Abstract: A method of method of processing an image of a video sequence including a plurality of images, wherein the video sequence is divided into a sequence of consecutive subsets each including one or more consecutive images of the video sequence is provides, which includes: determining, among the consecutive subsets, a current subset that includes the image, upon determining that the image is a first image of the current subset to be processed, determining an analysis window corresponding to the current subset, wherein the analysis window includes a plurality of consecutive images of the video sequence, and includes at least one image of the current subset, and generating processing results for the image by performing a processing on the images of the analysis window.

Abstract: A method of processing a first image in a first plurality of images, wherein the first image is divided into a plurality of pixel blocks, is proposed, which comprises, for a current block of the first image: selecting, in a set of a plurality of predefined interpolation filters, an interpolation filter based on a prediction of an interpolation filter determined by a supervised learning algorithm to which data related to the current block is input; and using the selected interpolation filter for calculating fractional pixel values in a second image of the plurality of images for a temporal prediction of pixels of the current block based on a reference block correlated to the current block in the second image, wherein the second image is distinct from the first image and was previously encoded according to an image encoding sequence for encoding the images of the plurality of images.

Abstract: A method for encoding image data of an image divided into a plurality of pixel blocks using a machine learning algorithm, is proposed, which comprises, by a computing platform comprising a processor configured for implementing the machine learning algorithm, for a block of the pixel blocks: obtaining a block neighborhood set of at least one pixel of the image, wherein the at least one pixel is located outside the block in a spatial neighborhood of a corresponding pixel located on an edge of the pixel block, and generating, by the machine learning algorithm configured for performing end-to-end image compression, a bitstream representing the encoded pixel block, by encoding the pixel block based on input data comprising the pixel block and the block neighborhood set.

Abstract: A method for managing computational resources allocated for encoding of one or more multimedia content streams for distribution in dynamic mode to viewing devices through a distribution network is proposed, which comprises, by a processing node of the distribution network: obtain, for a multimedia content stream corresponding to a channel distributed to the viewing devices, a value of a computational resource allocation criterion, wherein the computational resource allocation criterion comprises an audience measurement for the corresponding channel; and determine, based on the computational resource allocation criterion, an allocation of computational resources of a computing platform configured for encoding the multimedia content stream.

Abstract: A method for managing computational resources allocated for encoding of one or more multimedia content streams for distribution in dynamic mode to viewing devices through a distribution network is proposed, which comprises, by a processing node of the distribution network: obtain, for a multimedia content stream corresponding to a channel distributed to the viewing devices, a value of a computational resource allocation criterion, wherein the computational resource allocation criterion comprises an audience measurement for the corresponding channel and a video content complexity of the multimedia content stream; and determine, based on the computational resource allocation criterion, an allocation of computational resources of a computing platform configured for encoding the multimedia content stream.

Abstract: A method of processing an image is proposed, which includes: determining, based on the image, one or more noise templates, wherein each of the one or more noise templates includes noise pixels representing noise contained in the image; calculating one or more first autocovariance values, based on the noise pixels of at least one of the one or more noise templates; based on the one or more first autocovariance values, selecting an entry of a noise model database among database entries which respectively include values of noise model parameters corresponding to a noise model.

Abstract: A method for managing encoding of multimedia content stored in a file is proposed, which comprises: determining, using a supervised learning algorithm, a prediction of processing resources required for encoding the multimedia content, based on one or more multimedia content characteristics of the multimedia content and on one or more multimedia content encoding parameters for encoding the multimedia content; and determining a processing configuration for encoding the multimedia content based on the prediction of processing resources.

Abstract: A method of encoding a video sequence is proposed, which comprises: for a video encoding tool of a set of video encoding tools configured for encoding the video sequence comprised in a video encoder, determining a prediction of a configuration of the video encoder to use or not the video encoding tool for encoding the video sequence, and encoding, by the video encoder, the video sequence using a configuration of the video encoder based on the predicted configuration of the video encoder.

Abstract: A method of processing a video sequence is proposed, which comprises, for a video encoding tool of a set of video encoding tools configured for encoding the video sequence comprised in a video encoder, determining an efficiency value based on an encoding performance value and an encoding computational complexity value of the video encoding tool for encoding the video sequence, and determining, based on the efficiency value of the video encoding tool, whether to configure the video encoder to use the video encoding tool for the encoding of the video sequence.

Abstract: A method of processing an image divided into a plurality of pixel blocks which are processed according to a processing sequence is provided, which comprises, for a current pixel block: determining an application area comprising a set of pixels in blocks preceding the current block in the processing sequence; performing a gradient analysis on at least one pixel of the application area; selecting at least two intra prediction video coding modes among a plurality of intra prediction video coding modes usable for processing the current pixel block, wherein at least one of the selected intra prediction video coding modes is selected based on the gradient analysis; determining a plurality of predictors of the current pixel block based on the at least two selected intra prediction video coding modes; and generating a predictor of the current pixel block based on the determined plurality of predictors.

Abstract: A method for encoding a first image within a first set of images, in which the first image is cut into blocks, each block being encoded according to one among a plurality of coding modes, is proposed, which comprises, for a current block of the first image, the determination, on the basis of at least one second image distinct from the first image and previously encoded according to an encoding sequence of the images of the first set of images, of a prediction of a feature of the current block in one or more third images from the first set of images distinct from the first image and not yet encoded according to the encoding sequence, and the use of the prediction to encode the current block while minimizing a flow-distortion criterion.

