Abstract: A method for encoding or decoding an image. The method includes obtaining a CTB having a vertical size (CtbSizeY) and a horizontal size (CtbSizex), the CTB comprising set of sample values comprising a current sample value having a location (x,y) within the CTB. The method also includes selecting a filter strength value for the current sample based on y and filtering the current sample using the selected filter strength value. Selecting a filter strength value for the current sample based on y comprises: i) determining whether the following first condition is true: (y==(CtbSizeY-5) OR y==(CtbSizeY-4)) AND var==1, where var is set equal to 1 as a result of a determination that a second condition is satisfied; and ii) choosing a filter strength value of 10 as a result of determining the first condition is true.

Abstract: A method for encoding a picture in which at least a first object has been detected, wherein the picture comprises a first block. The method comprises obtaining first bounding information indicating the spatial location of the first object within the picture, wherein the bounding information specifies a first picture area within which the first object is located. The method also includes determining a first quantization parameter, QP, value for the first block, wherein determining the first QP value for the first block comprises using the first bounding information in a process for determining the first QP value.

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 decoding a picture from a bitstream. The method includes decoding a tile partition structure from one or more syntax elements in the bitstream. The method includes determining a number of tiles N in the picture. The method includes decoding a number of partitions in the picture, wherein each partition comprises an integer number of tiles. The method includes deriving a size and/or location for a current partition, wherein deriving the size and/or location for the current partition comprises: decoding one or more location syntax elements for the current partition from the bitstream, deriving a top-left position of the current partition as a location of a first tile in raster scan order that is not included in one or more previously derived partition, and deriving the size and/or the location of the current partition from a value of the one or more location syntax element together with a size and/or location of the one or more previously derived partition.

Abstract: There is provided a method (600) for decoding a current coded picture from a video bitstream. The method comprises deriving a list of delta quantization parameter, QP, values from parameter set syntax elements in the video bitstream. The method comprises deriving an index value, IV, from a slice header, a segment header or a picture header, associated with the current coded picture. The method comprises deriving a delta QP value for the current coded picture using the derived list of delta QP values and the IV. The method comprises using the derived delta QP value to derive an initial QP value, QPi, for the current coded picture. The method comprises using the initial QP value in a decoding process to decode the current coded picture or segment thereof.

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: Methods and apparatus for encoding or decoding a first structure within a picture in a video sequence are provided. The method comprises, for a second structure within the first structure, determining an initial motion vector, MV, and MV candidates. The method comprises, for each of the MV candidates, using the MV candidate to derive first and second reference blocks, and a size of the first and second reference blocks may not be equal to a size of the second structure. The method comprises deriving a cost value based on samples from the first and second reference blocks. The method comprises using the cost values from the MV candidates to derive a best MV candidate and using the best MV candidate to derive prediction samples for the second structure.

Abstract: There is provided a method for processing a bitstream including a coded picture. The method comprises receiving the bitstream. The method comprises decoding the received bitstream including the coded picture, thereby obtaining i) a decoded picture, ii) filtering information about a first neural network, NN, based filtering, and iii) filtering area information indicating a first filtering area to which the first NN based filtering is to be applied. The method comprises applying the first NN based filtering to the first filtering area in the decoded picture. The received bitstream includes a group of one or more syntax elements that indicates that the first NN based filtering is to be applied to a first part of the decoded picture but is not to be applied to a second part of the decoded picture. The first filtering area corresponds to the first part of the decoded picture.

Abstract: A method performed by an apparatus to determine compression parameters to choose when compressing images or videos for use in a machine vision task is provided. The method includes compressing an uncompressed original image or video at a plurality of different quality levels and/or bit rates to create a plurality of compressed images or videos. The method further includes, for each compressed image or video compressed at the different quality levels and/or bit rates: decompressing the compressed image or video to create a decompressed image or video, executing a machine vision algorithm on the decompressed image or video to generate machine vision results for the decompressed image or video and deriving a performance value indicating a performance of the decompressed image or video based on comparing the machine vision results to an assumed truth.

