Abstract: There is provided a method for decoding a video frame. The method comprises receiving an encoded video frame sequence data associated with a video frame sequence, wherein the video frame sequence includes the video frame. The method comprises deriving from the received encoded video frame sequence data (i) sample value information of one or more blocks and/or (ii) block information about said one or more blocks. The method further comprises, based on (i) the derived sample value information and/or (ii) the derived block information, selecting from a set of machine learning, ML, models at least one ML model to use for decoding the video frame. The method comprises decoding the video frame using the selected at least one ML model.

Abstract: There is provided a method. The method comprises obtaining a weighting factor. The method comprises obtaining input sample values. The method further comprises determining neural network, NN, filtered sample values by providing the input sample values to a neural network. The method comprises determining revised sample values based on (i) the weighting factor and (ii) the NN filtered sample values.

Abstract: A method for encoding or decoding an image is provided. The method comprises obtaining pixel values of pixels included in the image. The method further comprises converting the pixel values into convoluted values using a convolution network that comprises a first convolution layer, wherein the first convolution layer is configured to receive first input values and generate first output values using a convolution operation. The method further comprises obtaining first quality values, and (i) combining the first quality values with the pixel values, thereby generating the first input values or (ii) combining the first quality values with the first output values, thereby generating first combined values.

Abstract: There is provided a method of training a machine learning, ML, model used for generating encoded video data or decoded video data. The method comprises obtaining original video data. The method further comprises converting the original video data into ML input video data. The method further comprises providing the ML input video data into the ML model, thereby generating first ML output video data, and training the ML model based on a difference between the original video data and the ML input video data and a difference between the original video data and the first ML output video data.

Abstract: There is provided a method of selecting from a picture a patch for training a machine learning, ML, model used for encoding or decoding video data. The method comprises randomly selecting one or more coordinates of the patch, and converting said one or more coordinates of the patch into converted one or more coordinates of the patch. The method further comprises training the ML model based on the converted one or more coordinates of the patch. Each of said one or more converted coordinates of the patch is an integer multiple of 2p, where p is an integer.

Abstract: There is provided a method. The method comprises obtaining an input video frame data associated with an input video frame. The method comprises performing a deblocking filtering operation on one or more samples included in the input video frame, thereby generating one or more deblocked samples. The method further comprises performing a machine-learning (ML)-based filtering operation and/or adaptive loop filtering operation on one or more samples included in the input video frame, thereby generating one or more filtered samples. The method comprises, using said one or more deblocked samples and/or said one or more filtered samples, producing encoded or decoded video frame data including an encoded or decoded video frame.

Abstract: There is provided a method for processing a bitstream. The method comprises determining a value, N, wherein N identifies a number of ordered layer representations, wherein N is greater than or equal to 3 such that the N ordered layer representations comprises a highest layer representation, a second highest layer representation, and a third highest layer representation. The method further comprises determining a value for the highest layer representation. The method comprises, after determining the value for the highest layer representation and before determining a value for the third highest layer representation, determining a value for the second highest layer representation. The method comprises, after determining the value for the second highest layer representation, determining a value for the third highest layer representation.

Abstract: A method for generating an encoded video or a decoded video is provided. The method comprises obtaining values of reconstructed samples, and obtaining input information comprising any one or a combination of: i) information about filtered samples, ii) information about predicted samples, or iii) information about skipped samples. The method further comprises providing the values of reconstructed samples and the input information to a machine learning, ML, model, thereby generating at least one ML output data, and based at least on said at least one ML output data, generating the encoded video or the decoded video.

Abstract: A method of performing bi-directional optical flow, BDOF, processing for a video sequence of images, with each image including a plurality of blocks with bidirectional-predicted inter coding blocks, BPICBs. The method includes obtaining a shifted pair of refinement parameters per a subblock of a bidirectional-predicted inter coding block, wherein the shifted pair of refinement parameters includes a shifted first refinement parameter and a shifted second refinement parameter.

Abstract: There is provided a method for processing a bitstream. The method comprises determining a value, N, wherein N identifies a number of ordered layer representations, wherein N is greater than or equal to 3 such that the N ordered layer representations comprises a highest layer representation, a second highest layer representation, and a third highest layer representation. The method further comprises determining a value for the highest layer representation. The method comprises, after determining the value for the highest layer representation and before determining a value for the third highest layer representation, determining a value for the second highest layer representation. The method comprises, after determining the value for the second highest layer representation, determining a value for the third highest layer representation.

