翻译这个文档的目的，是为了还原DXO对其评分体系的官方描述，帮助大家理解DXO的评分方法。并按照官方建议使用其评分。

翻译这个文档不代表本人认同其评分计算方法和测试数据的准确性，至于DXO的评分体系是否绝对科学，本人不做评价。

翻译这个文档的初衷是为了让大家能比较客观地看待DXO的评分。并在理解的前提下，选择对自己有意义的部分作为购机参考。

翻译这个文档的动因是避免一知半解，断章取义，以点带面地使用DXO数据。

本文希望照搬原文，来解释DXO对其数据的使用建议，使得任何形式的误用都有据可查。

本人只管照搬内容，不做任何带有引导性质的陈述或总结。若翻译中出现本质错误，欢迎指出！

*  原文粘贴在翻译下方，链接图片在本文最下方
*  本帖只讨论主题相关内容，欢迎严谨的推理和讨论，但不欢迎以偏概全和以点带面的观点，更不欢迎无理取闹

译文开始：
为帮助摄影师比较手中的设备，DxOMark提供了4个分数来体现相机的传感器性能。
• 传感器总体得分，总体得分可以体现普通使用场景（注1）下的性能，它由下列三个使用场景的得分经计算而得：
• 肖像和棚拍，基于传感器的最优色彩位深度（Color Depth）
• 室外风景，基于传感器的最大动态范围（Dynamic Range）
• 体育，基于低光照场景下的ISO（low-light ISO）

DxOMark评分的关键点：
• 所有的分数都只反映传感器的原始性能。所有的测试都直接在RAW文件上进行的，没有经过去马赛克操作和其他图像处理过程。DxOMark并未对相机的其他重要指标例如图像处理过程，机械健壮性，易用度，灵活度，光学质量，价格等进行评估。虽然传感器性能是一项非常关键的指标，但并不是选择数码相机的唯一指标。
传感器总体得分：
• 总体得分及分辨率是相互独立的两个性能指标。也就是说，并不会仅仅因为A相机比B相机像素多（因此能看到更多细节）就能使A相机的总体得分比B相机高。传感器的总体得分，用于度量传感器捕获到的信号质量，包含了像素级的质量和传感器的整体质量。因此，在使用传感器总体得分对相机进行比较之前，先决定自己所需的目标分辨率是非常重要的（目标分辨率很大程度上依赖于你要使用的屏幕尺寸或打印尺寸）。
一旦你决定了一个合适的分辨率，传感器总体得分就会成为一个强大的比较工具。
• 一个相机的分辨率同时依赖于传感器性能和镜头性能。如果将分辨率纳入数码相机的比较范围，你同时还应该参考DxOMark的镜头得分，镜头得分会从几方面对镜头质量打分，其中包括了镜头分辨率。
• 传感器总体得分呈对数分布。5分的差异对应了1/3档的灵敏度。
• 传感器的总体得分依据一个预定义的打印场景来标准化（注2） -- 标准场景指：将800万像素的图片以300dpi的分辨率打印到8x12英寸（20厘米x30厘米）的纸上。任何其他的标准化，即使选择更高分辨率作为标准化的依据，结果也会落入同一个得分范围，有些相机不具备输出所选择分辨率的能力，将不能纳入对比。
• 传感器总体得分是一个开放的分数（注3），不是一个百分比。这个得分由现有的相机，从低端的DSC，到中高端、以至于专业级的DSLR的得分经计算而得，结果都在0到100之间。但随着新技术的应用很可能会有更高得分的型号出现。
使用场景得分：
• 每种使用场景各用一个图像质量指标来度量，因此度量单位是根据各自使用的指标而定：
• 肖像和棚拍的得分是用色深来定义的，它的单位是“位”。
• 室外风景的得分是用最大动态范围来定义的，它的单位是曝光量（EV）
• 体育拍摄的得分是用低光照条件下的灵敏度来定义的，它的单位是ISO数值。
为了能更好使用传感器得分，你需要：
1. 确定你的使用场景：一般拍摄，肖像棚拍，室外风景，还是体育题材。
2. 选择你需要的分辨率。
3. 如果你用RAW格式拍摄，传感器得分可以根据你选择的分辨率和使用场景，帮助你找到最好的相机。