Abstract: In one aspect there is disclosed a method of applying deblocking on implicit vertical TU boundaries when the CU width is larger than the maximum TU width and applying deblocking on implicit horizontal TU boundaries when the CU height is larger than the maximum TU height. Some exemplary embodiments include HEVC deblocking and deblocking using longer filters.

Abstract: There is provided a method for decoding a picture included in a coded video bitstream. The method comprises obtaining a computational complexity indicator indicating complexity of a computation needed for decoding the coded video bitstream and providing to a decoder the computational complexity indicator associated with the coded video bitstream, wherein the obtained computational complexity indicator is configured to be used for determining whether one or more coding tools is allowed to be used for decoding the picture.

Abstract: A MIP method (700) for predicting samples of a current block. The method includes storing (s702) a set of MIP weight matrices. The method also includes determining (s704) the width, W, and the height, H, of the current block. The method further includes setting (s706) a mipSizeId variable to 1 as a result of: i) determining that W=4 storing a set of MIP weight matrices and H=16 or ii) determining that W=16 and H=4. The method further includes storing (s708) a value, predModeIntra, that specifies a MIP prediction mode for the current block. The method further includes determining (s710), based on predModeIntra and the value of the mipSizeId variable, a modeId value. And the method further includes selecting (s712), from the set of MIP weight matrices, a MIP weight matrix to be used for the current block, wherein the selection is based on modeId and the value of the mipSizeId variable.

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 for encoding a picture. The method comprises, for each picture on either side of the current picture with a distance of one or two away from the current picture, computing a first error value, E, wherein the first error value, E, is computed for each block of a set of blocks of a first block size of the current picture and is based on the respective block of the current picture and a corresponding block in the picture. The method comprises, for each picture on either side of the current picture with a distance of one or two away from the current picture, aggregating the first error value, E, over a block of a second block size for each block of the set of blocks of the first block size contained in the block of the second block size to generate a second error value, E?, corresponding to the picture.

Abstract: In one aspect there is disclosed a method of applying deblocking on implicit vertical TU boundaries when the CU width is larger than the maximum TU width and applying deblocking on implicit horizontal TU boundaries when the CU height is larger than the maximum TU height. Some exemplary embodiments include HEVC deblocking and deblocking using longer filters.

Abstract: Methods and apparatuses for applying a deblocking to a coding unit in video pictures a provided. In one aspect, the deblocking is applied to sub-block boundaries in the coding unit and to implicit transform unit boundaries in the coding unit.

Abstract: Methods and apparatuses for processing a current block within a current picture. The processing may include determining whether a second syntax element S1 is present in a coded video bitstream. The processing may include, if the S1 is not present, inferring which one of implicit signaling and explicit signaling is used to signal a second piece of motion information M1, or, if the S1 is present, determining which one of implicit signaling and explicit signaling is used to signal the M1 based on a decoded value of the S1. The processing may include, if implicit signaling is inferred or determined, deriving the M1, or, if explicit signaling is inferred or determined, determining the M1. The processing may include generating a prediction block Pb01 for the current block based on a first piece of motion information M0, the M1, and a weight factor W1. The second syntax element S1 may be a multi-hypothesis prediction, MHP, merge flag and the third syntax element S2 may be a motion information index.

Abstract: A prediction unit for an encoder or decoder implements matrix based intra prediction. Input boundary samples for a current block are downsampled to obtain reduced boundary samples for matrix multiplication and/or linear interpolation, or both. In one embodiment, downsampling is performed in a manner that aligns the reduced boundary samples with an output of a matrix multiplication unit of the prediction unit. In other embodiments, downsampling is performed without averaging. The embodiments reduce the complexity of the prediction unit and the latency of the encoder or decoder.

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.