Vijay Mahadevan has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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.
December 22, 2006
June 26, 2008
Khaled Helmi El-Maleh, Vijay Mahadevan, Haohong Wang
Abstract: This disclosure is directed to techniques for selective video frame rate upconversion (FRUC) in a video decoder. A video decoder selectively enables or disables FRUC based on one or more adaptive criteria. The adaptive criteria may be selected to indicate whether FRUC is likely to introduce spatial artifacts. Adaptive criteria may include a motion activity threshold, a mode decision threshold, or both. The criteria are adaptive, rather than fixed. When the criteria indicate that a frame includes excessive motion or new content, the decoder disables FRUC.
Abstract: The disclosure is directed to techniques for evaluating temporal quality of encoded video. Instead of estimating jerkiness based solely on frame rate or motion activity, the number of consecutive dropped frames forms a basic estimation unit. Several human visual system factors, such as sensitivity to temporal quality fluctuation and motion activity, have been taken into account to make the predicted jerkiness more consistent with the actual human visual response. The temporal quality metric can be used to estimate human perceived discomfort that is introduced by temporal discontinuity under various combinations of video shots, motion activity and local quality fluctuations. The techniques can be applied in two modes: (1) bitstream or (2) pixel mode. The quality metric can be used to evaluate temporal quality, or to control encoding or decoding characteristics to enhance temporal quality.