总体得分是如何度量的？
传感器总体得分是三个场景得分的平均值，即：注重色深的肖像棚拍场景，注重动态范围的室外风景场景，以及注重低光照ISO性能的体育拍摄场景。
三个使用场景（各自都有一个分数）-- 肖像，风景，以及体育 -- 分别体现传感器的不同性能。每个场景得分都关联到一个反映图像质量的属性：

肖像摄影：颜色深度
在棚拍场景里往往都会使用可控的，充足的灯光。即使在手持拍摄的情况下，棚拍摄影师也很少会调高ISO。在拍摄肖像和产品一类题材时，最为重要的是尽量获取到最丰富的色彩。
图像质量的度量单位中，与色深最相关的是色彩灵敏度。色彩灵敏度反映了传感器区分细微颜色变化的灵敏程度，一般是表示命中或错过色板上的颜色。传感器能反映的最大颜色灵敏度，以“位”为单位，表示传感器能区分的最大颜色数量。
颜色灵敏度越高，意味着能识别越多的颜色。跟动态范围一样，ISO设为最小的时候，颜色灵敏度最高，随着ISO增高，颜色灵敏度迅速下降。DxO只测试最大颜色灵敏度。
22位的颜色灵敏度意味着优秀，1位以下的差别是不易觉察的。

风景摄影：最大动态范围
风景摄影师经常会仔细地构图，然后选择合适的时机拍摄。这种拍摄一般都会将相机固定在三脚架，然后尽量选择低ISO拍摄，这样可以降低噪点，得到更好的图像质量。
只要场景中没有移动物体，长一点的快门时间在三脚架上不是问题。然而动态范围却是至关重要的，摄影师经常希望从高对比的场景中得到更多细节，包括明亮的天空和阴暗的树叶、山丘等。理想情况下，相机的动态范围应该比场景的动态范围更大，否则要么暗光区域的细节丢失，要么高光区域过曝。
动态范围随着ISO的增加而迅速下降，任何模拟及数字放大处理都会给暗部带来更多的噪点，使得层次难以辨识。最大动态范围是指一个传感器能记录的最亮部细节和最暗部细节之间的范围，用EV数（曝光量）或者光圈档位（f-stops）来表示，每增加1EV（或一档），曝光量增加1倍。
动态范围对应了一个相机能捕获的最亮（饱和度）和最暗（通常指当噪点比信号更多的时候，例如SNR低于 0dB的情况）的光照条件。
12EV的动态范围被认为优秀，0.5EV的差别一般不易察觉。
动态范围值是一个开放值，因为入射光强度是没有限制的。

体育和运动摄影：低光照ISO
前面两个场景中，光照条件要么很充足（棚拍），要么很稳定（风景），图片记者和运动摄影师经常在低光照的条件下拍摄运动中的目标。想压低ISO，同时又要获得可用照片的难度很大。
当拍摄动态场景例如运动会的时候，摄影师的主要目标是凝固动作，将更短的快门时间视为最高优先级。为补偿曝光时间的延长，他们不得不提高ISO设置，这就意味着信噪比的下降。最高能将ISO提高到多少，同时还能保持相当好的图像质量？我们的低光照ISO指标可以告诉他们答案。
SNR(signal-to-noise ratio) 信噪比，表示一张图像里有多少噪点和多少实际的信息（信号）。SNR越高，图像看上去越好，因为细节还没有被噪点淹没。SNR的单位是dB，它呈对数分布：6dB的差异意味着信噪比相差一倍，也就意味着噪点数量相差一倍。
30dB的SNR视为优秀。所以，低光照ISO代表了一个相机最高的可接受的ISO值，具体是：达到30dB的SNR，并且保持9EV的动态范围，同时保证18位色深的前提下，一个相机允许使用的最高ISO值。
25%的低光照ISO差别代表1/3EV，不易被察觉。
随着相机不断改进，低光照ISO数值也会随之上升，因此这是一个开放值。

