Resolution measurements

Quite often the resolution of the camera system is only equated with the pixel count of the sensor. Even though the pixel count has a strong correlation with final resolution of the camera system, it is only one factor in the resolution entity. The lens system has a significant impact on the resolution as well as the image processing algorithms. Since the final resolution is a product of the camera components, the evaluation of the result is more complicated.

It has been fascinating to follow the progress of resolution measurements and measurement standardization, because they represent well the race between image processing algorithms and quality measurement methods. The first resolution measurement standard for digital cameras, ISO 12233, was published in 2000, where the resolution measurement was based on a modulation transfer function (MTF) of a high contrast, slanted edge type test chart (ISO 12233:2000). The MTF method and slanted edge charts were very usable ways of measuring the resolution of digital cameras until the cameras began using artificial sharpening algorithms to improve the perceptual sharpness in images.

Artificial sharpening is a method where edge areas in the image are highlighted by adding artificial contrast to the edges. It is interesting to note that artificial sharpening is the same method the human vision system uses to separate outlines even in a poorly luminated environment, so called Mach Band effect (Umbaugh 2005). However, the use of artificial sharpening distorts the results of the MTF method when resolution is measured from high contrast slanted edges (Imatest Sharpening).

Figure 19 shows MTF examples from three mobile phone cameras with different pixel counts, sharpening methods, and overall resolution. Device (a) has a very discreet sharpening without any risk of over sharpening. On the other hand, device (b) has one of the strongest artificial sharpening (a bump in the MTF curve) of the measured devices. Finally, device (c) has significant problems with resolution, even though it has clearly the highest pixel count.

Some assumptions about the lens system quality can be made, when the center and corner resolutions are compared. Also the risk of aliasing and Moiré artefacts can be evaluated from the MTF level after Nyquist frequency.

(a)

(b)

(c)

Figure 19 MTF curves of three mobile phones captured from a low contrast slanted edge chart: (a) Very discreet sharpening, 8 mega pixels, (b) Over sharpening, 13 mega pixels (c) Poor resolution performance, 20 megapixels.

Artificial sharpening algorithms do not highlight low contrast edges as much as high contrast edges and therefore the test charts of the new standard are based on low contrast edges (ISO 12233 2014). Despite this the sharpening is still visible in low contrast slanted edge chart as shown in Figure 19. The new version of the standard defines two different test charts usable for the MTF calculation: a slanted edge type chart and Siemens star based one, see Figure 20. The sinusoidal Siemens star chart should be much more immune to artificial sharpening (Artmann 2015), even though contradictory measurements have been also published (Imatest Slanted-Edge versus Siemens Star). However, where the slanted edge method can measure only one resolution angle at a time, the Siemens star method can measure several.

Another notable change in the 2014 version is that it even contains three different test charts and also the old version of the test chart is kept as an informative annex.

The reason for three different test chart can be found from the competition between different test algorithms and also competition between different test companies.

The result, a standard with three different measurement methods, is quite a lamentable compromise.

At the same time as artificial sharpening issues were found, it was noticed that the sharpness of edges were not the only resolution metrics that should be measured (Artmann and Wueller 2009; Cao et al. 2009; CPIQ texture metrics 2009). When

aggressive denoising algorithms are used, they may corrupt texture areas of the images. For example, leaves, sand and other natural compositions which look like noise were filtered out by the denoising algorithms. To reveal and measure the texture artefact, so called dead leaves or spilled coins test chart was developed. The method was based on a statistically computed test chart which contains different sized circles, mimicking dead leaves on the ground. If the denoising algorithm is too aggressive, it starts to filter out the smallest elements of the chart and the filtering amount can be measured.

Again, the first version of the texture resolution measurement was revealed to be inaccurate. When the captured image has a significant amount of noise, the noise particles were recognized as the smallest circles and the corresponding texture resolution result was too good (Artmann and Wueller 2012). Artmann and Wueller suggested measuring the noise of the image from a uniform gray area of the test chart and suppressing the noise from the dead leaves chart result accordingly. This method was acknowledged for a while, until it was noticed that some noise removing algorithms remove noise much more efficiently from the uniform areas than other parts of the image. When the noise of the gray area was lower than the noise of the dead leaves chart, the method still gave too good results. Finally, the latest suggestion of measuring the texture sharpness originated from the Kirk et al.

paper, where the noise is calculated from the dead leaves test chart itself and a cross-correlation is calculated between the captured image and the original test chart data (Kirk et al. 2014). This seems to be a very good approach, but it requires a full references based approach, which is a very demanding testing method. Figure 20 describes part of the evolution of resolution measurement during recent years.

(a) (b) (c) (d) Figure 20 Examples of the resolution test charts: (a) High contrast slanted

edge, (b) Low contrast slanted edge, (c) Detail of sinusoidal Siemens star and (d) Colored dead leaves. The image is based on paper by Peltoketo 2014

It is especially notable that several lens based artefacts affect sharpness differently depending on the distance from the optical axis. Thus it is reasonable to measure resolution at least from the center of the image and from the corners of the image.

The first version of the ISO 12233 standard defined a limiting resolution where the resolution response drops to 5% towards a reference response measured from the line width/picture height MTF curve. The most recent version of the standard does not define any limiting resolution but repeats the old version by highlighting the importance of the whole MTF curve. This is reasonable, because the MTF curve reveals much more data than a single resolution value, like sharpening and probability of aliasing. Due to the lack of the exact limiting threshold specification, several different MTF values are used to specify resolution using a single number.

MTF50, MTF10 and MTF5 are used where the number (50, 10, 5) represents the value where the contrast is decreased to that specific percentage. Also peak values are used i.e. MTF50P where the decrease is not calculated from the initial value but from the peak of the MTF curve.

As a summary, two different resolution measurement metrics are mostly used and acknowledged nowadays. The MTF metrics, which are based on slanted edge charts or Siemens stars and the texture resolution measurement based on the dead leaves chart. The texture resolution metric is not yet part of any official standard, even though the CPIQ group has proposed it.