Both CCD (Charge-Coupled Device) and CMOS (Complementary Metal-Oxide Semiconductor) sensors operate based on the photoelectric effect, a physical principle in which a photon striking an atom of a metal or semimetal results in the ejection of an electron.

In the realm of image sensors, the efficiency of SSFs is a crucial factor. The key question is whether CCD sensors can offer more efficient SSFs than CMOS, thereby delivering a richer and more informative color signal. However, the answer is not straightforward. While some CCD sensors may indeed have more efficient SSFs than certain CMOS sensors, the opposite is also true in many cases. So, what determines which SSF is better?

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Both technologies use specially refined and doped silicon (Si) to serve as a semiconductor suitable for sensor construction:

In this case, we see the CIE 1931 2° standard observer curves, the first proposed by the Commission Internationale de l’Éclairage in 1931. If the sensor’s curves matched these, the camera would see exactly as we do, making the characterization phase unnecessary. However, it is not possible to achieve identical SSFs to those of the standard observer.

Under tungsten light, there are greater differences, but it would be difficult to determine which of the two sensors performs better. Once again, the performances are largely comparable, and the practical differences are negligible. Therefore, we can conclude that the distinction between CCD and CMOS does not result in substantial changes to the output. Other variables contribute to the differences in performance observed in photographs taken with two cameras, whether they are CCD, CMOS, or of the same technology.

This article explores a particularly contentious topic in photography—the debate between CCD and CMOS colors. The commonly held belief that CCD colors are superior to CMOS has been vigorously debated across various forums. However, this claim lacks solid technical grounding and empirical evidence to support it as an absolute truth. To provide a clearer understanding, we aim to delve deeper into the issue.

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Even under the artificial light of tungsten, the raw output of the cameras remains similar, with a sigma of 0.62 and a maximum error of 2.74 DeltaE 2000.

As we can see, the performances are remarkably similar and practically equivalent. The design similarities of the two sensors are evident, even though they belong to different technologies, showing comparable results in the test for the D50 illuminant.

An efficient sensor is designed to enhance this separation ability, particularly under common illuminants like sunlight, allowing for a more precise and nuanced capture of colors that can then be refined through the characterization process.

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Moreover, the sensor only provides a value for the intensity of the electric charge, without any detection of color variation. To replicate reality as perceived by human senses, it becomes essential to limit the sensor’s sensitivity range by adding Near Ultraviolet (NUV) and Near Infrared (NIR) filters. Beneath these filters lies a matrix of color filters, typically arranged in a Bayer pattern:

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Even in its simplest form, the sensor demonstrates sensitivity beyond the range of human-perceptible light. Below is a typical curve:

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It is essential to note that the choice between CCD and CMOS is only one of many factors influencing sensor quality. Several other elements should be considered to ensure that the chosen option aligns with the project’s specific requirements. In normal usage, the difference in appearance between CCD and CMOS images can be attributed to other factors, including:

In photographic technology, the characterization phase is critical because cameras cannot naturally render colors as we perceive them. While roses may appear red, grass green, and the sky blue, these colors are not captured as we see them in their raw form. Camera manufacturers do not design sensors to directly mirror natural color perception. Instead, they aim to maximize the sensor’s signal separation capability.

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As a result, a camera does not, by itself, return realistic colors. The raw data contained in a RAW file requires characterization, which is the role played by the camera profile. Each sensor, whether CCD or CMOS, has its own specific SSF curves. These curves result from the combination of all the layers: the NIR and NUV filters, the color matrix, and the native sensitivity of the silicon (Si), which depends on the manufacturing process. The variability between sensors can be minimal or quite significant, but even at this stage, it is practically impossible to attribute these differences solely to the CCD or CMOS technology.

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To further validate our conclusions, we will utilize a multispectral image of a scene measured in the laboratory and present the results of the scene-referred reconstruction using Cobalt profiling. The scene is defined from 400 to 700 nm with a range of 10 nm.

In the Adobe profiling tutorial, it was demonstrated that the most significant factor influencing the difference in camera output is the characterization of raw data materials. However, in cases where cameras undergo proper characterization and profiling with equal precision and technology, it becomes extremely challenging to discern differences in the scene-reconstructed version of the captured image, as confirmed in the final test.

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Now that the sensor’s sensitivity has been restricted to the visible range and it has been equipped with a color filter matrix, we obtain three distinct RGB curves. Ideally, these curves should align with those of the standard CIE observer:

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To start our analysis correctly, it’s essential to reconsider the terminology used in this debate. The word “best” implies a standard of quality based on subjective observations of images from different cameras or raw converters. A more fitting term would be “pleasant.” It is often suggested that cameras using CCD technology produce more appealing color palettes, leading to the assumption that the difference between CCD and CMOS is responsible for this. However, can this difference be solely attributed to the variation in these technologies? In short, the answer is no. The reasons behind this conclusion require a more detailed and nuanced explanation.

In practice, this process involves converting incident photons into electrons, which are then collected to form an electric charge proportional to the intensity of the exposure. Any sensor based on this physical principle behaves in an ideally linear manner, meaning that doubling the incident photons results in a doubling of the collected electric charge, which is subsequently converted into a digital value by the A/D (Analog-to-Digital) circuit.

The heat map in the u’v’ diagram represents the intensity of output variation in 16-bit encoding for each sample, with a change of 1 dCh (Delta Chromaticity), ranging from zero (no separation capacity) to 300 (maximum separation capacity). The graph displays the Adobe RGB gamut (red triangle) and the Pointer gamut as a reference (irregular perimeter). The Pointer gamut encompasses all real objects observable in reflection.

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When comparing CCD and CMOS technologies in equally qualitative projects, the quantification of their impact remains unclear. The capacity to separate signals between the two primarily depends on the overall sensor quality rather than technological preference. In the cameras reviewed, the hardware performance concerning color discrimination was found to be similar, with only minimal distinctions detectable, which were more prominent in laboratory testing.

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Under this illuminant, the cameras provide very similar output with minimal differences. The sigma is 0.50, with a maximum error of 2.38 DeltaE 2000.

There are more than ten thousand unique spectral samples of the Reflectance class, each corresponding to the chosen illuminant.

Signal separation refers to the sensor’s ability to differentiate between two spectral inputs that produce XYZ triplets close together in the tristimulus space defined by the standard observer. The goal is to maximize this capacity across a broad area of the human locus for various illuminants. This capability is crucial in distinguishing between objects with very similar colors, which is fundamental in producing the raw data required to accurately reproduce reality.

Having established the context, we now move to the most intriguing part: an experimental comparison between two cameras, one utilizing CCD technology and the other using CMOS technology. We selected the Nikon D200 and the Nikon D700, which represent CCD and CMOS respectively. Both cameras are from the same manufacturer and are sufficiently close in production time, allowing us to isolate the CCD versus CMOS variable as much as possible.