An image processing apparatus includes a first setting unit configured to set a target range of a luminance level, and a generation unit configured to generate a processed image by performing image processing on a first region of an image in such a manner that the first region is distinguished from a second region and a third region, the first region having a luminance level included in the target range and having a color not included in a target color gamut, the second region having a luminance level not included in the target range and having a color not included in the target color gamut, and the third region having a luminance level not included in the target range and having a color included in the target color gamut.

To enable proper adjustment of a monitor using a color bar regardless of a difference in transfer functions. Provided is an image processing device including a determination unit configured to determine a transfer function related to conversion between light and an image signal and to be used in a display device among a plurality of transfer functions, and a generation unit configured to generate a color bar signal corresponding to the transfer function determined by the determination unit and output the generated color bar signal to the display device.

Implementations provide an aircraft synthetic vision system (“SVS”) giving passengers an exterior view. Omitting windows reduces costs, lowers weight, and simplifies hypersonic aircraft design and construction. Unlike contemporary SVSs, no lag exists between the exterior view and actual aircraft motion. Passengers experience no airsickness associated with a visual and vestibular system feedback mismatch. Lag is eliminated by predicting aircraft interior motion based on sensor feedback, and (b) displaying video camera images transformed to match the predicted aircraft orientation when the images get through the display system latency. Implementations predict aircraft orientation and pre-transform—through the use of dead reckoning to adjust a video signal based on sensed aircraft dynamics and an aircraft electronic model—an image captured from a video camera to match that orientation at the image display time. This approach is robust, cheaper, and more effective than conventional SVS at providing an airsickness-free view of the aircraft exterior.

A video quality evaluation system comprises a training module, a calculation module, an analytical module, a designing module, an optimization module, and an estimation module. The training module collects training videos and trains labels of perceptual quality generation that is associated with the collected videos. The calculation module determines objective metrics based on the trained labels that are associated with the collected videos. The analytical module analyses scenes of the training video, and correlates objective metrics associated with the analysed scenes using perceptual parameters. The designing module designs a Convolutional Neural Network (CNN) Architecture based on data associated with the objective metrics and trains a model generated based on the designed CNN architecture. The optimization module optimizes the model and optimizes power after the model optimization. The estimation module estimates perceptual quality scores for incoming video data after the power optimization.

An image sensor including a pixel array including a plurality of sub-pixels arranged along a plurality of rows and a plurality of columns, and k number of sub-pixels representing one color pixel in conjunction with each other, where k is an integer equal to or greater than four and calibration circuitry may be provided. The calibration circuitry may be configured to receive digital image signal generated based on pixel signals output from the pixel array, calculate a color gain of the digital image signal based on a coefficient set calculated based on a reference image signal generated by a reference image sensor under a first light source having a first color temperature, store coefficient sets associated with a plurality of color temperatures including the first color temperature, and apply the color gain to the digital image signal to generate a calibrated image signal.

An image sensor including a pixel array including a plurality of sub-pixels arranged along a plurality of rows and a plurality of columns, and k number of sub-pixels representing one color pixel in conjunction with each other, where k is an integer equal to or greater than four and calibration circuitry may be provided. The calibration circuitry may be configured to receive digital image signal generated based on pixel signals output from the pixel array, calculate a color gain of the digital image signal based on a coefficient set calculated based on a reference image signal generated by a reference image sensor under a first light source having a first color temperature, store coefficient sets associated with a plurality of color temperatures including the first color temperature, and apply the color gain to the digital image signal to generate a calibrated image signal.

An image sensor which relates to a technology for estimating crosstalk of each pixel is disclosed. The image sensor includes a pixel array including a plurality of unit test patterns, each of which is used to measure values of crosstalk components of a plurality of light blocking pixels generated by a single open pixel, the light blocking pixels and the single open pixel included in the unit test pattern, a storage circuit configured to store the measured values of the respective unit test patterns, a calculation circuit configured to calculate a crosstalk value about each target pixel included in the pixel array by combining the stored values, and a correction circuit configured to correct pixel data of the target pixel by reflecting the calculated crosstalk value in the pixel data.

An image sensor which relates to a technology for estimating crosstalk of each pixel is disclosed. The image sensor includes a pixel array including a plurality of unit test patterns, each of which is used to measure values of crosstalk components of a plurality of light blocking pixels generated by a single open pixel, the light blocking pixels and the single open pixel included in the unit test pattern, a storage circuit configured to store the measured values of the respective unit test patterns, a calculation circuit configured to calculate a crosstalk value about each target pixel included in the pixel array by combining the stored values, and a correction circuit configured to correct pixel data of the target pixel by reflecting the calculated crosstalk value in the pixel data.

A vehicular backing up assistance system includes a rearview camera at a rear portion of a vehicle, the vehicle being a vehicle family member of a particular family of pickup trucks. The system receives first input data that corresponds to the vehicle configuration of the vehicle. When the vehicle is backing up, the system receives second input data corresponding to steering angle of the vehicle. The system displays a predicted trajectory of the vehicle at the in-cabin display as an overlay overlaying the images captured by the rearview camera being displayed at the in-cabin display. The overlay comprises a pair of guidelines that are spaced apart and that are appropriate to (i) the wheelbase of the vehicle configuration of a plurality of vehicle configurations of the particular family of pickup trucks the rearview camera is mounted at and (ii) a current steering angle of the vehicle.

The invention relates to a method for calibrating color components of the sensors of a multi-camera device. The method comprises capturing images by more than one sensor of a multi-camera device (910); creating a pool of images of the captured images (920); extracting a first set of color correction parameters utilizing the pool of images (930); extracting a second set of color correction parameters utilizing the pool of images, wherein the second set of color correction parameters has the smallest errors relative to the first set of color correction parameters (940); and calibrating color components of said more than one sensors of the multi-camera device according to the second set of color correction parameters (950).