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Digital versus film imaging The issue is channel capacity

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  1. Digital versus film imagingThe issue is channel capacity Norman Koren Imatest Boulder, Colorado www.imatest com • Film and digital cameras • Image quality and Shannon information capacity • Sharpness and tricks for enhancing it • Noise and tricks for reducing it • Approximate Shannon capacity as estimate of image quality • The Imatest program for measuring image quality— will enable educated consumers to test the quality of their digital cameras and lenses. Will there be a market? • Results for different cameras (data on film is incomplete.) This is a work in progress. There are still more questions than answers. Digital vs film imaging & Channel capacity Norman Koren

  2. Film and digital cameras • Film cameras: my old 35mm SLR (Single-lens reflex) is shown. • Slide film: Low grain but low exposure (dynamic) range (~5 f-stops). • Negative film: High exposure range (~9 f-stops but more grain. • Compact digital cameras: Small, lightweight. Up to 8 megapixels (3-5 typical). Non-interchangeable lenses, mostly zooms. Small sensors (11 mm diag.) & pixel sites have a limited dynamic range and high noise at high ISO speeds. • Digital SLRs: Size and weight comparable to film SLRs. 5-14 megapixels (6 typical). Interchangeable lenses. Large sensors (22 mm diag.) have low noise. Limited exposure range (~6 f-stops) with standard 8-bit depth JPEG conversion, but can have high exposure range when RAW conversion takes advantage of the 12+ bit A-to-D converters on the sensors. Digital vs film imaging & Channel capacity Norman Koren

  3. Three factors driving the evolution from film to digital photography • Cost:Higher initially than film cameras; buyers switch when they perceive a savings in their individual timeframes. • Convenience:Ease of obtaining the end result, which is changing as workflows for obtaining digital prints evolve and photographers move from prints and slides to electronic display. • Quality:How do the two media compare? Quality-conscious photographers will only switch when digital quality is perceived as comparable to film (various formats). Misconceptions persist. The focus of this talk. Bottom line: 300 million digital cameras are projected to be sold in the next five years. Digital vs film imaging & Channel capacity Norman Koren

  4. Factors affecting image quality Important factors, BUT, these can be adjusted in the camera or during post-processing, providing the exposure has captured the essential information. • Color (hue, saturation) • Contrast • Brightness • Sharpness • Noise (grain) Intrinsic factors, related to the information captured by the camera. If the camera doesn’t capture them, they won’t be there (though you can fake it to a limited degree with sharpening and noise reduction). Digital vs film imaging & Channel capacity Norman Koren

  5. Film (negative) Kodacolor 100 Canon 35mm SLR 20mm f/2.8 4000 dpi scan 5662x3744 pixels Sharp, but grainy Film vs. digitalobservations Digital: Canon EOS-10D (6 mpxl) 17-35mm f/4L 3072x2048 pxls . Nearly as sharp, little grain Digital vs film imaging & Channel capacity Norman Koren

  6. Noise, sharpness, and Shannon capacity • A camera’s intrinsic image quality is a function of both sharpness and noise (or its equivalent in film, grain). Can one devise a “figure of merit” that includes both? • Hypothesis: Image quality is proportional to the Shannon information information capacity C, which is a function of bandwidth W, noise N, and signal S. • C = W log2(1 + S/N) • Some of the complexities are discussed in the following slides. These include • Measuring noise N and bandwidth W. • How to define signal S. • The effect of signal processing on N, W, and the estimate of Shannon capacity, C. Looks simple, but it’s difficult to calculate: the devil is in the details; the calculation must be approximate (for now)— that’s why Shannon capacity isn’t widely used. Yet. Digital vs film imaging & Channel capacity Norman Koren

  7. Sharpness A property of a lens, film, sensor, or system defined by boundaries between zones Bar pattern: Sharpness defined by 10-90% risetime. Patterns of increasing spatial frequency (Log scale) Sine pattern: Sharpness defined by contrast at a given spatial frequency. The top half of each pattern is sharp; the bottom is less sharp. How is sharpness measured? Digital vs film imaging & Channel capacity Norman Koren

  8. MTF50 Spatial frequency LP/mm Sharpness and spatial..frequency response Sine and bar patterns are shown with and without rolloff for a high quality 35mm lens (on a 0.5 mm virtual target) Amplitude response of bar pattern Rise distance (10-90%) difficult to calculate for compound systems. The relative contrast of a sine pattern (pure tone) is called Spatial Frequency Response (SFR)or Modulation Transfer Function (MTF); Multiplicative for compound systems. Perceived image sharpness strongly correlates with MTF50, the spatial frequency where contrast is half its low frequency value. MTF50 is a close approximation to bandwidth W in Shannon capacity calculations. Digital vs film imaging & Channel capacity Norman Koren

