Abstract: Techniques are described in which pixel intensity values of a display of a computing device may be nonlinearly scaled down to reduce the power consumption of the display. The computing device may determine a compressed pixel brightness range for the display. The computing device may determine a set of initial scaled pixel intensity levels based at least in part on the compressed pixel intensity range. The computing device may determine a set of scaled pixel intensity values based at least in part on an estimated power consumption value associated with the set of initial scaled pixel intensity levels. The computing device may scale the pixel brightness of pixels of the display from one of a plurality of pixel intensity levels to a corresponding scaled pixel intensity level of the set of scaled pixel intensity levels.

Abstract: Techniques are described in which pixel intensity values of a display of a computing device may be nonlinearly scaled down to reduce the power consumption of the display. The computing device may determine a compressed pixel brightness range for the display. The computing device may determine a set of initial scaled pixel intensity levels based at least in part on the compressed pixel intensity range. The computing device may determine a set of scaled pixel intensity values based at least in part on an estimated power consumption value associated with the set of initial scaled pixel intensity levels. The computing device may scale the pixel brightness of pixels of the display from one of a plurality of pixel intensity levels to a corresponding scaled pixel intensity level of the set of scaled pixel intensity levels.

Abstract: In an example, a method for image processing may include inputting a first pixel value corresponding to a first pixel of an image into a LUT. The LUT may map one or more LUT input values to one or more LUT output values. The first pixel value may correspond to a first LUT input value that maps to a first LUT output value in the LUT. The first pixel may include one or more pixel values. The method may include generating a noise value for the first LUT input value. The method may include generating a first interpolated LUT output value for the first LUT input value based on the noise value. The method may include transforming the image into a transformed image using the first interpolated LUT output value.

Abstract: In an example, a method for image processing may include inputting a first pixel value corresponding to a first pixel of an image into a LUT. The LUT may map one or more LUT input values to one or more LUT output values. The first pixel value may correspond to a first LUT input value that maps to a first LUT output value in the LUT. The first pixel may include one or more pixel values. The method may include generating a noise value for the first LUT input value. The method may include generating a first interpolated LUT output value for the first LUT input value based on the noise value. The method may include transforming the image into a transformed image using the first interpolated LUT output value.

Abstract: This disclosure describes techniques for processing motion vectors such that the resulting motion vectors better correlate with the true motion of a video frame. In one example, the techniques may include comparing a block motion vector corresponding to a video block to a sub-block motion vector corresponding to a sub-block contained within the video block. The techniques may further include selecting one of the block motion vector and the sub-block motion vector as a spatially-estimated motion vector for the sub-block based on the comparison. Motion vectors that better correlate with true motion may be useful in applications such as motion compensated frame interpolation (MCI), moving object tracking, error concealment, or other video post-processing that requires the true motion information.

Abstract: A preferred method for hierarchical motion vector processing determines reliability levels of blocks in image data according to residual energy levels. Macroblocks of an image frame are merged according to reliability levels of the motion vectors of blocks. Single motion vectors are selected for merged macroblocks. Motion vectors of blocks merged in the step of merging are iteratively assigned by minimizing the bi-directional prediction difference on successively smaller merged blocks. The reliability levels are preferably determined by measure residual energy of both chrominance and luminance components. In preferred embodiments, motion vector correlation is used to assist the MV reliability classification and the merging and iterative assignment. Refinement and smoothing can be conducted on successively finer block sizes. Additionally, preferred methods account for occlusions by choosing only one of forward or backward prediction for occlusion regions depending upon the class of the occlusion.

Abstract: A preferred method for hierarchical motion vector processing determines reliability levels of blocks in image data according to residual energy levels. Macroblocks of an image frame are merged according to reliability levels of the motion vectors of blocks. Single motion vectors are selected for merged macroblocks. Motion vectors of blocks merged in the step of merging are iteratively assigned by minimizing the bi-directional prediction difference on successively smaller merged blocks. The reliability levels are preferably determined by measure residual energy of both chrominance and luminance components. In preferred embodiments, motion vector correlation is used to assist the MV reliability classification and the merging and iterative assignment. Refinement and smoothing can be conducted on successively finer block sizes. Additionally, preferred methods account for occlusions by choosing only one of forward or backward prediction for occlusion regions depending upon the class of the occlusion.

Abstract: This disclosure describes techniques for processing motion vectors such that the resulting motion vectors better correlate with the true motion of a video frame. In one example, the techniques may include comparing a block motion vector corresponding to a video block to a sub-block motion vector corresponding to a sub-block contained within the video block. The techniques may further include selecting one of the block motion vector and the sub-block motion vector as a spatially-estimated motion vector for the sub-block based on the comparison. Motion vectors that better correlate with true motion may be useful in applications such as motion compensated frame interpolation (MCI), moving object tracking, error concealment, or other video post-processing that requires the true motion information.