Abstract: The present disclosure provides a method and a device for image quality enhancement based on a super-resolution neural network. The present disclosure in at least one embodiment provides an image quality enhancement method optimized for distortion characteristics of a target image, including generating one or more training datasets with one or more distortions, obtaining training distortion characteristic values respectively by inputting the one or more training datasets into a degradation encoder neural network (DEN), obtaining a service distortion characteristic value by inputting a service dataset comprising image patches of the target image into the degradation encoder neural network, computing a similarity between each of the training distortion characteristic values and the service distortion characteristic value, and selecting the training dataset having the highest similarity to the service distortion characteristic value.

Abstract: The present disclosure relates to video encoding or decoding and, more specifically, to an apparatus and a method for applying an artificial neural network (ANN) to video encoding or decoding. The apparatus and the method of the present disclosure are characterized by applying a CNN-based filter to a first picture and at least one of a quantization parameter map and a block partition map to output a second picture.

Abstract: The present disclosure relates to video encoding or decoding and, more specifically, to an apparatus and a method for applying an artificial neural network (ANN) to video encoding or decoding. The apparatus and the method of the present disclosure are characterized by applying a CNN-based filter to a first picture and at least one of a quantization parameter map and a block partition map to output a second picture.

Abstract: The present disclosure relates to video encoding or decoding and, more specifically, to an apparatus and a method for applying an artificial neural network (ANN) to video encoding or decoding. The apparatus and the method of the present disclosure are characterized by applying a CNN-based filter to a first picture and at least one of a quantization parameter map and a block partition map to output a second picture.

Abstract: The present disclosure relates to video encoding or decoding and, more specifically, to an apparatus and a method for applying an artificial neural network (ANN) to video encoding or decoding. The apparatus and the method of the present disclosure are characterized by applying a CNN-based filter to a first picture and at least one of a quantization parameter map and a block partition map to output a second picture.

Abstract: Disclosed herein is a method for decoding a video including determining a coding unit to be decoded by block partitioning, decoding prediction syntaxes for the coding unit, the prediction syntaxes including a skip flag indicating whether the coding unit is encoded in a skip mode, after the decoding of the prediction syntaxes, decoding transform syntaxes including a transformation/quantization skip flag and a coding unit cbf, wherein the transformation/quantization skip flag indicates whether inverse transformation, inverse quantization, and at least part of in-loop filterings are skipped, and the coding unit cbf indicates whether all coefficients in a luma block and two chroma blocks constituting the coding unit are zero, and reconstructing the coding unit based on the prediction syntaxes and the transform syntaxes.

Abstract: Disclosed herein is a method for adaptive bidirectional optical flow estimation for inter prediction compensation during video encoding. The method aims to reduce complexity and/or cost of bidirectional optical flow (BIO) at a pixel level or a subblock level.

Abstract: Disclosed herein is a method for adaptive bidirectional optical flow estimation for inter prediction compensation during video encoding. The method aims to reduce complexity and/or cost of bidirectional optical flow (BIO) at a pixel level or a subblock level.

Abstract: The present disclosure relates to video encoding or decoding and, more specifically, to an apparatus and a method for applying an artificial neural network (ANN) to video encoding or decoding. The apparatus and the method of the present disclosure are characterized by applying a CNN-based filter to a first picture and at least one of a quantization parameter map and a block partition map to output a second picture.

Abstract: Disclosed herein is a method for adaptive bidirectional optical flow estimation for inter prediction compensation during video encoding. The method aims to reduce complexity and/or cost of bidirectional optical flow (BIO) at a pixel level or a subblock level.

Abstract: Disclosed herein is a method for adaptive bidirectional optical flow estimation for inter prediction compensation during video encoding. The method aims to reduce complexity and/or cost of bidirectional optical flow (BIO) at a pixel level or a subblock level.

Abstract: Disclosed herein is a method for decoding a video including determining a coding unit to be decoded by block partitioning, decoding prediction syntaxes for the coding unit, the prediction syntaxes including a skip flag indicating whether the coding unit is encoded in a skip mode, after the decoding of the prediction syntaxes, decoding transform syntaxes including a transformation/quantization skip flag and a coding unit cbf, wherein the transformation/quantization skip flag indicates whether inverse transformation, inverse quantization, and at least part of in-loop filterings are skipped, and the coding unit cbf indicates whether all coefficients in a luma block and two chroma blocks constituting the coding unit are zero, and reconstructing the coding unit based on the prediction syntaxes and the transform syntaxes.

Abstract: Disclosed herein is video encoding or decoding for efficiently encoding video. The techniques of the present disclosure are related to various split shapes of a block, syntaxes representing various split types of blocks, and syntax elements represented at a high level therefor.

Abstract: Disclosed herein is a method for decoding a video including determining a coding unit to be decoded by block partitioning, decoding prediction syntaxes for the coding unit, the prediction syntaxes including a skip flag indicating whether the coding unit is encoded in a skip mode, after the decoding of the prediction syntaxes, decoding transform syntaxes including a transformation/quantization skip flag and a coding unit cbf, wherein the transformation/quantization skip flag indicates whether inverse transformation, inverse quantization, and at least part of in-loop filterings are skipped, and the coding unit cbf indicates whether all coefficients in a luma block and two chroma blocks constituting the coding unit are zero, and reconstructing the coding unit based on the prediction syntaxes and the transform syntaxes.

Abstract: Disclosed herein is a method for adaptive bidirectional optical flow estimation for inter-screen prediction compensation during video encoding. The method aims to reduce complexity and/or cost of bidirectional optical flow (BIO) at a pixel level or a subblock level.

Abstract: Disclosed herein is a method for adaptive bidirectional optical flow estimation for inter-screen prediction compensation during video encoding. The method aims to reduce complexity and/or cost of bidirectional optical flow (BIO) at a pixel level or a subblock level.

Abstract: Disclosed herein is a method for adaptive bidirectional optical flow estimation for inter-screen prediction compensation during video encoding. The method aims to reduce complexity and/or cost of bidirectional optical flow (BIO) at a pixel level or a subblock level.

Abstract: Disclosed herein is a method for adaptive bidirectional optical flow estimation for inter-screen prediction compensation during video encoding. The method aims to reduce complexity and/or cost of bidirectional optical flow (BIO) at a pixel level or a subblock level.

Abstract: Disclosed herein is video encoding or decoding for efficiently encoding video. The techniques of the present disclosure are related to various split shapes of a block, syntaxes representing various split types of blocks, and syntax elements represented at a high level therefor.

Abstract: Disclosed herein is video encoding or decoding for efficiently encoding video. The techniques of the present disclosure are related to various split shapes of a block, syntaxes representing various split types of blocks, and syntax elements represented at a high level therefor.