Abstract: In a decoder, a desired image is estimated by first retrieving coding modes from an encoded side information image. For each bitplane in the encoded side information image, syndrome bits or parity bits are decoded to obtain an estimated bitplane of quantized transform coefficients of the desired image. A quantization and a transform are applied to a prediction residual obtained using the coding modes, wherein the decoding uses the quantized transform coefficients of the encoded side information image, and is based on previously decoded bitplanes in a causal neighborhood. The estimated bitplanes of quantized transform coefficients of the desired image are combined to produce combined bitplanes. Then, an inverse quantization, an inverse transform and a prediction based on the coding modes are applied to the combined bitplanes to recover the estimate of the desired image.

Abstract: In a decoder, a desired image is estimated by first retrieving coding modes from an encoded side information image. For each bitplane in the encoded side information image, syndrome bits or parity bits are decoded to obtain an estimated bitplane of quantized transform coefficients of the desired image. A quantization and a transform are applied to a prediction residual obtained using the coding modes, wherein the decoding uses the quantized transform coefficients of the encoded side information image. The estimated bitplanes of quantized transform coefficients of the desired image are combined to produce combined bitplanes. Then, an inverse quantization, an inverse transform and a prediction based on the coding modes are applied to the combined bitplanes to recover the estimate of the desired image.

Abstract: A method codes pictures in a bitstream, wherein the bitstream includes coded pictures to obtain data for associated TUs and data for generating a transform tree, and a partitioning of coding units (CUs) into Prediction Units (PUs), and data for obtaining prediction modes or directions associated with each PU. One or more mapping tables are defined, wherein each row of each table has an associated index and a first set of transform types to be used for applying an inverse transformation to the data in TU. The first set of transform types is selected according to an index, and then a second set of transform types is applied as the inverse transformation to the data, wherein the second set of transform types is determined according to the first set of transform types and a transform-toggle flag (ttf) to obtain a reconstructed prediction residual.

Abstract: A disparity vector for a pixel in a right image corresponding to a pixel in a left image in a pair of stereo images is determined. The disparity vector is based on a horizontal disparity and a vertical disparity and the pair of stereo images is unrectified. First, a set of candidate horizontal disparities is determined. For each candidate horizontal disparity, a cost associated with a particular horizontal disparity and corresponding vertical disparities is determined. The vertical disparity associated with a first optimal cost is assigned to each candidate horizontal disparity, so that the candidate horizontal disparity and the vertical disparity yield a candidate disparity vector. Lastly, the candidate disparity vector with a second optimal cost is selected as the disparity vector of the pixel in the right image.

Abstract: A bitstream corresponding to an encoded video is decoded. The encoded video includes a sequence of frames, and each frame is partitioned into encoded blocks. For each encoded block, an edge mode index is decoded based on an edge mode codeword and a prediction mode. The edge mode index indicates a subset of predetermined partitions selected from a partition library according to the prediction mode. The encoded block is partitioned based on the edge mode index to produce two or more block partitions. To each block partition, a coefficient rearrangement, an inverse transform and an inverse quantization is applied to produce a processed block partition. The processed block partitions are then combined into a decoded block for a video.

Abstract: Multiview videos are acquired by overlapping cameras. Side information is used to synthesize multiview videos. A reference picture list is maintained for current frames of the multiview videos, the reference picture indexes temporal reference pictures and spatial reference pictures of the acquired multiview videos and the synthesized reference pictures of the synthesized multiview video. Each current frame of the multiview videos is predicted according to reference pictures indexed by the associated reference picture list with a skip mode and a direct mode, whereby the side information is inferred from the synthesized reference picture. Alternatively, the depth images corresponding to the multiview videos of the input data, and this data are encoded as part of the bitstream depending on a SKIP type.

Abstract: Multiview videos are acquired by overlapping cameras. Side information is used to synthesize multiview videos. A reference picture list is maintained for current frames of the multiview videos, the reference picture indexes temporal reference pictures and spatial reference pictures of the acquired multiview videos and the synthesized reference pictures of the synthesized multiview video. Each current frame of the multiview videos is predicted according to reference pictures indexed by the associated reference picture list with a skip mode and a direct mode, whereby the side information is inferred from the synthesized reference picture. In addition, the skip and merge modes for single view video coding are modified to support multiview video coding by generating a motion vector prediction list by also considering neighboring blocks that are associated with synthesized reference pictures.

Abstract: A system and a method for decoding at least a portion of an image includes determining a current prediction mode based on a combination of a prediction mode residue and a function of at least one previous prediction mode and decoding the portion of the image using the current prediction mode.

Abstract: Specifically, a method decodes a picture. The picture is encoded and represented by blocks in a bitstream. For each block, a remap flag is obtained from the bit-stream. The block is either a remapped reconstructed block or a non-remapped reconstructed block. Either the non-mapped reconstructed block or an inverse remapped reconstructed block is output according to the remap flag. The remapped reconstructed block maximizes a similarity with the neighboring blocks, as compared to the similarity of the non-mapped reconstructed block and the neighboring blocks, by applying point operations to the remapped reconstructed block.

