A method for partitioning of input vectors for coding is presented. The method comprises obtaining of an input vector. The input vector is segmented, in a non-recursive manner, into an integer number, N.sup.SEG, of input vector segments. A representation of a respective relative energy difference between parts of the input vector on each side of each boundary between the input vector segments is determined, in a recursive manner. The input vector segments and the representations of the relative energy differences are provided for individual coding. Partitioning units and computer programs for partitioning of input vectors for coding, as well as positional encoders, are presented.

A method includes maintaining a set of parameters or weights derived through online learning for a neural net; transmitting an update of the parameters or weights to a decoder; deriving a first prediction block based on an output of the neural net using the parameters or weights; deriving a first encoded prediction error block through encoding a difference of the first prediction block and a first input block; encoding the first encoded prediction error block into a bitstream; deriving a reconstructed prediction error block based on the first encoded prediction error block; deriving a second prediction block based on an output of the neural net using the parameters or weights and the reconstructed prediction error block; deriving a second encoded prediction error block through encoding a difference of the second prediction block and a second input block; and encoding the second encoded prediction error block into a bitstream.

Various embodiments provide an apparatus, a method, and a computer program product. An example apparatus includes at least one processor; and at least one non-transitory memory comprising computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to iteratively perform following until a stopping criterion is met: provide a finetuning driving content (FDC) or a content derived from FDC to a decoder side neural network (DSNN); compute an output of the DSNN as a processed FDC; compute a loss based on the processed FDC and an approximated ground truth data (AGT) associated with the FDC; compute an update to the DSNN; and apply the computed update to the DSNN.

The invention provides a method for managing image data in a motor vehicle lighting system, the lighting system including at least one lighting module intended to project light beams, the light beams being generated from data relating to the selection of at least one image, each image being respectively defined by a matrix including a plurality of horizontal or vertical rows of pixels, with each pixel having a numerical value related to a light intensity of the pixel. The method includes determining whether the pixel under analysis is considered to be a significant point of inflection of the image, so as to transmit it to at least one lighting module, so that it is able to project a resulting image.

Systems and methods are described for video coding using affine motion models with adaptive precision. In an example, a block of video is encoded in a bitstream using an affine motion model, where the affine motion model is characterized by at least two motion vectors. A precision is selected for each of the motion vectors, and the selected precisions are signaled in the bitstream. In some embodiments, the precisions are signaled by including in the bitstream information that identifies one of a plurality of elements in a selected predetermined precision set. The identified element indicates the precision of each of the motion vectors that characterize the affine motion model. In some embodiments, the precision set to be used is signaled expressly in the bitstream; in other embodiments, the precision set may be inferred, e.g., from the block size, block shape or temporal layer.

Systems and methods are described for video coding using affine motion models with adaptive precision. In an example, a block of video is encoded in a bitstream using an affine motion model, where the affine motion model is characterized by at least two motion vectors. A precision is selected for each of the motion vectors, and the selected precisions are signaled in the bitstream. In some embodiments, the precisions are signaled by including in the bitstream information that identifies one of a plurality of elements in a selected predetermined precision set. The identified element indicates the precision of each of the motion vectors that characterize the affine motion model. In some embodiments, the precision set to be used is signaled expressly in the bitstream; in other embodiments, the precision set may be inferred, e.g., from the block size, block shape or temporal layer.

A method for coding unit partitioning in a video encoder is provided that includes performing intra-prediction on each permitted coding unit (CU) in a CU hierarchy of a largest coding unit (LCU) to determine an intra-prediction coding cost for each permitted CU, storing the intra-prediction coding cost for each intra-predicted CU in memory, and performing inter-prediction, prediction mode selection, and CU partition selection on each permitted CU in the CU hierarchy to determine a CU partitioning for encoding the LCU, wherein the stored intra-prediction coding costs for the CUs are used.

A method for coding unit partitioning in a video encoder is provided that includes performing intra-prediction on each permitted coding unit (CU) in a CU hierarchy of a largest coding unit (LCU) to determine an intra-prediction coding cost for each permitted CU, storing the intra-prediction coding cost for each intra-predicted CU in memory, and performing inter-prediction, prediction mode selection, and CU partition selection on each permitted CU in the CU hierarchy to determine a CU partitioning for encoding the LCU, wherein the stored intra-prediction coding costs for the CUs are used.

A spatial prediction method of a block of pixels of an image, called current block, using a plurality of spatial prediction modes based on neighboring pixels of the current block is disclosed. The method comprises determining a prediction block of the current block according to each one of said spatial prediction modes and selecting one of said prediction blocks according to a predetermined criterion. According to the invention, before carrying out the preceding steps, it is detected, among said spatial prediction modes, whether some of them are redundant. If such redundant modes are detected, one of them is replaced by an additional mode, distinct from the other spatial prediction modes.

A spatial prediction method of a block of pixels of an image, called current block, using a plurality of spatial prediction modes based on neighboring pixels of the current block is disclosed. The method comprises determining a prediction block of the current block according to each one of said spatial prediction modes and selecting one of said prediction blocks according to a predetermined criterion. According to the invention, before carrying out the preceding steps, it is detected, among said spatial prediction modes, whether some of them are redundant. If such redundant modes are detected, one of them is replaced by an additional mode, distinct from the other spatial prediction modes.