标准化评分：
不同的DSLR提供不同的分辨率，从600万到2400万像素（以后还会有更高的）。高分辨率传感器可以提供更多细节，但由于它们的单个像素更小，经常导致更高的噪点水平，最终导致动态范围、灰阶，和色彩敏感度的下降。然而，在同等条件下打印的时候，传感器性能差异有可能因为打印尺寸的不同而被缓和或凸显。
将一个800万像素的图片和一个3200万的图片都以300dpi分辨率打印在20x30cm的纸上，然后进行比较，这时，图片的原始得分对最终结果没有直接的参考意义。800万像素的图片需要经过插值后才能打印（20厘米，大约8英寸需要2300像素 ），而3200万像素的图片，需要经过大范围缩图，大约每4个原始像素缩小为1个像素。这个操作会极大地降低3200万像素图片的噪点，进而提高量化结果。
原始的测量值可以帮助估计100%观看时的图片质量，但为了更好的预测打印的比较结果，我们提供了一套标准化（意味着更可靠）的评分，它以800万像素的输出为参考，对原始得分进行标准化。

标准化评分的细节：
DxoMark为各种类型的数码相机打分，这些数码相机的传感器的像素数量，像素大小，以及信号处理都有很大差别。为了保证传感器之间的公平比较，非常重要的两点就是要保证在同等条件下测试，同时要将观看条件纳入对比范畴。
为了比较相机，我们将18度灰信噪比（SNR 18%），动态范围（Dynamic Range），灰阶范围（Tonal Range），色彩敏感度（Color Sensitivity）等指标随着ISO值而变化的函数，绘制在同一个坐标系上，反映ISO变化对各项指标影响的走势图。有两个关键元素可能对比较带来偏差：ISO灵敏度和传感器分辨率。比如，一台相机的ISO100可能和另一台相机的ISO100不完全一样；另外，相机的分辨率可能也不一样。更多的像素意味着更多细节，但一般会带来更多噪点。我们需要审视ISO灵敏度和分辨率对评估的影响，并且要讨论如何才能应付这些影响，以保证无论是什么分辨率和ISO灵敏度的传感器，DxOMark的总体得分都能够被用于为相机排名。
• ISO代表相机捕获的原始信号和最终输出信号之间的放大倍数来定义其对光线的敏感程度。例如，一个摄影师用两台不同的相机拍摄同一个场景。两台相机都有相同的曝光设置和ISO设置。令人感到惊奇的是，大多数情况下最终输出图片的曝光量并不一样。这就意味着要么曝光时间或者ISO灵敏度并没有精确地反映预先的设定。普通相机的ISO设置往往是为数不多的一些数值（100，200，400，800，1600，偶尔会有640，1250等）。国际标准化组织（ISO）精确定义过ISO敏感度，以及测量方法。为了方便用户的使用，ISO标准允许相机厂商在标识的时候把ISO数值补齐为100，200一类整数值；然而，将真实值补齐到接近的整数，意味着标称值与实际值可能有最多20%的偏差。此外，相机厂商还可能在图像处理过程中智能地改变ISO。为了避免ISO补齐所带来的偏差，DxO自己精确地测量ISO实际值，并且用它作为基础来绘制其他的传感器特征 -- SNR 18%（signal-to-noise ratio 18% 18度灰的信噪比）, DR（Dynamic Range，动态范围）, TR（Tonal Range，灰阶范围）, 以及CS（Color Sensitivity，颜色敏感度）。DxOMark的数据库页面提供了DxO测得的ISO和厂家标称的ISO之间的差异。

• 不同相机的分辨率不同，但最终的图片会在相同尺寸的屏幕，或打印到相同大小的相纸上进行比较。一方面，高分辨率相机在给定的输出设备上能够表现更好。另一方面，高分辨率相机因为更小的像素尺寸会降低信噪比（SNR）。DxO按分辨率对得分进行标准化，有助于对不同分辨率传感器的真实性能进行统一的对比，就像真的将两张图片打印到同一个打印机一样。现在考虑一个具有N个像素的传感器。在这个传感器基础上，通过计算标准差（注4），可以模拟出一个具有N0个像素的传感器的噪点水平。具体公式如下：• 正如我们看到的，高像素传感器输出低分辨率的图像时能赢得更高的SNR，DR，TR，和CS。对于DxOMark的传感器总体得分和指标，我们选择了800万像素作为参考分辨率，相当于在12x8英寸的纸上以300dpi的分辨率打印。然而其他的分辨率也是可以选来作为参考的，只需要将之前用800万像素标准化后的数据稍加移动即可（因为在公式里参考分辨率N0是一个对数）。我们需要记住的是，当分辨率增加一倍，意味着：
增加3dB的标准化 SNR
增加0.5位的标准化 DR
增加0.5位的标准化 TR
增加1.5位的标准化 CS

