H04N19/33

Upsampling for signal enhancement coding
11546634 · 2023-01-03 · ·

There is disclosed a method of encoding an input signal, the method comprising: receiving a base encoded signal, the base encoded signal being generated by feeding an encoder with a down-sampled version of an input signal; producing a first residual signal by: decoding the base encoded signal to produce a first decoded signal; and using a difference between the base decoded signal and the down-sampled version of the input signal to produce the first residual signal; producing a second residual signal by: correcting the base decoded signal using the residual signal to create a corrected decoded version; up-sampling the corrected decoded version; and using a difference between the up-sampled corrected decoded signal and the input signal to produce the second residual signal; wherein the up-sampling is one of bilinear or bicubic up-sampling. A corresponding decoding method is also disclosed.

METHODS FOR DECODING AND ENCODING AN IMAGE, ASSOCIATED DEVICES AND SIGNAL
20220417541 · 2022-12-29 ·

A method for decoding an image based on a base layer and enhancement information includes decoding the base layer in order to obtain at least one base component, oversampling the at least one base component in order to obtain at least one oversampled component, decoding the enhancement information by a part at least of artificial neural network in order to obtain enhancement values, and reconstructing the image based on the at least one oversampled component and the enhancement values. An associated encoding method, electronic decoding device, and electronic encoding device are provided.

Methods and systems for the efficient acquisition, conversion, and display of pathology images
11538578 · 2022-12-27 ·

A method for viewing pathology images in a web browser is provided. The method includes obtaining a pathology image in a first format, converting the pathology image into a pyramid representation file comprising images grouped into a plurality of levels, wherein the images in the different plurality of levels correspond to portions of the pathology image at a same or different degrees of resolution, and wherein the images are in the first format, storing the pyramid representation file in the first memory, receiving a request from a user to view the pathology image at a specified resolution, loading one or more images from at least one of the plurality of levels corresponding to the specified resolution, wherein the one or more images are in the first format and wherein the one or more images are loaded into a web browser coupled with the first memory, converting the images into a second format such that the images' degrees of resolution are maintained, and storing the images in the second format in a second memory.

EMBEDDING DATA WITHIN TRANSFORMED COEFFICIENTS USING BIT PARTITIONING OPERATIONS
20220408099 · 2022-12-22 ·

Examples described herein relate to decoding and encoding signals. Certain examples described herein encapsulate custom data that is not signal data within a stream of encoded signal data. The custom data may comprise a wide variety of metadata that annotates the signal data, or provides additional information relating to the signal data. Certain examples described herein encapsulate custom data within a set of transformed coefficient values that represent data derived from a transform operation that forms part of the signal encoding. The encapsulation is may be performed by applying a bit shift operation to coefficient bits representing the set of transformed coefficient values.

EMBEDDING DATA WITHIN TRANSFORMED COEFFICIENTS USING BIT PARTITIONING OPERATIONS
20220408099 · 2022-12-22 ·

Examples described herein relate to decoding and encoding signals. Certain examples described herein encapsulate custom data that is not signal data within a stream of encoded signal data. The custom data may comprise a wide variety of metadata that annotates the signal data, or provides additional information relating to the signal data. Certain examples described herein encapsulate custom data within a set of transformed coefficient values that represent data derived from a transform operation that forms part of the signal encoding. The encapsulation is may be performed by applying a bit shift operation to coefficient bits representing the set of transformed coefficient values.

Picture coding and decoding

A picture with multiple slices is encoded by generating a coded slice representation for each of the slices. A slice flag is set to a first value for the first slice in the picture and corresponding slice flags of the remaining slices are set to a second defined value. A respective slice address is generated for each remaining slice to enable identification of the slice start position within the picture for the slice. A coded picture representation of the picture comprises the coded slice representations, the slice addresses and the slice flags. The slice flags enable differentiation between slices for which slice addresses are required and the slice per picture for which no slice address is needed to identify its slice start position.

Picture coding and decoding

A picture with multiple slices is encoded by generating a coded slice representation for each of the slices. A slice flag is set to a first value for the first slice in the picture and corresponding slice flags of the remaining slices are set to a second defined value. A respective slice address is generated for each remaining slice to enable identification of the slice start position within the picture for the slice. A coded picture representation of the picture comprises the coded slice representations, the slice addresses and the slice flags. The slice flags enable differentiation between slices for which slice addresses are required and the slice per picture for which no slice address is needed to identify its slice start position.

Machine learning for visual processing

A method for developing an enhancement model for low-quality visual data, the method comprising the steps of receiving one or more sections of higher-quality visual data; and training a hierarchical algorithm. The hierarchical algorithm is operable to increase the quality of one or more sections of lower-quality visual data so as to substantially reproduce the one or more sections of higher-quality visual data. The hierarchical algorithm is then outputted.

Machine learning for visual processing

A method for developing an enhancement model for low-quality visual data, the method comprising the steps of receiving one or more sections of higher-quality visual data; and training a hierarchical algorithm. The hierarchical algorithm is operable to increase the quality of one or more sections of lower-quality visual data so as to substantially reproduce the one or more sections of higher-quality visual data. The hierarchical algorithm is then outputted.

Three-dimensional data encoding method, three-dimensional data decoding method, three-dimensional data encoding device, and three-dimensional data decoding device

A three-dimensional data encoding method includes: (i) when a number of three-dimensional points included in point cloud data to be encoded is n that is greater than a predetermined number, n being an integer greater than or equal to 2, calculating an encoding coefficient by generating a hierarchical structure in which each of n pieces of attribute information on the three-dimensional points is sorted into one of a higher frequency component and a lower frequency component to be layered, and generating a bitstream including the encoding coefficient calculated in the calculating; and (ii) when a number of three-dimensional points included in the point cloud data is m that is smaller than or equal to the predetermined number, m being an integer greater than or equal to 1, generating a bitstream in accordance with m pieces of attribute information on the three-dimensional points without generating a hierarchy structure.