Abstract: There is provided a method of generating encoded video data or decoded video data. The method comprises providing input data to a first convolution layer, CL, thereby generating first convoluted data, generating residual data based on the first convoluted data, and generating the encoded video data or the decoded video data based on a combination of the input data and the residual data.

Abstract: A method for producing phosphorus trifluoride and a method for producing phosphorus pentafluoride, which have high reaction efficiency, low reaction temperature and an excellent energy efficiency. The method includes: a step of introducing phosphorus trichloride and hydrogen fluoride into a first reactor, and a step of discharging phosphorus trifluoride from the first reactor, the first reactor contains carbon material. The method includes a step of introducing the phosphorus trifluoride obtained above and chlorine into a second reactor, and a step of discharging dichloro-phosphorus trifluoride from the second reactor. The method further includes a step of introducing the dichloro-phosphorus trifluoride obtained above and hydrogen fluoride into a third reactor; and a step of discharging phosphorus pentafluoride from the third reactor.

Abstract: A method for determining a prediction block for decoding or encoding a current block in a current picture of a video stream. The method includes obtaining a pair of initial motion vectors comprising a first and a second initial motion vector. The method also includes determining whether to refine the initial motion vectors. The step of determining whether or not to refine the initial motion vectors comprises: i) determining whether a first prediction scheme and/or a second prediction scheme is enabled and ii) determining to refrain from refining the initial motion vectors as a result of determining that either the first prediction scheme or second prediction scheme is enabled or determining to refine the initial motion vectors as a result of determining neither the first prediction scheme nor second prediction scheme is enabled.

Abstract: There is provided a method. The method comprises obtaining a weighting factor. The method comprises obtaining input sample values. The method further comprises determining neural network, NN, filtered sample values by providing the input sample values to a neural network. The method comprises determining revised sample values based on (i) the weighting factor and (ii) the NN filtered sample values.

Abstract: A method and decoder for determining that a decoder conforming to a first profile is capable of decoding a first picture of a video bitstream conforming to a second profile is provided. The method includes obtaining an indicator value, the indicator value indicating conformance to a profile. The method includes determining whether the first picture in the video bitstream is a still picture. The method includes responsive to the indicator value indicating conformance to the second profile and the first picture is determined to be a still picture, determining that the decoder conforming to the first profile is capable of decoding the first picture of the video bitstream.

Abstract: A method for encoding or decoding an image is provided. The method comprises obtaining pixel values of pixels included in the image. The method further comprises converting the pixel values into convoluted values using a convolution network that comprises a first convolution layer, wherein the first convolution layer is configured to receive first input values and generate first output values using a convolution operation. The method further comprises obtaining first quality values, and (i) combining the first quality values with the pixel values, thereby generating the first input values or (ii) combining the first quality values with the first output values, thereby generating first combined values.

Abstract: There is provided a method. The method comprises obtaining an input video frame data associated with an input video frame. The method comprises performing a deblocking filtering operation on one or more samples included in the input video frame, thereby generating one or more deblocked samples. The method further comprises performing a machine-learning (ML)-based filtering operation and/or adaptive loop filtering operation on one or more samples included in the input video frame, thereby generating one or more filtered samples. The method comprises, using said one or more deblocked samples and/or said one or more filtered samples, producing encoded or decoded video frame data including an encoded or decoded video frame.

Abstract: There is provided a method for decoding a video frame. The method comprises receiving an encoded video frame sequence data associated with a video frame sequence, wherein the video frame sequence includes the video frame. The method comprises deriving from the received encoded video frame sequence data (i) sample value information of one or more blocks and/or (ii) block information about said one or more blocks. The method further comprises, based on (i) the derived sample value information and/or (ii) the derived block information, selecting from a set of machine learning, ML, models at least one ML model to use for decoding the video frame. The method comprises decoding the video frame using the selected at least one ML model.

Abstract: A method for determining a prediction block for decoding or encoding a current block in a current picture of a video stream. The method includes obtaining a pair of initial motion vectors, the pair of initial motion vectors comprising a first initial motion vector and a second initial motion vector. The method also includes determining whether or not to refine the initial motion vectors. The method further includes refining the initial motion vectors as a result of determining to refine the initial motion vectors.

Abstract: There are provided mechanisms for temporal motion vector prediction of a current picture in a series of pictures of a video sequence. The method comprises determining, for a current block of the current picture, whether any one of: left, top, top-right and left-bottom spatial neighboring blocks of the current block in the current picture uses a collocated picture as a reference picture. The method further comprises, when the determination is that one of the left, top, top-right and left-bottom spatial neighboring block uses a collocated picture as a reference picture, using a motion vector of the one of the spatial neighboring blocks as a temporal vector of the current block.