• 另外需要注意，在DxO的测量数据库中，可以查看到标准化后的数据，也可以看到绝对值数据，然而DxOMark的传感器总体评分，只与标准化之后的数据相关。

注1. use case：“用例”一词源自于计算机软件测试领域，用于描述用户对产品的一个使用案例。测试过程一般会选择具有代表性的使用案例。本文的“使用场景”代替“用例”一词，是为了便于读者能快速理解。
注2. normalization：有的译作“归一化”，本文翻译为“标准化”。意思是DxO将不同相机的得分按分辨率进行转换后放在相同的条件下进行比较。
注3. 开放意味着无上限，这里指虽然目前的相机得分都在0~100之间，但以后的新相机有可能超过100分。
注4. 标准差在概率学中用于描述样本相对于标准值的偏移程度。在这里DXO使用这个概念，将像素差转换为标准差，然后作为参考系数，用于估计各指标在800万像素时的成绩。

原文粘贴：
To help photographers rank and compare photographic equipment, DxOMark provides four scores showing camera sensor image quality performance:
￥ Sensor Overall Score, showing the performance for a general-purpose use case, and computed from the following three use case scores:
￥ Portrait Score, based on optimum Color Depth.
￥ Landscape Score, based on maximum Dynamic Range.
￥ Sports Score, based on Low-Light ISO.

Key points about DxOMark Sensor Scores:
￥ All sensor scores reflect only the RAW sensor performance of a camera body. All measurements are performed on the RAW image file BEFORE demosaicing or other processing prior to final image delivery. DxOMark does not address such other important criteria as image signal processing, mechanical robustness, ease of use, flexibility, optics quality, value for money, etc. While RAW sensor performance is critically important, it is not the only factor that should be taken into consideration when choosing a digital camera.

Sensor Overall Score
￥ Sensor Overall Score AND resolution are two independent metrics of sensor performance. This means that just because camera A has more pixels than camera B (and thus sees more details) does not mean that camera A’s Sensor Overall Score will be better. Rather, Sensor Overall Score measures the quality of the captured signal, either at a pixel level or at the full sensor level. So before comparing cameras with Sensor Overall Score, it is important to first determine the resolution you are looking for (which largely depends on the size of the screen or the print you intend to use or produce).

￥ Once you choose an appropriate resolution, the Sensor Overall Score becomes a fair and powerful tool with which to make comparisons.

￥ In a camera, resolution is dependent on both sensor and lens performances. So to compare and rank digital cameras while taking resolution into account, you should look at the DxOMark Score for lenses (with camera), which weighs a number of image quality parameters, including resolution.
￥ Sensor Overall Score is logarithmic. A 5-point difference on the scale corresponds to a gain or loss of sensitivity of 1/3 of a stop.
￥ Sensor Overall Score is normalized for a defined printing scenario—8Mpix printed on 8"x12" (20cmx30cm) at 300dpi resolution. Any other normalization, even at a higher resolution, would lead to the same ranking, given that any camera that could not deliver the chosen resolution would be eliminated from the comparison.
￥ Sensor Overall Score is open and it is not a percentage. This score has been computed so that the current set of cameras, from low-end DSCs up to professional DSLRs and medium-format cameras, show results within a range from 0 to 100. However, new technologies may well lead to higher performance models.
Sensor Use Case Scores
￥ Each sensor use case score is defined by a specific image quality metric. Therefore the reported value for each score is expressed with the unit of the corresponding metric, as follows:
￥ Portrait Score is defined as the color depth performance and its unit is a number of bits;
￥ Landscape Score is defined as the maximum dynamic range performance and its unit is an exposure value (EV);

￥ Sports Score is defined as the low-light sensitivity performance and its unit is an ISO sensitivity value.

