Ateme

Envivio France

Inria Institut National De Recherche En Informatique Et En Automatique

Other

Centre National De La Recherche Scientifique

Thomson Licensing

Thomas Guionnet

Mohsen Abdoli

Sébastien Herbreteau

Christine Guillemot

Sassan Pejhan

Elie Mora

Jean Kypreos

Anthony Nasrallah

Abdelmajid Moussaoui

Laurent Guillo

Tanguy Person

Marco Cagnazzo

Attilio Fiandrotti

Guillaume Hervouet

Michael Ropert

Mathieu Muller

Sylvain Buriau

Marine Sorin

Mickaël Le Guerroue

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.

Method for image processing and apparatus for implementing the same

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.

Method for processing a video sequence and apparatus for implementing the same

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.

Method for dynamic computational resource management and apparatus for implementing the same

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.

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.

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.

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.

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 (&#x3c6;, . . . , &#x3c6;) with a generated noise sample corresponding to an additive Gaussian noise of AR model variance parameter (&#x3c3;), 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 (&#x3c3;) of a variance of the noise template, and based on a comparison of the estimated variance (&#x3c3;) with a predetermined threshold (T), correcting the AR model variance parameter (&#x3c3;).

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.

Method for selecting encoding profiles for multimedia content for on-demand distribution

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 convergence check loop.

Disclosed is a method for processing a video stream including a set of images liable to contain a transition effect, the method including the following steps: detecting a fade-type transition effect within the set of images of the video stream and, where appropriate, calculating minimum and maximum brightnesses LMIN and LMAX for each image of the set of images containing the fade; estimating minimum and maximum brightnesses EMIN and EMAX for all of the images not containing the detected transition effect; determining minimum and maximum brightnesses SMIN and SMAX for a standard dynamic range; calculating minimum and maximum brightnesses TMIN and TMAX depending on the minimum and maximum brightnesses LMIN, LMAX, EMIN, EMAX, SMIN and SMAX calculated and determined beforehand; transmitting the calculated minimum and maximum brightnesses TMIN and TMAX with a view to dynamic range compression.

Dynamic range compression method

The invention concerns a method for encoding a multi-view video stream comprising at least two video sequences, comprising a step of motion information prediction for a square or rectangular block of a current picture of a current view from a list of candidate motion information predictors, a motion information predictor including at least a motion vector, and a step of entropic encoding of an item of information representative of the position of a selected motion information predictor in the list. The method of the invention comprises, for the encoding of a current block, determining in the list of candidate motion information predictors of the current block a motion information predictor comprising a disparity motion vector pointing to a block of a picture of a video sequence corresponding to a view different from the current view, and in case of positive determination, including in the list of candidate motion information predictors at least one refined motion information predictor comprising a refined disparity motion vector predictor obtained from the determined disparity motion vector so as to obtain a refined list of candidate motion information predictors.

Method and device for motion information prediction refinement

A system is proposed for dynamic reduction of the entropy of a signal upstream to a data compression device. The signal includes a set of successive frames. The system has a filtering decision module that provides a setpoint value of filtering and a filtering module that filters the signal according to the setpoint value of filtering and gives a filtered signal to the data compression device. The system further includes a module for obtaining a piece of information on complexity for each frame of the signal. The filtering decision module is adapted to determining the setpoint value of filtering, for each frame of the signal, as a function inter alia of the piece of information on complexity.

System and method for the dynamic reduction of the entropy of a signal upstream to a data compression device

The invention concerns a method for encoding a video stream, an associated method for decoding a video stream, an associated encoder, an associated decoder and associated computer programs. The encoding method is directed to encoding a video stream comprising at least one video sequence, comprising, for the encoding of a square or rectangular block of a picture of a video sequence of the video stream, selecting a motion information predictor for the block from a list of candidate motion information predictors, and entropic encoding of an index representative of a position of the selected motion information predictor in the list.

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Rennes, France

Thomas Guionnet