Abstract:
A computer implemented method for automatically identifying shot changes in a video sequence in real-time or near-real-time is disclosed. Optical flow energy change differences between frames, sum-of-square differences between optical-flow-compensated frames, and hue histogram changes within frames are analyzed and stored in frame buffers. A feature vector formed from a combination of these measurements is compared to a feature vector formed from thresholds based on tunable recall and precision to declare the presence or absence of a shot change.
Abstract:
A digital image processing system takes color plus Z channel data as input, preprocesses, decimates, and codes the Z channel in-band as digital watermark data embedded within the color data prior to encoding and transmission. A second digital image processing system receives, decodes, and extracts the decimated Z channel data before applying statistical regularization to restore a full-resolution Z channel prior to depth-image-based rendering.
Abstract:
A digital image processing system takes color plus Z channel data as input, preprocesses, decimates, and codes the Z channel in-band as digital watermark data embedded within the color data prior to encoding and transmission. A second digital image processing system receives, decodes, and extracts the decimated Z channel data before applying statistical regularization to restore a full-resolution Z channel prior to depth-image-based rendering.
Abstract:
A computer implemented method for automatically identifying shot changes in a video sequence in real-time or near-real-time is disclosed. Optical flow energy change differences between frames, sum-of-square differences between optical-flow-compensated frames, and hue histogram changes within frames are analyzed and stored in frame buffers. A feature vector formed from a combination of these measurements is compared to a feature vector formed from thresholds based on tunable recall and precision to declare the presence or absence of a shot change.