Abstract:
Provided are a method and apparatus for estimating a motion vector using a plurality of motion vector predictors, an encoder, a decoder, and a decoding method. The method includes calculating spatial similarities between the current block and the plurality of neighboring partitions around the current block, selecting at least one of the neighboring partitions based on the calculated spatial similarities, and estimating a motion vector of the selected partition as the motion vector of the current block.
Abstract:
Provided are a method and apparatus for estimating a motion vector using a plurality of motion vector predictors, an encoder, a decoder, and a decoding method. The method includes calculating spatial similarities between the current block and the plurality of neighboring partitions around the current block, selecting at least one of the neighboring partitions based on the calculated spatial similarities, and estimating a motion vector of the selected partition as the motion vector of the current block.
Abstract:
An image decoding method including determining coding units having a hierarchical structure for decoding an image using split information of a coding unit, determining at least one prediction unit for predicting a coding unit among the coding units using information about a partition type, determining at least one transformation unit for inversely transforming the coding unit using split information of the at least one transformation unit, wherein the split information of a coding unit, the information about a partition type and the information about a depth of the at least one transformation unit are parsed from a bitstream, parsing from the bitstream transformation coefficients generated by transformation according to the at least one transformation unit generated by dividing the coding unit, reconstructing residual of the at least one transformation unit by performing inverse quantization and inverse transformation on the parsed transformation coefficients, and performing intra prediction or inter prediction on the prediction unit to generate a predictor, and reconstructs the image based on the residual and the predictor.
Abstract:
An apparatus for decoding an image includes an entropy decoder that performs entropy decoding to generate quantized transformation coefficients of a transformation unit in a coding unit and an inverse transformer that inverse quantizes the quantized transformation coefficients to generate transformation coefficients of the transformation unit and inverse transforms the transformation coefficients to generate residual components of the transformation unit.
Abstract:
An apparatus for decoding an image, the apparatus including an entropy decoder that performs entropy-decoding to obtain quantized transformation coefficients of at least one transformation unit in a coding unit of the image, a decoder that determines a prediction mode of at least one prediction unit in the coding unit from information indicating the prediction mode for the at least one prediction unit, when the prediction mode is determined to be an inter prediction mode, not in an intra prediction mode, determines a size of the at least one transformation unit in the coding unit regardless of a size of the at least one prediction unit in the coding unit, and performs inverse-quantization and inverse-transformation on the quantized transformation coefficients of the at least one transformation unit to obtain residuals, and a restorer that performs inter prediction for at least one prediction unit in the coding unit to generate a predictor and restores the image by using the residuals and the predictor.
Abstract:
Disclosed are an image encoding method and apparatus for encoding an image by grouping a plurality of adjacent prediction units into a transformation unit and transforming the plurality of adjacent prediction into a frequency domain, and an image decoding method and apparatus for decoding an image encoded by using the image encoding method and apparatus.
Abstract:
Provided are methods and apparatuses for encoding and decoding a motion vector including a method of decoding that includes obtaining a current coding unit by hierarchically split from a maximum coding unit according to a current depth, obtaining a prediction mode information of a current coding unit from bitstream, determining motion vector predictor candidates from among motion vectors of adjacent coding unit adjacent to the current coding unit, and determining a motion vector predictor of the current coding unit from among the motion vector predictor candidates based on prediction mode information of the current coding unit, wherein the adjacent coding unit comprise a first block outside the current coding unit located on a lower-left side of the current coding unit.
Abstract:
Provided are methods and apparatuses for encoding and decoding a motion vector. The method of encoding the motion vector includes: selecting, as a mode of encoding information about a motion vector predictor of the current block, a first mode in which information indicating the motion vector predictor from among at least one motion vector predictor is encoded or a second mode in which information indicating generation of the motion vector predictor based on blocks or pixels included in a previously encoded area adjacent to the current block is encoded; determining the motion vector predictor of the current block according to the selected mode and encoding the information about the motion vector predictor of the current block; and encoding a difference vector between the motion vector of the current block and the motion vector predictor of the current block.
Abstract:
An image processing apparatus, including a processor configured to execute instructions stored in a memory to: obtain characteristic information of a first image, divide the characteristic information into a plurality of groups, input each group into a respective layer of a plurality of layers included in a convolutional neural network and perform a convolution operation using one or more kernels to obtain a plurality of pieces of output information, generate an attention map including weight information corresponding to each pixel included in the first image, based on the plurality of pieces of output information, generate a spatially variant kernel including a kernel corresponding to the each pixel, based on the attention map and a spatial kernel including weight information according to a position relationship between the each pixel and a neighboring pixel, and generate a second image by applying the spatially variant kernel to the first image.
Abstract:
An audio processing apparatus may obtain second audio signals corresponding to channels included in a second channel group from first audio signals corresponding to channels included in a first channel group, downsample at least one third audio signal corresponding to at least one channel identified based on a correlation with the second channel group from among the channels included in the first channel group, by using an artificial intelligence (AI) model, and generate a bitstream including the second audio signals corresponding to the channels included in the second channel group and the downsampled at least one third audio signal. The first channel group includes a channel group of an original audio signal, and the second channel group is constructed by combining at least two channels from among the channels included in the first channel group.