  9. Imatest Sharpness calculationderived from ISO-12233 standard Results are derived from a slanted-edge image in a test chart. Algorithm: Find average edge location. Put each line into one of four “bins” based on avg. edge. Find mean 4x oversampled edge, then take Fourier transform of the spatial derivative. Upper (black) curve is the average edge. Lower (black) curve is the Spatial Frequency Response (SFR or MTF). These results are strongly affected by sharpening, The dashed red curves are the edge and MTF response with standardized sharpening that corrects for oversharpening. Digital vs film imaging & Channel capacity Norman Koren

  10. Sharpening • Enhances the perceived sharpness. • Present in virtually all digital images because most look soft without it. Can be applied in the camera, RAW converter, and/or image editor. • Subtracts a fraction of neighboring pixels from each pixel. (Radius = 2 shown.) • Boosts contrast at high spatial frequencies (boosts MTF50). Amount depends on sharpening fraction and radius. • Different amounts of sharpening in • different cameras makes comparisons challenging. • Sharpening enhances edges, but oversharpening creates “halos” at edges. Can look artificial. • Transfer function: MTFsharp( f ) = (1-ksharp*cos(2*pi*f*V ))/(1-ksharp) • where V = Sharpening radius / pixel spacing Digital vs film imaging & Channel capacity Norman Koren

  11. Standardized sharpening I • Different degrees of sharpening makes comparisons between cameras challenging. • Oversharpened images have “halos” at edges. Can look artificial (on right). Common in compact digital cameras. • Undersharpened images do not appear as sharp as they could be. • Standardized sharpening is a strategy to deal with the differences in sharpening. • Sharpen (or de-sharpen) the image with a fixed radius (usually R = 2; the value used in most compact digital cameras) so the MTF response at f = 0.3*Nyquist (0.15*cycles/pixel) is equal to MTF at f = 0. • The response with standardized sharpening to the strongly oversharpened image on the right is shown by the dashed red (– – –) curves. Digital vs film imaging & Channel capacity Norman Koren

  12. Standardized sharpening II • This image, from an excellent 11 megapixel DSLR, is undersharpened. • MTF50 without standardized sharpening is 0.264 cycles/pixel = 1426 LW/PH, not as good as the 5 megapixel compact digital camera on the previous page: 1459 LW/PH. • But with standardized sharpening the numbers are 1872 LW/PH and 1357 LW/PH: closer to expectations. (Both cameras have excellent optics.) • MTF50 with standardized sharpening is a good approximation for bandwidth W in the Shannon capacity equation. Digital vs film imaging & Channel capacity Norman Koren

  13. 1 2 3 4 Noise N (grain in film) 1. No noise 2. 5% added Gaussian noise (pixels enlarged) 3. 12% added Gaussian noise 4.3 with noise-reduction signal processing RMS noise is the standard deviation () of the pixel levels in a smooth area. Noise increases with increasing ISO speed for both digital and film– many mechanisms are involved. Digital cameras increase ISO speed by amplifying the signal – and the noise is amplified along with it. The visibility of noise depends on its amplitude, spectral distribution, and on the magnification of the image. Middle spatial frequencies are typically the most visible. Digital vs film imaging & Channel capacity Norman Koren

  14. 3 4 Noise reduction (cheatin’) 3. 12% added Gaussian noise 4. With noise-reduction signal processing Noise degrades image quality, but visible noise can be reduced by means of signal processing, AKA, CHEATIN’. Zone 4 has been smoothed (i.e., blurred or lowpass filtered) in low contrast regions (where adjacent pixels are beneath a threshold) and sharpened near the edges (where adjacent pixels are above a threshold). Some noise is still visible at low spatial frequencies, where the eye is highly sensitive. A rapid rolloff of the measured noise spectrum is strong evidence of noise reduction. The response on the right is unusual. Typical unfiltered response tends to be near white (with a slow rolloff). Digital vs film imaging & Channel capacity Norman Koren

  15. Noise reduction example Canon EOS-10D; ISO 200(a high quality digital SLR) Low frequency noise artifacts are visible in the sky, which has been enhanced (darkened and boosted in contrast) for aesthetic purposes. This is clear evidence of noise reduction– in a camera has an excellent reputation for low noise. The CMOS sensor evidently requires noise reduction. Digital vs film imaging & Channel capacity Norman Koren

