Abstract: A user equipment (UE) includes a memory element and a processor. The memory element is configured to store a plurality of head-related transfer functions. The processor is configured to receive an audio signal. The audio signal includes a plurality of ambisonic signals. The processor is also configured to identify an orientation of the UE based on physical properties of the UE. The processor is also configured to rotate the plurality of ambisonic signals based on the orientation of the UE. The processor is also configured to filter the plurality of ambisonic signals using the plurality of head-related transfer functions to form speaker signals. The processor is also configured to output the speaker signals.

Abstract: A user equipment (UE) includes a receiver and a processor. The receiver is configured to receive a standard dynamic range (SDR) image and metadata related to an HDR image. The processor is configured to identify relevant portions of the SDR image to be enhanced based on the metadata related to the HDR image. The processor is also configured to increase an intensity of the relevant portions of the SDR image to create an enhanced SDR image. The processor is also configured to output the enhanced SDR image to a display.

Abstract: A user equipment includes a modem receives a compressed bitstream and metadata. The UE also includes a decoder that decodes the compressed bitstream to generate an HDR image, an inertial measurement unit that determines viewpoint information based on an orientation of the UE, and a graphics processing unit (GPU). The GPU maps the HDR image onto a surface and renders a portion of the HDR image based on the metadata and the viewpoint information. A display displays the portion of the HDR image.

Abstract: A user equipment (UE) includes a receiver, at least one sensor, and a processor. The receiver is configured to receive a bit stream including at least one encoded image and metadata. The sensor is configured to determine viewpoint information of a user. The processor is configured to render the at least one encoded image based on the metadata and the viewpoint.

Abstract: A user equipment (UE) includes a receiver and a processor. The receiver is configured to receive a standard dynamic range (SDR) image and metadata related to an HDR image. The processor is configured to identify relevant portions of the SDR image to be enhanced based on the metadata related to the HDR image. The processor is also configured to increase an intensity of the relevant portions of the SDR image to create an enhanced SDR image. The processor is also configured to output the enhanced SDR image to a display.

Abstract: A user equipment (UE) includes a receiver, display, and processor. The receiver is configured to receive a data stream including a plurality of frames. The data stream includes a region of interest in a key frame of the plurality of frames. The display is configured to display a portion of a frame of the plurality of frames. The processor is configured to perform an action to focus a current view of the UE to the region of interest in the key frame. Each frame of the plurality of frames includes a plurality of images stitched together to form a stitched image. The stitched image for at least one frame of the plurality of frames includes at least one high dynamic range (HDR) image and at least one standard dynamic range (SDR) image.

Abstract: A user equipment (UE) includes a memory element and a processor. The memory element is configured to store a plurality of head-related transfer functions. The processor is configured to receive an audio signal. The audio signal includes a plurality of ambisonic signals. The processor is also configured to identify an orientation of the UE based on physical properties of the UE. The processor is also configured to rotate the plurality of ambisonic signals based on the orientation of the UE. The processor is also configured to filter the plurality of ambisonic signals using the plurality of head-related transfer functions to form speaker signals. The processor is also configured to output the speaker signals.

Abstract: An embodiment of this disclosure provides an audio receiver. The audio receiver includes a memory configured to store an audio signal and processing circuitry coupled to the memory. The processing circuitry is configured to receive the audio signal. The audio signal comprises a plurality of ambisonic components. The processing circuitry is also configured to separate the audio signal into a plurality of independent ambisonic subcomponents such that each of the independent ambisonic subcomponents is from a different source. The processing circuitry is also configured to decode each of the independent ambisonic subcomponents. The processing circuitry is also configured to combine each of the decoded independent ambisonic subcomponents into speaker signals.

Abstract: The invention relates to compressing of sparse data sets contains sequences of data values and position information therefor. The position information may be in the form of position indices defining active positions of the data values in a sparse vector of length N. The position information is encoded into the data values by adjusting one or more of the data values within a pre-defined tolerance range, so that a pre-defined mapping function of the data values and their positions is close to a target value. In one embodiment, the mapping function is defined using a sub-set of N filler values which elements are used to fill empty positions in the input sparse data vector. At the decoder, the correct data positions are identified by searching though possible sub-sets of filler values.

Abstract: A method is disclosed for maintaining spatial queues in digital sound signals. Sound signals are received from each of a plurality of transducers. The sound signals are transformed using a common real-valued spectral gain, G, to maintain spatial cues within the sound signals, the common spectral gain, G, determined by: calculating G as a function of a derivative of a known cost function and as a function of at least one multichannel frequency-domain Bayesian short-time estimator.

