The present disclosure relates to a method performed by a deviation analyzing system (1) for black level drift assessment of a digital camera image sensor (21). The deviation analyzing system measures (1001) for pixels of an image captured by the image sensor, a respective luminance value and corresponding chromaticity value of each pixel. The deviation analyzing system further determines (1002) a respective average chromaticity value for differing luminance levels of the measured luminance values, based on for each luminance level averaging the corresponding measured chromaticity values. Moreover, the deviation analyzing system determines (1003), when the respective average chromaticity values of a range (4) of luminance levels indicate—to a predeterminable extent—chromaticity deviations (5) from the respective average chromaticity values (6) of other luminance levels (7), that a black level setting of the image sensor is drifted from a true black level.

The disclosure also relates to a deviation analyzing system in accordance with the foregoing, a digital camera comprising such a deviation analyzing system, and a respective corresponding computer program product and non-volatile computer readable storage medium.

An image signal processor that generates a display signal receives an input image signal having a first pedestal level from an image sensor, generates a first signal from the input image signal, the first signal including a second pedestal level, the second pedestal level being different from the first pedestal level and being determined in accordance with the first pedestal level and a processing gain of the image signal processor, generates a second signal having the second pedestal level by amplifying the first signal in accordance with the processing gain, generates a third signal having the second pedestal level by removing a noise signal from the second signal; and generates a fourth signal by subtracting the second pedestal level from the third signal.

The present disclosure relates to a method performed by a deviation analyzing system (1) for black level drift assessment of a digital camera image sensor (21). The deviation analyzing system measures (1001) for pixels of an image captured by the image sensor, a respective luminance value and corresponding chromaticity value of each pixel. The deviation analyzing system further determines (1002) a respective average chromaticity value for differing luminance levels of the measured luminance values, based on for each luminance level averaging the corresponding measured chromaticity values. Moreover, the deviation analyzing system determines (1003), when the respective average chromaticity values of a range (4) of luminance levels indicate—to a predeterminable extent—chromaticity deviations (5) from the respective average chromaticity values (6) of other luminance levels (7), that a black level setting of the image sensor is drifted from a true black level. The disclosure also relates to a deviation analyzing system in accordance with the foregoing, a digital camera comprising such a deviation analyzing system, and a respective corresponding computer program product and non-volatile computer readable storage medium.

An image signal processor that generates a display signal receives an input image signal having a first pedestal level from an image sensor, generates a first signal from the input image signal, the first signal including a second pedestal level, the second pedestal level being different from the first pedestal level and being determined in accordance with the first pedestal level and a processing gain of the image signal processor, generates a second signal having the second pedestal level by amplifying the first signal in accordance with the processing gain, generates a third signal having the second pedestal level by removing a noise signal from the second signal; and generates a fourth signal by subtracting the second pedestal level from the third signal.

An image signal processor that generates a display signal receives an input image signal having a first pedestal level from an image sensor, generates a first signal from the input image signal, the first signal including a second pedestal level, the second pedestal level being different from the first pedestal level and being determined in accordance with the first pedestal level and a processing gain of the image signal processor, generates a second signal having the second pedestal level by amplifying the first signal in accordance with the processing gain, generates a third signal having the second pedestal level by removing a noise signal from the second signal; and generates a fourth signal by subtracting the second pedestal level from the third signal.

An image signal processor that generates a display signal receives an input image signal having a first pedestal level from an image sensor, generates a first signal from the input image signal, the first signal including a second pedestal level, the second pedestal level being different from the first pedestal level and being determined in accordance with the first pedestal level and a processing gain of the image signal processor, generates a second signal having the second pedestal level by amplifying the first signal in accordance with the processing gain, generates a third signal having the second pedestal level by removing a noise signal from the second signal; and generates a fourth signal by subtracting the second pedestal level from the third signal.

To enable a good quality optimization of the luminances of an image, so that they are not only optimized for a particular maximum displayable luminance of a display, but also a particular amount of light in the viewing environment in which the display is watched, the inventor has invented a method of processing an input image to obtain an output image, wherein the input image has pixels which have input luminances which fall within a first luminance dynamic range (DR 1), which first luminance dynamic range has a first maximum luminance (PL_V_HDR), wherein a reference luminance mapping function (F_L) is received as metadata associated with the input image, wherein the reference luminance mapping function specifies a relationship between luminances of a first reference image and luminances of a second reference image, wherein the first reference image has a first reference maximum luminance and the second reference image has a second reference maximum luminance, wherein the input image is equal to one of the first reference image and the second reference image, wherein the output image is not equal to the first reference image nor the second reference image; wherein the processing comprises applying an adapted luminance mapping function (FL_DA) to the input pixel luminances, to obtain the output luminances, wherein the adapted luminance mapping function (FL_DA) is calculated based on the reference luminance mapping function (F_L) and a maximum luminance value (PLA), wherein the calculation involves finding a position on a metric which corresponds to the maximum luminance value (PLA), wherein a first endpoint of the metric corresponds to the first maximum luminance (PL_V_HDR) and a second endpoint of the metric corresponds to a maximum luminance of one of the first reference image and the second reference image not being equal to the input image, characterized in that maximum luminance value (PLA) is calculated based on a maximum luminance (PL_D) of a display which is to be supplied with the output image, and a black level value (b) of the display, wherein the calculation comprises applying an inverse of an electro-optical transfer function to the maximum luminance (PL_D), subtracting from the resulting value the black level value (b), and applying the electro-optical transfer function to the subtraction to obtain the maximum luminance value (PLA).

An image sensor includes a pixel array including at least one light-shielded area where no light enters and an imaging area where light enters, wherein each pixel includes a photoelectric conversion element, a black level processing unit that corrects an output of each pixel in the imaging area, and a memory that stores a predetermined black level reference for each pixel in the imaging area. The processing unit calculates a Slope, which is determined by an average output value at imaging of pixels in the at least one light-shielded area taken during imaging and a reference average output value of pixels in the at least one light-shielded area under certain conditions taken prior to imaging, and correct an output of each pixel in the imaging area using the predetermined black level reference and the Slope.

A method for black level correction, including determining whether the first image sensor and/or the second image sensor triggers a calibration mode based on a calibration signal, performing, in response to the first image sensor triggering the calibration mode and the second image sensor not triggering the calibration mode, first image acquisition through the first image sensor based on a first automatic exposure configuration, and obtaining a calibration result by performing black level calibration, obtaining, in response to the first image sensor not triggering the calibration mode and the second image sensor triggering the calibration mode, an image to be processed by performing second image acquisition through the first image sensor based on a second automatic exposure configuration, and performing black level correction on the image to be processed based on the second automatic exposure configuration and the calibration result.

Methods and systems to improve the operation of graphic's system are described. In general, techniques are disclosed for compensating for an image sensor's non-zero black-level output. More particularly, a image sensor noise model may be used to offset an image's signal prior to clipping so that the image's dark signal exhibits a linear or near linear mean characteristic after clipping. In one implementation the noise model may be based on calibration or characterization of the image sensor prior to image capture. In another implementation the noise model may be based on an evaluation of the image itself during image capture operations. In yet another implementation the noise model may be based on analysis of an image post-capture (e.g., hours, days, . . . after initial image capture).