Abstract:
Automatic weight adjustment (AWA) to derive an optimal color correction matrix may address unbalanced color reproduction performance among different illuminations and/or unbalanced color reproduction performance for specific memory colors. AWA may emphasize particular colors and equalize color performance. AWA may be implemented in an automatic process with optional manual operation.
Abstract:
In accordance with one embodiment of the disclosure, apparatus are provided, including an image processor, a unique image processing mechanism, and a unique image processing activation mechanism. The image processor includes the unique image processing mechanism, which processes a certain type of image. The unique image processing activation mechanism causes the unique image processing mechanism to process a given image.
Abstract:
Embodiments include methods of image processing in which pixels of an image are classified according to color. An exposure control operation is performed according to the pixel classifications. In one example, the pixels are classified according to a predetermined segmentation of a color space, based on predicted sensor responses. In another example, the pixels are classified according to a color selected from among at least two colors.
Abstract:
Embodiments include a method of image processing including decomposing a reflectance spectrum for a test surface into a linear combination of reflectance spectra of a set of test targets. The coefficient vector calculated in this decomposition is used to predict a response of an imaging sensor to the test surface. A plurality of such predicted responses may be used for various applications involving color detection and/or classification, including human skin tone detection.
Abstract:
Apparatus are provided including an image signal carrier, a luminance information evaluator, and a chrominance information modifier. The image signal carrier is encoded with an image signal including luminance information and chrominance information. The luminance information evaluator evaluates the luminance information in the image signal for a given region within the image to identify when the given region is one of substantially white and substantially dark. The chrominance information modifier is provided to modify the chrominance information corresponding to the given region when the given region is one of substantially white and substantially dark
Abstract:
In accordance with one embodiment of the disclosure, apparatus are provided, including an image processor, a unique image processing mechanism, and a unique image processing activation mechanism. The image processor includes the unique image processing mechanism, which processes a certain type of image. The unique image processing activation mechanism causes the unique image processing mechanism to process a given image.
Abstract:
The disclosure is directed to techniques for region-of-interest (ROI) video processing based on low-complexity automatic ROI detection within video frames of video sequences. The low-complexity automatic ROI detection may be based on characteristics of video sensors within video communication devices. In other cases, the low-complexity automatic ROI detection may be based on motion information for a video frame and a different video frame of the video sequence. The disclosed techniques include a video processing technique capable of tuning and enhancing video sensor calibration, camera processing, ROI detection, and ROI video processing within a video communication device based on characteristics of a specific video sensor. The disclosed techniques also include a sensor-based ROI detection technique that uses video sensor statistics and camera processing side- information to improve ROI detection accuracy. The disclosed techniques also include a motion-based ROI detection technique that uses motion information obtained during motion estimation in video processing.
Abstract:
The disclosure is directed to techniques for region-of-interest (ROI) video processing based on low-complexity automatic ROI detection within video frames of video sequences. The low-complexity automatic ROI detection may be based on characteristics of video sensors within video communication devices. In other cases, the low-complexity automatic ROI detection may be based on motion information for a video frame and a different video frame of the video sequence. The disclosed techniques include a video processing technique capable of tuning and enhancing video sensor calibration, camera processing, ROI detection, and ROI video processing within a video communication device based on characteristics of a specific video sensor. The disclosed techniques also include a sensor-based ROI detection technique that uses video sensor statistics and camera processing side- information to improve ROI detection accuracy. The disclosed techniques also include a motion-based ROI detection technique that uses motion information obtained during motion estimation in video processing.
Abstract:
Techniques are described for automatic print image matching (PIM) parameter extraction. An original image is captured and PIM parameter data is extracted automatically based on specifics of the original image. At least one automated PIM parameter is calculated automatically from the PIM parameter data. At least one automated PIM parameter is inserted in PIM header information for communication to a rendering device to modify the original image when rendered.
Abstract:
The registration of images comprising segmenting an image in a frame into a set of sectors which forms a circle. Generating a plurality of sets of projections in a base frame, wherein each set of projections is generated from any sector amongst the set of sectors from the base frame. Also generating a plurality of sets of projections in a movement frame, wherein each set of projections is generated from any sector amongst the set of sectors from the movement frame. Then summing each set of projections, from any sector amongst the set of sectors from the base frame and summing each set of projections from any sector amongst the set of sectors from the movement frame. Furthermore, comparing a set of each sum of projections from the base frame with a set of each sum of projections from the movement frame, and generating a rotation angle estimate to add to the base frame.