  16. More on noise reduction Although noise reduction smoothing often improves perceived image quality, lost low-contrast detail can result in a “plasticy” appearance in lightly textured areas, like skin. Digital plastic surgery. Extreme noise reduction is revealed by the rapid rolloff in the noise spectrum plot, shown on the right for a 14 megapixel DSLR with a CMOS sensor. Why cheatin’?Because information capacity C = W log2(1 + S/N) derived from a simple targets appears to be increased even though actual information (low contrast detail) is lost.Better targets are needed. The contrast threshold used for noise reduction reduces the actual Shannon capacity C by the roughly the same amount as the equivalent amount of noise N. Digital vs film imaging & Channel capacity Norman Koren

  17. Signal S: the third parameter in Shannon Capacity • The choice of signal S remains an issue: S depends on scene contrast, which varies widely. Possibilities for calculating C: • Total dynamic range of camera: difficult to measure; requires 16-bit depth (48-bit color) files. Better for DSLRs. • “Typical” sunny outdoor scene: 160:1 ratio • Glossy reflective surfaces: up to 100:1 ratio. • Lower signals (10:1 or less) representative of smooth areas like skies. • Imatest plots C as a function of S. These numbers are new and difficult to interpret, so C for S = 100% of reflective target contrast (about 100:1 ratio) is chosen as an artibrary standard. Digital vs film imaging & Channel capacity Norman Koren

  18. Sharpness and noise: summary I • Sharpness: Measured by MTF derived from slanted-edge target. • MTF50 correlates with perceived sharpness. • Sharpening enhances MTF. Some is required: present in virtually all digital images, but the amount varies. • Too much sharpening results in “halos.” • “Standardized sharpening” is used for a fair comparison between cameras. • MTF50 with std. sharpening used for W in Shannon eqn. • Noise N: Measured by RMS variation of signal. • Amount depends on pixel size, ISO speed, sensor technology (CMOS vs. CCD), etc. • Visibility depends on spectrum, degree of enlargement. • Noise reduction (NR) reduces N but removes detail. Digital vs film imaging & Channel capacity Norman Koren

  19. Sharpness and noise: summary II • Noise NN measured with NR can be used in Shannon equation to (continued):give a number that correlates to visual quality rather than true information capacity. • Signal S: Choice of signal remains an issue: Depends on scene contrast. Scenes vary widely. • Arbitrary choice: use 100% of contrast in reflective target (about a 100:1 ratio). Also plot C vs. S (scene contrast). • ShannonC = W log2(1 + S/N) • capacity: A function of bandwidth, signal, and noise. • Difficult to measure exactly: an approximation is required. • Because signal processing masks the true C, we have to settle for a “visual” Shannon capacity based on imperfect measurements. Digital vs film imaging & Channel capacity Norman Koren

  20. Results: 6 megapixel digital SLR Popular consumer DSLR. Excellent performance for a wide range of ISO speeds. MTF50(corr) = 1370 LW/PH. C (@100% of W-B) = 4.15 MB. Digital vs film imaging & Channel capacity Norman Koren

  21. Results: 14 megapixel digital SLR Highest pixel count DSLR (2004). CMOS sensor; no anti-aliasing filter. Extreme noise reduction apparent in the noise spectrum. MTF50(corr) = 3282 LW/PH (higher than total pixel count: aliasing is an issue). C (@100% of W-B) = 15.5 MB (somewhat bogus due to NR and aliasing). Digital vs film imaging & Channel capacity Norman Koren

  22. Results: 5 megapixel compact Popular high quality compact. Significant oversharpening. Excellent performance at low ISO speeds. MTF50(corr) = 1366 LW/PH. C (@100% of W-B) = 2.92 MB. Digital vs film imaging & Channel capacity Norman Koren

  23. Results: 8 megapixel compact High-end compact. Excellent performance at low ISO speeds; degrades rapidly at high ISO speeds. MTF50(corr) = 1609 LW/PH. C (@100% of W-B) = 4.65 MB. Digital vs film imaging & Channel capacity Norman Koren

  24. Conclusions An 8 megapixel DSLR has about the same total MTF50 (in LW/PH) as 35mm film cameras (ISO 100 color slide film scanned at 4000 dpi and sharpened). BUT since noise is lower (and dynamic range is somewhat higher) with DSLRs, a 6 megapixel DSLR has comparable image quality (and Shannon capacity C, though tests are incomplete). Compact digital cameras have excellent capacity, though their performance degrades at high ISO speeds and their dynamic range is more limited. Shannon capacity is difficult to measure because W and N are masked by signal processing and no single value of S represents all scenes. The Imatest program now makes it possible for photographers to measure (approximate) Shannon capacity. Shannon capacity may well become accepted as a metric for measuring camera quality when (1) devilish details in measuring W, N, and S are worked out, (2) the concepts become more familiar, and (3) perceptual testing (relating C to perceived image quality is performed. Digital vs film imaging & Channel capacity Norman Koren