To better use Sensor Scores:

1 Identify your preferred use case: General purpose, Portrait, Landscape, and/or Sports.
2 Choose the resolution that you need for the kind of photography you do.
3 If you shoot in RAW, Sensor Scores will help you rank the best cameras according to the resolution and use case(s) that you have chosen.

How is Sensor Overall Score measured?
The Sensor Overall Score is an average of the Portrait Score based on color depth, the Landscape Score based on dynamic range, and the Sports Score based on low-light ISO.

Three use cases (with one score for each) — Portrait, Landscape, and Sports — report on different aspects of sensor performance. Each use case score is associated with one defined image quality metric as defined below:

Portrait photography: Color Depth
Flash studio photography involves a controlled and usually maximal amount of light. Even when shooting with hand-held cameras, studio photographers rarely move from the lowest ISO setting. What matters most when shooting products or portraits is to aim for the richest color rendition.
The best image quality metric that correlates with color depth is color sensitivity. Color sensitivity indicates to what degree of subtlety color nuances can be distinguished from one another, often meaning a hit or a miss on a pantone palette. Maximum color sensitivity reports, in bits, the number of colors that the sensor is able to distinguish.
The higher the color sensitivity, the more color nuances that can be distinguished. As with dynamic range, color sensitivity is greatest when ISO speed is minimal, and falls rapidly with rising ISO settings. DxO Labs has focused on measuring only maximum color sensitivity.
A color sensitivity of 22bits is excellent, and differences below 1 bit are barely noticeable.

Landscape photography: maximum Dynamic Range
Landscape photographers often carefully compose their images and choose the optimal time to shoot. This type of photography commonly involves mounting the camera on a tripod and using the lowest possible ISO setting to minimize noise, thus maximizing image quality.
Unless there is motion, relatively long shutter speeds are not an issue with a tripod. What is paramount is dynamic range, especially because photographers will often aim for detail in high-contrast settings, juxtaposing bright sky with shadowy foliage, mountains, etc. Ideally, the dynamic range of the camera should be greater than the dynamic range of the scene, otherwise details in shadows are lost or highlights are burned.

Dynamic range falls rapidly with higher ISO settings, as any analog or digital amplification performed will increase the noise in the darker areas, making it harder to distinguish between fine levels of contrast.
Maximum dynamic range is the greatest possible amplitude between light and dark details a given sensor can record, and is expressed in EVs (exposure values) or f-stops, with each increase of 1 EV (or one stop) corresponding to twice the amount of light.

Dynamic range corresponds to the ratio between the highest brightness a camera can capture (saturation) and the lowest brightness it can capture (typically when noise becomes more important than the signal, i.e., a signal-to-noise ratio below 0 dB).
A value of 12 EV is excellent, with differences below 0.5 EV usually not noticeable.
This scale is open, as incoming light is not a bounded quantity.

Sports & action photography: Low-Light ISO
Unlike the two previous scenarios in which light is either generous (studio) or stability is assured (landscape), photojournalists and action photographers often struggle with low available light and high motion. Achieving usable image quality is often difficult when pushing ISO.

When shooting a moving scene such as a sports event, action photographers’ primary objective is to freeze the motion, giving priority to short exposure time. To compensate for the lack of exposure, they have to increase the ISO setting, which means the SNR will decrease. How far can they go while keeping decent quality? Our low-light ISO metric will tell them.

The SNR indicates how much noise is present in an image compared to the actual information (signal). The higher the SNR value, the better the image looks, because details aren't drowned by noise. SNR strength is given in dB, which is a logarithmic scale: an increase of 6 dB corresponds to doubling the SNR, which equates to half the noise for the same signal.

An SNR value of 30dB means excellent image quality. Thus low-light ISO is the highest ISO setting for a camera that allows it to achieve an SNR of 30dB while keeping a good dynamic range of 9 EVs and a color depth of 18bits.

A difference in low-light ISO of 25% represents 1/3 EV and is only slightly noticeable.
As cameras improve, low-light ISO will continuously increase, making this scale open.

Defining viewing conditions for normalizing measurements and scores:
Different DSLRs offer resolutions ranging from about 6 up to 24Mpix (with higher resolutions to come). Higher-resolution sensors offer more detail, but their smaller pixel size often leads to higher noise levels, and consequently to lower dynamic range, tonal range, and color sensitivity. When printing under identical conditions, however, the performance differences can be either mitigated or accentuated by the choice of print size.

