining SNR is the White - Black SNR, SNR_BW, where S is the difference in pixel levels between the White and Black patches in the GretagMacbeth ColorChecker in Colorcheck (density range of 1.45), or the pixel difference corresponding to a chart (scene) density difference of 1.5 in Stepchart. Noise N is taken from a middle level patch with density around 0.7. SNRBW = 20 log10(( SWHITE – SBLACK ) / Nmid ) a noise spectrum plot, described below. In addition, Light Falloff displays a spatial map of noise. Characteristic Stepchart results are shown below. Column (B): Uniform pixel noise 19 of 451
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