A multimedia file and methods of generating, distributing and using the multimedia file are described. Multimedia files in accordance with embodiments of the present invention can contain multiple video tracks, multiple audio tracks, multiple subtitle tracks, a complete index that can be used to locate each data chunk in each of these tracks and an abridged index that can enable the location of a subset of the data chunks in each track, data that can be used to generate a menu interface to access the contents of the file and ‘meta data’ concerning the contents of the file. Multimedia files in accordance with several embodiments of the present invention also include references to video tracks, audio tracks, subtitle tracks and ‘meta data’ external to the file. One embodiment of a multimedia file in accordance with the present invention includes a series of encoded video frames, a first index that includes information indicative of the location within the file and characteristics of each encoded video frame and a separate second index that includes information indicative of the location within the file of a subset of the encoded video frames.

A multimedia file and methods of generating, distributing and using the multimedia file are described. Multimedia files in accordance with embodiments of the present invention can contain multiple video tracks, multiple audio tracks, multiple subtitle tracks, a complete index that can be used to locate each data chunk in each of these tracks and an abridged index that can enable the location of a subset of the data chunks in each track, data that can be used to generate a menu interface to access the contents of the file and ‘meta data’ concerning the contents of the file. Multimedia files in accordance with several embodiments of the present invention also include references to video tracks, audio tracks, subtitle tracks and ‘meta data’ external to the file. One embodiment of a multimedia file in accordance with the present invention includes a series of encoded video frames, a first index that includes information indicative of the location within the file and characteristics of each encoded video frame and a separate second index that includes information indicative of the location within the file of a subset of the encoded video frames.

Provided is an artificial intelligence (AI) decoding apparatus includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, the processor is configured to: obtain AI data related to AI down-scaling an original image to a first image; obtain image data corresponding to an encoding result on the first image; obtain a second image corresponding to the first image by performing a decoding on the image data; obtain deep neural network (DNN) setting information among a plurality of DNN setting information from the AI data; and obtain, by an up-scaling DNN, a third image by performing the AI up-scaling on the second image, the up-scaling DNN being configured with the obtained DNN setting information, wherein the plurality of DNN setting information comprises a parameter used in the up-scaling DNN, the parameter being obtained through joint training of the up-scaling DNN and a down-scaling DNN, and wherein the down-scaling DNN is used to obtain the first image from the original image.

The present disclosure relates to systems, methods, and non-transitory computer-readable media to enhance texture image delivery and processing at a client device. For example, the disclosed systems can utilize a server-side compression combination that includes, in sequential order, a first compression pass, a decompression pass, and a second compression pass. By applying this compression combination to a texture image at the server-side, the disclosed systems can leverage both GPU-friendly and network-friendly image formats. For example, at a client device, the disclosed system can instruct the client device to execute a combination of decompression-compression passes on a GPU-network-friendly image delivered over a network connection to the client device. In so doing, client device can generate a tri-pass-compressed-texture from a decompressed image comprising texels with color palettes based on previously reduced color palettes from the first compression pass at the server-side, which reduces computational overhead and increases performance speed.

The present disclosure relates to systems, methods, and non-transitory computer-readable media to enhance texture image delivery and processing at a client device. For example, the disclosed systems can utilize a server-side compression combination that includes, in sequential order, a first compression pass, a decompression pass, and a second compression pass. By applying this compression combination to a texture image at the server-side, the disclosed systems can leverage both GPU-friendly and network-friendly image formats. For example, at a client device, the disclosed system can instruct the client device to execute a combination of decompression-compression passes on a GPU-network-friendly image delivered over a network connection to the client device. In so doing, client device can generate a tri-pass-compressed-texture from a decompressed image comprising texels with color palettes based on previously reduced color palettes from the first compression pass at the server-side, which reduces computational overhead and increases performance speed.

Example embodiments provide a system for training a data coding pipeline including a feature extractor neural network, an encoder neural network, and a decoder neural network configured to reconstruct input data based on encoded features. A plurality of losses corresponding to different tasks may be determined for the coding pipeline. Tasks may be performed based on an output of the coding pipeline. A weight update may be determined for at least a subset of the coding pipeline based on the plurality of losses. The weight update may be configured to reduce a number of iterations for fine-tuning the coding pipeline for one of the tasks. This enables faster adaptation of the coding pipeline for one of the tasks after deployment of the coding pipeline. Apparatuses, methods, and computer programs are disclosed. Apparatuses, methods, and computer programs are disclosed.

Example embodiments provide a system for training a data coding pipeline including a feature extractor neural network, an encoder neural network, and a decoder neural network configured to reconstruct input data based on encoded features. A plurality of losses corresponding to different tasks may be determined for the coding pipeline. Tasks may be performed based on an output of the coding pipeline. A weight update may be determined for at least a subset of the coding pipeline based on the plurality of losses. The weight update may be configured to reduce a number of iterations for fine-tuning the coding pipeline for one of the tasks. This enables faster adaptation of the coding pipeline for one of the tasks after deployment of the coding pipeline. Apparatuses, methods, and computer programs are disclosed. Apparatuses, methods, and computer programs are disclosed.

Operations of a method include obtaining a segment of image data that represents a portion of a frame of video image data to be encoded. The operations include determining, based on the segment and a target bitrate, a quantization parameter (QP) value for the segment. The operations include determining a minimum QP value and a maximum QP value that establishes a range of QP values an integrated bit rate control algorithm may use to encode the segment. The operations include encoding the segment with a first QP value that is greater than the minimum QP value and less than the maximum QP value. The operations include adjusting, by the bit rate control algorithm, the first QP value to a second QP value that is greater than the minimum QP value and less than the maximum QP value. The operations include transmitting the encoded segment to a remote device.

One or more multi-stage optimization iterations are performed with respect to a compression algorithm. A given iteration comprises a first stage in which hyper-parameters of a perceptual quality algorithm are tuned independently of the compression algorithm. A second stage of the iteration comprises tuning hyper-parameters of the compression algorithm using a set of perceptual quality scores generated by the tuned perceptual quality algorithm. The final stage of the iteration comprises performing a compression quality evaluation test on the tuned compression algorithm.

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.