Abstract: A video encoded as a bit stream is decoded by maintaining a set of dictionaries generated from decoded prediction residual signals, wherein elements of the set of dictionaries have associated indices. A current macroblock is entropy decoded and inverse quantized to produce decoded coefficients. For the current macroblock, a particular dictionary of the set of dictionaries is selected according to a prediction mode signaled in the bit stream, and particular elements of the particular dictionary are selected according to a copy mode signal in the bit stream and the associated index. The particular elements is scaled and combined, using the decoded coefficients, to reconstruct a current decoded macroblock prediction residual signal. Then, the current decoded macroblock prediction residual signal is combined with previously decoded macroblocks to generate an output macroblock of a reconstructed video, wherein the steps are performed in a decoder.

Abstract: Blocks in pixel images are template matched to select candidate blocks and weights according to a structural similarity and a perceptual distortion of the blocks. The perceptual distortion is a function of a just-noticeable-distortion (JND). A filter outputs a prediction residual between the block and the candidate blocks. The prediction residual is transformed and quantized to produce a quantized prediction residual using the JND. The matching and quantizing is optimized jointly using the perceptual distortion. Then, the quantized prediction residual and the weights are entropy encoded into a bit-stream for later decoding.

Abstract: A bitstream includes coded pictures, and split-flags for generating a transform tree. The bit stream is a partitioning of coding units (CUs) into Prediction Units (PUs). The transform tree is generated according to the split-flags. Nodes in the transform tree represent transform units (TU) associated with the CUs. The generation splits each TU only if the corresponding split-flag is set. For each PU that includes multiple TUs, the multiple TUs are merged into a larger TU, and the transform tree is modified according to the splitting and merging. Then, data contained in each PU can be decoded using the TVs associated with the PU according to the transform tree.

Abstract: A disparity vector for a pixel in a right image corresponding to a pixel in a left image in a pair of stereo images is determined. The disparity vector is based on a horizontal disparity and a vertical disparity and the pair of stereo images is unrectified. First, a set of candidate horizontal disparities is determined. For each candidate horizontal disparity, a cost associated with a particular horizontal disparity and corresponding vertical disparities is determined. The vertical disparity associated with a first optimal cost is assigned to each candidate horizontal disparity, so that the candidate horizontal disparity and the vertical disparity yield a candidate disparity vector. Lastly, the candidate disparity vector with a second optimal cost is selected as the disparity vector of the pixel in the right image.

Abstract: A quality of a virtual image for a synthetic viewpoint in a 3D scene is determined. The 3D scene is acquired by texture images, and each texture image is associated with a depth image acquired by a camera arranged at a real viewpoint. A texture noise power is based on the acquired texture images and reconstructed texture images corresponding to a virtual texture image. A depth noise power is based on the depth images and reconstructed depth images corresponding to a virtual depth image. The quality of the virtual image is based on a combination of the texture noise power and the depth noise power, and the virtual image is rendered from the reconstructed texture images and the reconstructed depth images.

Abstract: Videos of a scene are processed for view synthesis. The videos are acquired by corresponding cameras arranged so that a view of each camera overlaps with the view of at least one other camera. For each current block, motion or disparity vector is obtained from neighboring blocks. A depth block is based on a corresponding reference depth image and the motion or disparity vector. A prediction block is generated based on the depth block using backward warping. Then, predictive coding for the current block using the prediction block.

Abstract: Embodiments of the invention disclose a system and a method for transforming a biometric of a user to a binary feature vector suitable for user authentication, comprising steps of: partitioning the biometric into a set of regions, wherein each region is a contiguous region confining a part of the biometric; determining, for each region, biometric parameters; applying, to each region, binary logical conditions to produce at least one bit of the binary feature vector, wherein the applying is performed by a processer; and outputting the binary feature vector.

Abstract: Embodiments disclose a method and a system for determining securely the Manhattan distance between a first and a second signal. The system is mapping the first signal to a first binary signal; mapping the second signal to a second binary signal, such that the squared distance between the first signal and the second binary signals equals the Manhattan distance; reducing respectively dimensions of the first binary signal and the second binary signal to produce a first low dimensional signal and a second low dimensional signal, such that the squared distance between the first low dimensional signal and the second low dimensional signals approximates the squared distance between the first binary signal and the second binary signals; and determining securely the squared distance between the first low dimensional signal and the second low dimensional signals to securely determine the Manhattan distance between the first signal and the second signal.

Abstract: Embodiments of the invention disclose a system and a method for embedding a symbol in a glyph, comprising the steps of determining a set of landmarks representing an outline of the glyph; determining a data segment between two landmarks, wherein the data segment is suitable for embedding the symbol; modifying the data segment according to the symbol to produce a modified glyph such that the symbol is embedded in the modified glyph; and outputting the modified glyph.

Abstract: A video encoded as a bit stream is decoded using trained sparse orthonormal transforms generated from decoded prediction residual signals, wherein the transforms have associated indices. A current macroblock is entropy decoded and inverse quantized to produce decoded coefficients. For the current macroblock, an L0-norm regularized training process generates a set of mode-dependent sparse orthonormal inverse transforms, selected according to a prediction mode signaled in the bit stream. Decoded coefficients are inverse transformed and then are combined with previously decoded macroblocks to generate an output macroblock of a reconstructed video.

Abstract: A synthetic image block in a synthetic picture is generated for a viewpoint based on a texture image and a depth image. A subset of samples from the texture image are warped to the synthetic image block. Disoccluded samples are marked, and the disoccluded samples in the synthetic image block are filled based on samples in a constrained area. The method and system enables both picture level and block level processing for synthetic reference picture generation. The method can be used for power limited devices, and can also refine the synthetic reference picture quality at a block level to achieve coding gains.