Abstract: A method of method of processing an image includes: determining estimates of parameters of an auto-regressive, AR, parametric model of noise contained in the image, according to which a current noise pixel is computed as a combination of a linear combination of P previous noise pixels in a causal neighborhood of the current noise pixel weighted by respective AR model linear combination parameters (?1, . . . , ?P) with a generated noise sample corresponding to an additive Gaussian noise of AR model variance parameter (?), generating a noise template of noise pixels based on the estimated AR model parameters, wherein the noise template is of a predetermined pixel size smaller than the pixel size of the image, determining an estimate (?P) of a variance of the noise template, and based on a comparison of the estimated variance (?P) with a predetermined threshold (T?), correcting the AR model variance parameter (?).

Abstract: A method of processing an image divided into a plurality of pixel blocks which are processed according to a processing sequence is provided, which comprises, for a current pixel block: determining an application area comprising a set of pixels in blocks preceding the current block in the processing sequence; performing a gradient analysis on at least one pixel of the application area; selecting at least two intra prediction video coding modes among a plurality of intra prediction video coding modes usable for processing the current pixel block, wherein at least one of the selected intra prediction video coding modes is selected based on the gradient analysis; determining a plurality of predictors of the current pixel block based on the at least two selected intra prediction video coding modes; and generating a predictor of the current pixel block based on the determined plurality of predictors.

Abstract: A method of processing an image is proposed, which includes: generating a decoded image by first encoding the image, wherein the first encoding the image includes decoding encoded data generated based on the image, determining estimates of parameters of a parametric model of noise contained in the image based on the decoded image, including the estimates of parameters of the parametric model of noise in an encoded stream generated by second encoding the image.

Abstract: A method of processing a first image in a first plurality of images, wherein the first image is divided into a plurality of pixel blocks, is proposed, which comprises, for a current block of the first image: selecting, in a set of a plurality of predefined interpolation filters, an interpolation filter based on a prediction of an interpolation filter determined by a supervised learning algorithm to which data related to the current block is input; and using the selected interpolation filter for calculating fractional pixel values in a second image of the plurality of images for a temporal prediction of pixels of the current block based on a reference block correlated to the current block in the second image, wherein the second image is distinct from the first image and was previously encoded according to an image encoding sequence for encoding the images of the plurality of images.

Abstract: A method, implemented by computer means, for digitally encoding multimedia content into one or more video streams for on-demand distribution to at least one receiving terminal, the method comprising the following steps: from a given set of profiles, each profile being characterized by a value of at least one encoding parameter, forming one or more ordered groups of profiles, it being possible for a same profile to be present in multiple groups, such that in each group, each profile and its immediate successor satisfy a first predetermined condition, and selecting from the formed groups at least one group of profiles satisfying a second predetermined condition.

Abstract: A method of processing an image is proposed, which includes: determining, based on the image, one or more noise templates, wherein each of the one or more noise templates includes noise pixels representing noise contained in the image; calculating one or more first autocovariance values, based on the noise pixels of at least one of the one or more noise templates; based on the one or more first autocovariance values, selecting an entry of a noise model database among database entries which respectively include values of noise model parameters corresponding to a noise model.

Abstract: A method of method of processing an image includes: determining estimates of parameters of an auto-regressive, parametric model of noise, according to which a current noise pixel is computed combining linear combination of previous noise pixels in a causal neighborhood of the current noise pixel weighted by respective model linear combination parameters with a generated noise sample corresponding to an additive Gaussian noise of model variance parameter; performing a convergence check loop, each iteration including: generating a noise template of noise pixels based on the estimated model parameters, the noise template having predetermined pixel size smaller than the image pixel size; estimating a noise template variance; if the estimated variance is below a first predetermined threshold or above a second predetermined threshold, proportionally decreasing the model linear combination parameters with a predetermined correcting factor, and performing another convergence check loop; otherwise exiting the converge

Abstract: A method of method of processing an image includes: determining estimates of parameters of an auto-regressive, parametric model of noise, according to which a current noise pixel is computed combining linear combination of previous noise pixels in a causal neighborhood of the current noise pixel weighted by respective model linear combination parameters with a generated noise sample corresponding to an additive Gaussian noise of model variance parameter; performing a convergence check loop, each iteration including: generating a noise template of noise pixels based on the estimated model parameters, the noise template having predetermined pixel size smaller than the image pixel size; estimating a noise template variance; if the estimated variance is below a first predetermined threshold or above a second predetermined threshold, proportionally decreasing the model linear combination parameters with a predetermined correcting factor, and performing another convergence check loop; otherwise exiting the converge

Abstract: A method of method of processing an image includes: determining estimates of parameters of an auto-regressive, AR, parametric model of noise contained in the image, according to which a current noise pixel is computed as a combination of a linear combination of P previous noise pixels in a causal neighborhood of the current noise pixel weighted by respective AR model linear combination parameters (?1, . . . , ?P) with a generated noise sample corresponding to an additive Gaussian noise of AR model variance parameter (?), generating a noise template of noise pixels based on the estimated AR model parameters, wherein the noise template is of a predetermined pixel size smaller than the pixel size of the image, determining an estimate (?P) of a variance of the noise template, and based on a comparison of the estimated variance (?P) with a predetermined threshold (T?), correcting the AR model variance parameter (?).