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.

Abstract: Luma adaptation for digital image processing. Luminance signals are separated from sensor RGB signals representing an image. A transfer function is obtained from the luminance signals. Using the transfer function, the sensor RGB signals are adjusted to adapt the luma of the image.

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.

Abstract: A method is disclosed that includes receiving image data and calculating brightness information of the image data. The method includes correcting at least one lens roll-off value to be used in a lens roll-off correction operation based on the brightness information. The method also includes performing the lens roll-off correction operation on the image data using the at least one corrected lens roll-off value.

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 a particular embodiment, a method is disclosed that includes performing a first test using a first pixel value of a pixel to determine whether the pixel is outside a skin color region of a color space. The method includes, when the first test does not identify the pixel as outside the skin color region, performing a second test using a second pixel value of the pixel to determine whether the pixel is outside the skin color region of the color space. The method further includes, when the second test does not identify the pixel as outside the skin color region, performing a third test using a third pixel value of the pixel to determine whether the pixel is outside the skin color region of the color space.

Abstract: Image processing methods and systems are disclosed. In a particular embodiment, a method is disclosed that includes receiving image data. The image data includes color component data representing a location of a pixel in a color space. The method further includes performing a linear transformation of the location of the pixel in the color space when the location is identified as within a skin color region of the color space. The linear transformation is performed by mapping the location of the pixel at a first portion of the skin color region to a second portion of the skin color region based on a position of the pixel within the skin color region and based on the proximity of the position of the pixel to a boundary of the skin color region. The color space remains substantially continuous at the boundary of the skin color region after applying the linear transformation.

Abstract: The disclosure is directed to techniques for automatic focus control. The automatic focus control techniques prioritize focus of a camera based on skin tone using a skin color detection approach which is intrinsically image sensor-dependent. Sensor-dependent skin color detection to support automatic skin tone prioritized focus control in a camera can enhance the focus of people in the scene. The techniques may be especially useful in digital video camera design, digital still photo camera design, and sensor applications involving people tracking. Sensor-dependent skin color detection is performed once a specific sensor is characterized by taking several raw images of a standard color test target in controlled illumination conditions. Sensor-dependent skin color detection can provide high detection precision and reliability. With sensor-dependent skin color detection, the focus of a camera can be automatically adjusted to prioritize regions of an image containing skin tones.

Abstract: To improve the color performance, disclosed herein are advanced methods for improving chroma accuracy, by reducing the color difference between reproduced colors and human perceptual responses, as well as enhancing preferred colors. Chroma enhancement may be performed by selecting one of a plurality of chroma enhancement matrices (CEM), and converting a received color by multiplying it with the chroma enhancement matrix (or a matrix comprising the CEM). The matrix may be selected by mapping a received color value into a region and selecting the corresponding chroma enhancement matrix (or alternately, the corresponding parameters, from which color conversion may be performed). Additionally, enhancement may be adjusted for a variety of environments or preferences. For example, one of a plurality of sets of matrices may be selected in accordance with a desired mode. Optimization techniques for determining the desired chroma enhancement matrices for one or more modes are disclosed.

Abstract: Methods and apparatus for processing a captured image of a medium, content on the medium, and a background surrounding the medium are provided. A captured image is processed by 1) improving the visibility of the content by recovering the original appearance of the medium and enhancing the appearance of the content image, 2) removing the background by determining the boundary of the medium, and/or 3) correcting geometric distortion in the content to improve readability. Any of the three processing steps may be used alone or any combination of the three processing steps may be used to process the image. The image processing may operate on the image after it is captured and stored to a memory and may be implemented on an image-capturing device that captures the image. In other embodiments, the image processing is implemented on another device that receives the image from the image-capturing device.

Abstract: Techniques for identifying and enhancing colors in a digital image associated with one or more target color shades. In an embodiment, the target color shades may include a shade of blue associated with the sky, a shade of green associated with outdoor foliage, or the color red. In an embodiment, the blue chroma (Cb) and red chroma (Cr) coordinates of a pixel are evaluated to determine whether to apply an enhancement factor. The enhancement factor may incorporate an exposure index (EI) auxiliary enhancement factor, a color temperature (D) auxiliary enhancement factor, and a luminance (Y) of each pixel. Further aspects for implementing the techniques in software and hardware are disclosed.

