Abstract: A mobile device comprising a first image sensor, a second image sensor configured to change position with respect to the first image sensor, a controller configured to control the position of the second image sensor, and an image processing module configured to process and combine images captured by the first and second image sensors.

Abstract: The disclosure is directed to techniques for automatic segmentation of a region-of-interest (ROI) video object from a video sequence. ROI object segmentation enables selected ROI or “foreground” objects of a video sequence that may be of interest to a viewer to be extracted from non-ROI or “background” areas of the video sequence. Examples of a ROI object are a human face or a head and shoulder area of a human body. The disclosed techniques include a hybrid technique that combines ROI feature detection, region segmentation, and background subtraction. In this way, the disclosed techniques may provide accurate foreground object generation and low-complexity extraction of the foreground object from the video sequence. A ROI object segmentation system may implement the techniques described herein. In addition, ROI object segmentation may be useful in a wide range of multimedia applications that utilize video sequences, such as video telephony applications and video surveillance applications.

Abstract: This disclosure describes identifying key frames from a sequence of video frames. A first set of information generated by operating on uncompressed data is accessed. A second set of information generated by compressing the data is also accessed. The first and second sets of information are used to identify key frames from the video frames.

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: The rendering of 3D video images on a stereo-enabled display (e.g., stereoscopic or autostereoscopic display) is described. The process includes culling facets facing away from a viewer, defining foreground facets for Left and Right Views and common background facets, determining lighting for these facets, and performing screen mapping and scene rendering for one view (e.g., Right View) using computational results for facets of the other view (i.e., Left View). In one embodiment, visualization of images is provided on the stereo-enabled display of a low-power device, such as mobile phone, a computer, a video game platform, or a Personal Digital Assistant (PDA) device.

Abstract: A method for real-time 2D to 3D video conversion includes receiving a decoded 2D video frame having an original resolution, downscaling the decoded 2D video frame into an associated 2D video frame having a lower resolution, and segmenting objects present in the downscaled 2D video frame into background objects and foreground objects. The method also includes generating a background depth map and a foreground depth map for the downscaled 2D video frame based on the segmented background and foreground objects, and deriving a frame depth map in the original resolution based on the background depth map and the foreground depth map. The method further includes providing a 3D video frame for display at a real-time playback rate. The 3D video frame is generated in the original resolution based on the frame depth map.

Abstract: This disclosure describes rate control techniques that can improve video coding based on a “two-pass” approach. The first pass codes a video sequence using a first set of quantization parameters (QPs) for the purpose of estimating rate-distortion characteristics of the video sequence based on the statistics of the first pass. A second set of QPs can then be defined for a second coding pass. The estimated rate-distortion characteristics of the first pass are used to select QPs for the second pass in a manner that minimizes distortion of the frames of the video sequence.

Abstract: The disclosure is directed to video processing. The various video processing techniques include generating blocks of information for a frame of video, allocating bits from a bit budget to each of the blocks, the number of bits being allocated to each of the blocks being a function of the information contained therein, and using the bits allocated to each of the blocks to represent the information contained therein.

Abstract: The disclosure is directed to techniques for region-of-interest (ROI) coding for video telephony (VT). The disclosed techniques include a technique for generation of a quality metric for ROI video, which jointly considers a user's degree of interest in the ROI, ROI video fidelity, and ROI perceptual quality in evaluating the quality of an encoded video sequence. The quality metric may be used to bias ROI coding and, in particular, the allocation of coding bits between ROI and non-ROI areas of a video frame.

Abstract: A stereo 3D video frame includes left and right components that are combined to produce a stereo image. For a given amount of distortion, the left and right components may have different impacts on perceptual visual quality of the stereo image due to asymmetry in the distortion response of the human eye. A 3D video encoder adjusts an allocation of coding bits between left and right components of the 3D video based on a frame-level bit budget and a weighting between the left and right components. The video encoder may generate the bit allocation in the rho (?) domain. The weighted bit allocation may be derived based on a quality metric that indicates overall quality produced by the left and right components. The weighted bit allocation compensates for the asymmetric distortion response to reduce overall perceptual distortion in the stereo image and thereby enhance or maintain visual quality.

Abstract: The rendering of 3D video images on a stereo-enabled display (e.g., stereoscopic or autostereoscopic display) is described. The process includes culling facets facing away from a viewer, defining foreground facets for Left and Right Views and common background facets, determining lighting for these facets, and performing screen mapping and scene rendering for one view (e.g., Right View) using computational results for facets of the other view (i.e., Left View). In one embodiment, visualization of images is provided on the stereo-enabled display of a low-power device, such as mobile phone, a computer, a video game platform, or a Personal Digital Assistant (PDA) device.

