Abstract: A method for converting a frame rate of an input video is provided. The method includes performing a motion estimation by generating at least one motion field for at least one pair of frames of the input video, wherein the at least one motion field includes a set of motion vectors for each block of a first reference frame of the input video, the set of motion vectors indicating to a second reference frame of the input video, and preparing first data for a predetermined interpolation phase for each block of an interpolated frame, where the first data include at least one parameter for a pre-trained occlusion processing (OcC) convolutional neural network (CNN), the at least one parameter being obtained based on the at least one motion field.

Abstract: A video frame rate conversion (FRC) method, including obtaining at least one pair of frames of a video; partitioning each frame into a plurality of blocks; estimating motion a block-by-block basis to obtain: a forward motion field including a set of first motion vectors, and a backward motion field including a set of second motion vectors; obtaining occlusions based on the forward motion field and the backward motion field; and calculating an artifact probability metric indicating a probability of an appearance of an artifact in the motion-compensated frame interpolation between the first frame and the second frame.

Abstract: A method for converting a frame rate of an input video is provided. The method includes performing a motion estimation by generating at least one motion field for at least one pair of frames of the input video, wherein the at least one motion field includes a set of motion vectors for each block of a first reference frame of the input video, the set of motion vectors indicating to a second reference frame of the input video, and preparing first data for a predetermined interpolation phase for each block of an interpolated frame, where the first data include at least one parameter for a pre-trained occlusion processing (OcC) convolutional neural network (CNN), the at least one parameter being obtained based on the at least one motion field.

Abstract: A motion estimation method for video data including frames and an apparatus therefor are provided. The motion estimation method includes determining whether a current frame unit for which motion estimation is to be performed corresponds to a double block, when the current frame unit corresponds to a double block, acquiring a candidate vector set corresponding to a first single block included in the double block, as a candidate vector set of the double block, individually calculating a confidence function value of each candidate vector included in the candidate vector set of the double block, for the first single block and a second single block included in the double block, and acquiring an estimated motion vector of the first single block and an estimated motion vector of the second single block, based on the calculated confidence function value of each candidate vector.

Abstract: A motion estimation method for video data including frames and an apparatus therefor are provided. The motion estimation method includes determining whether a current frame unit for which motion estimation is to be performed corresponds to a double block, when the current frame unit corresponds to a double block, acquiring a candidate vector set corresponding to a first single block included in the double block, as a candidate vector set of the double block, individually calculating a confidence function value of each candidate vector included in the candidate vector set of the double block, for the first single block and a second single block included in the double block, and acquiring an estimated motion vector of the first single block and an estimated motion vector of the second single block, based on the calculated confidence function value of each candidate vector.

Abstract: A technology for processing digital signals is provided, and more particularly, a method of encoding or decoding a multiview video sequence based on adaptive compensation of local illumination mismatches between frames of the multiview video sequence is provided. The method may be performed using a correction parameter used to correct a change in illumination. The correction parameter may be computed based on at least one of a first estimate estDi,j, a second estimate estRi,j, a value Ri,j of a pixel of a reference block, and values TDk and TRk of restored pixels.

Abstract: A technology for processing digital signals is provided, and more particularly, a method of encoding or decoding a multiview video sequence based on adaptive compensation of local illumination mismatches between frames of the multiview video sequence is provided. The method may be performed using a correction parameter used to correct a change in illumination. The correction parameter may be computed based on at least one of a first estimate estDi,j, a second estimate estRi,j, a value Ri,j of a pixel of a reference block, and values TDk and TRk of restored pixels.

Abstract: Provided is an illumination and contrast compensation method applied to the frames comprising multi-view video sequence. Relations between the values of the pixels of the reference block and the values of the pixels neighboring the reference block and relations between the restored values of the pixels neighboring the current block and the values of the pixels neighboring the reference block is determined. An illumination and contrast compensation parameters for illumination and contrast compensation of discrepancy (mismatch) compensation between reference and encoded blocks is determined on the basis of the determined relations, values of the pixels of the reference block, restored values of the pixels neighboring the current block and values of the pixels neighboring the reference block.