Abstract: This disclosure describes barcode scanning techniques for an image capture device. The image capture device may automatically detect a barcode within an image while the image capture device is operating in a non-barcode image capture mode, such a default image capture mode. In one aspect, the detection of the barcode within the image may be based on a combination of identified edges and low intensity regions within the image. The image capture device may configure, based on the detection of the barcode, one or more image capture properties associated with the image capture device to improve a quality at which the images are captured. The image capture device captures the image in accordance with the configured image capture properties. The techniques may effectively provide a universal and integrated front-end for producing improved quality images of barcodes without requiring significant interaction with a user via a complicated user interface.

Abstract: This disclosure describes techniques for detecting a barcode within an image. An image processor may, for example, process an image to detect regions within the image that may be barcodes. The image processor may identify regions of the image that exhibit a high concentration of edges and a high concentration of pixels with low optical intensity co-instantaneously as potential barcodes. The image processor may identify the regions using a number of morphological operations. The image processor may then determine whether the identified regions are actually barcodes by verifying whether the region have unique barcode features. The barcode detection techniques described in this disclosure may be independent of barcode size, location and orientation within the image. Moreover, the use of morphological operations results in faster and more computationally efficient barcode detection, as well as lower computational complexity.

Abstract: An apparatus, system, and method provide optimization of color space conversion using a joint quality metric representing differences between reference human visual representations and converted device captured image representations in a perceptual uniform color space, where the conversion includes transforming a nonstandard color space (RGB) to a perceptual standard color space (sRGB) and where the joint quality metric includes a color portion, a noise portion, and a contrast portion. An optical sensor produces a digital reference signal when capturing an image of a reference source such as a Macbeth ColorChecker color rendition chart. After white balancing and color correcting the digital reference signal, the gamma curve parameters are determined by minimizing the joint quality metric which is based on differences, within a uniform perceptual color space such as CIELAB, between the gamma curve compensated signal and the standard values for the reference source.

Abstract: Techniques for improving the quality of images are described. A first histogram of intensity values may be obtained for an input image and diffused to obtain a second histogram with better intensity coverage. The diffusion may be achieved by filtering the first histogram for multiple iterations with a diffusion function obtained based on a filter function and a diffusion control function. The filter function may control the rate and/or characteristics of the diffusion. The diffusion control function may control shifts in positions of lobes in the first histogram. A transformation function may be determined based on a first cumulative distribution function (CDF) for the first histogram and an inverse function for a second CDF for the second histogram. An output image may be generated by mapping each pixel value in the input image to a corresponding pixel value in the output image based on the transformation function.

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: 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.

Abstract: The disclosure describes adaptive filtering techniques to improve the quality of captured image information, such as video or still images. An image sensor captures image information and determines a plurality of parameter values based on a current exposure index and a current scaling factor of the image information. The adaptive spatial image filter includes both horizontal and vertical sharpening filters and configures, i.e., adapts, the horizontal sharpening filter and the vertical sharpening filter based on the plurality of parameter values determined by the image sensor. The adaptive spatial image filter applies the horizontal and vertical sharpening filters to at one channel of the image information to generate filtered image information.

Abstract: Methods and apparatus for processing a captured image of a medium, content on the medium, and a background surrounding the medium are provided. A captured image is processed by 1) improving the visibility of the content by recovering the original appearance of the medium and enhancing the appearance of the content image, 2) removing the background by determining the boundary of the medium, and/or 3) correcting geometric distortion in the content to improve readability. Any of the three processing steps may be used alone or any combination of the three processing steps may be used to process the image. The image processing may operate on the image after it is captured and stored to a memory and may be implemented on an image-capturing device that captures the image. In other embodiments, the image processing is implemented on another device that receives the image from the image-capturing device.

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.