When one compares an 8Mpix image with a 32Mpix image printed on identical 20x30cm paper at 300dpi, the measurements for the original image do not give a good indication of the final result. The 8Mpix image will require no interpolation (2300 pixels for 20cm, or roughly 8 inches), while the 32MPix image will have to be reduced significantly, with 4 original pixels averaged into a single pixel. This averaging operation will significantly reduce the noise in the 32Mpix, and accordingly improves the measurements provided for the original resolution.

Original measurements can help gauge image quality when viewed at 100%, but to better predict how prints will compare, we provide a normalized (and thus more reliable) version based on 8Mpix.
See "Detailed computation of DxOMark Sensor normalization" for more information.

On DxOMark, we evaluate and rank many types of digital cameras with image sensors that vary widely in pixel count, pixel size, and digital signal processing. To ensure that sensor performance comparisons between cameras are fair, it is very important both to test under identical shooting conditions and to take viewing conditions into account.

To compare cameras, we plot on the same graph the evolution of certain characteristics (SNR 18%, dynamic range, tonal range, color sensitivity) as a function of the ISO sensitivity value. Two essential elements can introduce bias in this comparison: ISO speed and resolution. For example, ISO 100 for a given camera may not be completely equivalent to ISO 100 for another camera; further, the resolution of the cameras may be different. More pixels mean more details, but also more noise in general (see More pixels offset noise!). We review here the influence of ISO speed and resolution on measurement and discuss how we have dealt with them to ensure that the DxOMark Overall Sensor Score and DxOMark Sensor Metrics can be used to rank cameras regardless of their intrinsic resolution and sensor sensitivity.

￥ ISO speed defines light sensitivity by relating the amplitude of the output signal to the light captured by the camera.For example, a photographer takes two different cameras to shoot a scene. Both cameras have same optics and are set to same exposure time and ISO speed values. Surprisingly, in most cases the output images will differ in terms of exposure. This means that either the exposure time or the ISO sensitivity is actually different for the presumed “same” setting.The ISO setting scale on regular cameras normally utilizes a small number of values (100, 200, 400, 800, 1600, and sometimes 640, 1250). ISO speed (or sensitivity) definitions and measurement protocols are precisely described by the International Standards Organization (ISO)1, as explained in Metrics and Measurements. To make things easier for the user, the ISO standard allows for rounding up the actual sensitivity values to such numbers as 100, 200, etc.; however, rounding means that the nominal sensitivity can differ from the real sensitivity by up to 20%. Moreover, manufacturers can also use image processing to artificially change ISO sensitivity (by applying gains in the processing).To eliminate bias and rounding errors, DxO Labs accurately measures ISO sensitivity and uses it as the basis for plotting all other characteristics — SNR 18%, dynamic range, tonal range, and color sensitivity.The DxOMark Database page provides links to ISO speed graphs that show the differences between DxO Labs’ ISO-based speed data and manufacturers’ values.
￥ Resolution varies from camera to camera, but ultimately images will be compared on similarly-sized screens or prints. On one hand, a higher-resolution camera will perform better because more pixels equate to more information for a given output surface. On the other hand, the same camera has a smaller pixel size and this decreases the SNR. Normalizing by pixel resolution makes it possible to compare actual sensor performance, similar to printing two images on the same printer.Consider a sensor with  megapixels. Starting from this sensor, it is possible to simulate the noise standard deviation of a sensor with a reference resolution . In the formulas below, the values without subscripts are those that can be measured on the sensor (from actual shots of targets); values with the subscript “norm” are normalized values — that is, corresponding to a sensor with resolution .
We give the relationship between the measured and normalized values for standard deviation, signal to noise ratio (SNR), dynamic range (DR), tonal range (TR) and color sensitivity (CS) as follows:

￥ As can be seen, high-resolution sensors will gain more SNR, DR, TR and CS when reduced to a lower reference resolution. For DxOMark Sensor Overall Score and Metrics, we chose a reference resolution equal to 8 Megapixels, which is a bit less than a 12" x 8" print with a 300dpi printer. However, any other resolution can be chosen, as doing so only shifts the normalized values by a constant (because the reference resolution appears only as a logarithm in the formulas above).
What should be remembered is that doubling the resolution adds:
◦ 3dB to the normalized SNR
◦ 0.5 bit to the normalized DR
◦ 0.5 bit to the normalized TR
◦ 1.5 bit to the normalized CS.
￥ Also note that in DxO Labs’ Measurement Database, one can look either at normalized or absolute results, yet in the context of the DxOMark Sensor Overall Score, only normalized results are relevant. (Read more in our Insights section.)