Abstract: The invention relates to a method and apparatus for efficient encoding of media signals including audio. A 2d sparse representation, or spikegram, of one frame of a digitized audio signal is generated using an overcomplete set of kernels. The spikegram is then mapped to a non-negative matrix, which is decomposed into a 3D component matrix containing hidden components and a 3D weight matrix using a two-dimensional non-negative matrix factorization. Elements of the 3D component and weight matrices are then adaptively quantized using integer programming to determine an optimal quantization scheme, and the quantized values are the optionally encoded using an arithmetic coder.

Abstract: The invention relates to compressing of sparse data sets contains sequences of data values and position information therefor. The position information may be in the form of position indices defining active positions of the data values in a sparse vector of length N. The position information is encoded into the data values by adjusting one or more of the data values within a pre-defined tolerance range, so that a pre-defined mapping function of the data values and their positions is close to a target value. In one embodiment, the mapping function is defined using a sub-set of N filler values which elements are used to fill empty positions in the input sparse data vector. At the decoder, the correct data positions are identified by searching though possible sub-sets of filler values.

Abstract: The invention relates to sparse parallel signal coding using a neural network which parameters are adaptively determined in dependence on a pre-determined signal shaping characteristic. A signal is provides to a neural network encoder implementing a locally competitive algorithm for sparsely representing the signal. A plurality of interconnected nodes receive projections of the input signal, and each node generates an output once an internal potential thereof exceeds a node-dependent threshold value. The node-dependent threshold value for each of the nodes is set based upon the pre-determined shaping characteristic. In one embodiment, the invention enables to incorporate perceptual auditory masking in the sparse parallel coding of audio signals.

Abstract: A biologically-inspired process for universal audio coding based on neural spikes is presented. The process is based on the generation of sparse two-dimensional time-frequency representations of audio signals, called spikegrams. The spikegrams are generated by projecting the audio signal onto a set of over-complete adaptive gamma-chirp kernels. A masking model is applied to the spikegrams to remove inaudible spikes and to increase the coding efficiency. In respect of one aspect of the invention, the masked spikegram is then quantized using a genetic-algorithm-based quantizer (or its simplified linear version). The values are then differentially coded using graph based optimization and entropy coded afterwards.

Abstract: The present invention relates to a method for encoding an audio signal. In a first embodiment a model relating to temporal masking of sound provided to a human ear is provided. A temporal masking index is determined in dependence upon a received audio signal and the model using a forward and a backward masking function. Using a psychoacoustic model a masking threshold is determined in dependence upon the temporal masking index. Finally, the audio signal is encoded in dependence upon the masking threshold. The method has been implemented using the MPEG-1 psychoacoustic model 2. Semiformal listening test showed that using the method for encoding an audio signal according to the present invention the subjective high quality of the decoded compressed sounds has been maintained while the bit rate was reduced by approximately 10%. In a second embodiment, the inharmonic structure of audio signals is modeled and incorporated into the MPEG-1 psychoacoustic model 2.

Abstract: The present invention relates to a method for encoding an audio signal. In a first embodiment a model relating to temporal masking of sound provided to a human ear is provided. A temporal masking index is determined in dependence upon a received audio signal and the model using a forward and a backward masking function. Using a psychoacoustic model a masking threshold is determined in dependence upon the temporal masking index. Finally, the audio signal is encoded in dependence upon the masking threshold. The method has been implemented using the MPEG-1 psychoacoustic model 2. Semiformal listening test showed that using the method for encoding an audio signal according to the present invention the subjective high quality of the decoded compressed sounds has been maintained while the bit rate was reduced by approximately 10%. In a second embodiment, the inharmonic structure of audio signals is modeled and incorporated into the MPEG-1 psychoacoustic model 2.

Abstract: The present invention relates to a method for encoding an audio signal. In a first embodiment a model relating to temporal masking of sound provided to a human ear is provided. A temporal masking index is determined in dependence upon a received audio signal and the model using a forward and a backward masking function. Using a psychoacoustic model a masking threshold is determined in dependence upon the temporal masking index. Finally, the audio signal is encoded in dependence upon the masking threshold. The method has been implemented using the MPEG-1 psychoacoustic model 2. Semiformal listening test showed that using the method for encoding an audio signal according to the present invention the subjective high quality of the decoded compressed sounds has been maintained while the bit rate was reduced by approximately 10%. In a second embodiment, the inharmonic structure of audio signals is modeled and incorporated into the MPEG-1 psychoacoustic model 2.