A disclosed system may include a hardware distortion data pipeline that may include (1) a quantization module that generates a quantized data set, (2) an inverse quantization module that generates, from the quantized data set, an inverse quantized data set by executing an inverse quantization of the quantized data set, and (3) an inverse transformation module that generates an inversely transformed data set by executing an inverse transformation of the inverse quantized data set. The system may also include a hardware determination pipeline that determines a distortion metric based on the inversely transformed data set and the residual frame data set, and a hardware token rate pipeline that determines, based on the quantized data set, a token rate for an encoding of the residual frame data set via a video encoding pipeline. Various other methods, systems, and computer-readable media are also disclosed.

A disclosed system may include a hardware distortion data pipeline that may include (1) a quantization module that generates a quantized data set, (2) an inverse quantization module that generates, from the quantized data set, an inverse quantized data set by executing an inverse quantization of the quantized data set, and (3) an inverse transformation module that generates an inversely transformed data set by executing an inverse transformation of the inverse quantized data set. The system may also include a hardware determination pipeline that determines a distortion metric based on the inversely transformed data set and the residual frame data set, and a hardware token rate pipeline that determines, based on the quantized data set, a token rate for an encoding of the residual frame data set via a video encoding pipeline. Various other methods, systems, and computer-readable media are also disclosed.

Various concepts which further improve multi-view/layer coding concepts, are described.

Various concepts which further improve multi-view/layer coding concepts, are described.

Described is picture segmentation through columns and slices in video encoding and decoding. A video picture is divided into a plurality of columns, each column covering only a part of the video picture in a horizontal dimension. All coded tree blocks (“CTBs”) belonging to a slice may belong to one or more columns. The columns may be used to break the same or different prediction or in-loop filtering mechanisms of the video coding, and the CTB scan order used for encoding and/or decoding may be local to a column. Column widths may be indicated in a parameter set and/or may be adjusted at the slice level. At the decoder, column width may be parsed from the bitstream, and slice decoding may occur in one or more columns.

Described is picture segmentation through columns and slices in video encoding and decoding. A video picture is divided into a plurality of columns, each column covering only a part of the video picture in a horizontal dimension. All coded tree blocks (“CTBs”) belonging to a slice may belong to one or more columns. The columns may be used to break the same or different prediction or in-loop filtering mechanisms of the video coding, and the CTB scan order used for encoding and/or decoding may be local to a column. Column widths may be indicated in a parameter set and/or may be adjusted at the slice level. At the decoder, column width may be parsed from the bitstream, and slice decoding may occur in one or more columns.

In a method and a device for encoding or decoding an image according to the present invention, motion information for bidirectional prediction of a current block may be derived on the basis of an inter mode previously defined in the device for encoding or decoding an image, and inter prediction may be performed on the current block on the basis of the motion information, wherein the motion information for bidirectional prediction is adjusted to be motion information for unidirectional prediction according to the predefined inter mode.

A decoding device includes a controller classifying a bitstream as a first bitstream and a second bitstream based on a plurality of blocks defined by a matrix and included in a frame, a first decoder including a first processor performing decoding on the first bitstream and outputting first decoding data and a first memory, a second decoder including a second processor performing decoding on the second bitstream and outputting second decoding data and a second memory, a first buffer transmitting the first decoding data to the second memory, and a second buffer transmitting the second decoding data to the first memory. The first processor controls the second memory to store the first decoding data, and the second processor controls the first memory to store the second decoding data.

A method of decoding an image based on cross-channel prediction using artificial intelligence (AI) includes obtaining cross-channel prediction information by applying feature data for cross-channel prediction to a neural-network-based cross-channel decoder, obtaining a predicted image of a chroma image by performing cross-channel prediction based on a reconstructed luma image and the cross-channel prediction information, obtaining a residual image of the chroma image by applying feature data of the chroma image to a neural-network-based chroma residual decoder, and reconstructing the chroma image based on the predicted image and the residual image.

A method of decoding an image based on cross-channel prediction using artificial intelligence (AI) includes obtaining cross-channel prediction information by applying feature data for cross-channel prediction to a neural-network-based cross-channel decoder, obtaining a predicted image of a chroma image by performing cross-channel prediction based on a reconstructed luma image and the cross-channel prediction information, obtaining a residual image of the chroma image by applying feature data of the chroma image to a neural-network-based chroma residual decoder, and reconstructing the chroma image based on the predicted image and the residual image.