原文入口在图片中用箭头指出，原文中所提及的总体评分及场景评分用红框标出：

从色彩滤镜上看，尼康的肤色基本上是红色了，而佳能还有很大部分的黄！
看来还是佳能的人像肤色还原得更准确真实啊！

这个说法个人是比较同意的。同样bit数的cmos，表达的EV跨度大的（动态范围就大），就意味着相邻色阶亮度变化就大，就更容易出现肉眼可见的色阶断层。（就是传说中的“不细腻”）
但另一方面，动态范围大的，相邻色阶之间的渐进变化这种线性趋势仍然是保留的。所以个人认为这种情况通过后期（缩图或调整曲线，或者就是LR里的拉亮暗部，拉低亮部），这样压缩相邻色阶间的亮度值，使之更为邻近，也可以获取到细腻的过度。
所以感觉说佳能拍人对肤色还原好，“白里透红”（更准确的应该是说“黄里透红”），更多的应该是来自于前面那位兄弟所说的红滤镜的黄色倾向。

个人认为佳能的肤色表现好，有两个原因：
1.佳能色彩滤镜中使用的是黄色像素，对黄色表现更真实，肤色正好是以黄色为主，所以肤色的表现真实准确！
2.色阶更细腻！

感觉DXO的指标必须具体地看并且有条件地看待，不能只看总分。因为单从动态范围和色深指标等方面很难解释佳能人相照片比别的某某指标更强悍的相机的肤色表现更细腻和鲜活。

人像方面的色深指标并不一定对应更好的人像肤色表现， 比如佳能的人像很不错， 但是色深指标并不如对手相机。

动态范围数值可能比较令人困惑，抛开人像， 如果拍风景纯粹考虑暗部和亮部细节保留，DXO 的动态范围数据是很好的参考。screen模式的数据更直接，print模式下的动态范围是推算值，有局限。14bit的相机得到超过14 EV的动态范围值， 可能令人困惑。

噪声指标方面数值很好， 与实际感受相对应。 缩图合并像素后带来的信噪比提升算法很好，解释了高像素缩图提高信噪比的原理和现象。

老法师怎么YY也改变不了假能传感器屎出翔的现状 而且现在各家都搞五轴防抖了 偷工减料能跟质量问题康还没动静

个人认为：DXO的色深评测纯属扯淡。。。

叠加了红色通道的色彩噪声，对于佳能飞思这种认为黄色更重要的厂家，统统判了死刑！

另外，就算尼康索尼偏红，但也还是用黄色在的，DXO如此权威，但为什么没有一个厂家用纯红色呢？

为楼主点赞！

不屈不挠，黑之典范！
精神可嘉，态度积极。建议以后多补充专业知识，以补足业务上的短板。毕竟这份工作不能光靠满胸斗志和一腔热血。时代在进步，要与时俱进。Screen黑法已过时，建议停用。
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谢谢支持！

做个记号，以后精读。
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※※上有个视频，一个老外花了大约一个小时时间，讲解了如何使用DXO网站的数据。但他声明他跟DXO没有合作关系，不代表官方。你的观点和这老外很一致。但这老外用词更加夸张，他居然说他认为可以完全忽略Screen得分，因为他想象不出有什么场合需要像素对像素地查看一张图。

我是Screen和Print都看的，这老外他明显不懂我等100%党内心的追求。

网友们如果想看这个视频可以跟着这个链接，但有两个难点：一是需要翻墙，二是没有字幕。How to Use DxOMark: Lens Sharpness & Sensor Quality

本帖最后由 imkdk 于 2015-12-22 17:41 编辑

即使是完全相同的同一技术，和像素密度相同，但由于CMOS面积不同，也可能造成发热和噪点等不同。