Abstract: The disclosure is directed to decoder-side region-of-interest (ROI) video processing. A video decoder determines whether ROI assistance information is available. If not, the decoder defaults to decoder-side ROI processing. The decoder-side ROI processing may estimate the reliability of ROI extraction in the bitstream domain. If ROI reliability is favorable, the decoder applies bitstream domain ROI extraction. If ROI reliability is unfavorable, the decoder applies pixel domain ROI extraction. The decoder may apply different ROI extraction processes for intra-coded (I) and inter-coded (P or B) data. The decoder may use color-based ROI generation for intra-coded data, and coded block pattern (CBP)-based ROI generation for inter-coded data. ROI refinement may involve shape-based refinement for intra-coded data, and motion- and color-based refinement for inter-coded data.

Abstract: Techniques for complexity-adaptive and automatic two-dimensional (2D) to three-dimensional (3D) image and video conversion which classifies a frame of a 2D input into one of a flat image class and a non-flat image class are described. The flat image class frame is directly converted into 3D stereo for display. The frame that is classified as a non-flat image class is further processed automatically and adaptively, based on complexity, to create a depth map estimate. Thereafter, the non-flat image class frame is converted into a 3D stereo image using the depth map estimate or an adjusted depth map. The adjusted depth map is processed based on the complexity.

Abstract: A monoscopic low-power mobile device is capable of creating real-time stereo images and videos from a single captured view. The device uses statistics from an autofocusing process to create a block depth map of a single capture view. Artifacts in the block depth map are reduced and an image depth map is created. Stereo three-dimensional (3D) left and right views are created from the image depth map using a Z-buffer based 3D surface recover process and a disparity map which is a function of the geometry of binocular vision.

Abstract: An apparatus comprising a first image sensor, a second image sensor spaced apart from the first image sensor, a diversity combine module to combine image data from the first and second image sensors, and an image processing module configured to process combined image data from the diversity combine module.

Abstract: A mobile device comprising a first image sensor, a second image sensor configured to change position with respect to the first image sensor, a controller configured to control the position of the second image sensor, and an image processing module configured to process and combine images captured by the first and second image sensors.

Abstract: The disclosure is directed to techniques for automatic segmentation of a region-of-interest (ROI) video object from a video sequence. ROI object segmentation enables selected ROI or “foreground” objects of a video sequence that may be of interest to a viewer to be extracted from non-ROI or “background” areas of the video sequence. Examples of a ROI object are a human face or a head and shoulder area of a human body. The disclosed techniques include a hybrid technique that combines ROI feature detection, region segmentation, and background subtraction. In this way, the disclosed techniques may provide accurate foreground object generation and low-complexity extraction of the foreground object from the video sequence. A ROI object segmentation system may implement the techniques described herein. In addition, ROI object segmentation may be useful in a wide range of multimedia applications that utilize video sequences, such as video telephony applications and video surveillance applications.

Abstract: The disclosure is directed to techniques for automatic segmentation of a region-of-interest (ROI) video object from a video sequence. ROI object segmentation enables selected ROI or “foreground” objects of a video sequence that may be of interest to a viewer to be extracted from non-ROI or “background” areas of the video sequence. Examples of a ROI object are a human face or a head and shoulder area of a human body. The disclosed techniques include a hybrid technique that combines ROI feature detection, region segmentation, and background subtraction. In this way, the disclosed techniques may provide accurate foreground object generation and low-complexity extraction of the foreground object from the video sequence. A ROI object segmentation system may implement the techniques described herein. In addition, ROI object segmentation may be useful in a wide range of multimedia applications that utilize video sequences, such as video telephony applications and video surveillance applications.

Abstract: The disclosure is directed to techniques for automatic segmentation of a region-of-interest (ROI) video object from a video sequence. ROI object segmentation enables selected ROI or “foreground” objects of a video sequence that may be of interest to a viewer to be extracted from non-ROI or “background” areas of the video sequence. Examples of a ROI object are a human face or a head and shoulder area of a human body. The disclosed techniques include a hybrid technique that combines ROI feature detection, region segmentation, and background subtraction. In this way, the disclosed techniques may provide accurate foreground object generation and low-complexity extraction of the foreground object from the video sequence. A ROI object segmentation system may implement the techniques described herein. In addition, ROI object segmentation may be useful in a wide range of multimedia applications that utilize video sequences, such as video telephony applications and video surveillance applications.

Abstract: This disclosure describes identifying key frames from a sequence of video frames. A first set of information generated by operating on uncompressed data is accessed. A second set of information generated by compressing the data is also accessed. The first and second sets of information are used to identify key frames from the video frames.