The disclosure relates to an artificial intelligence (AI) system that uses a machine learning algorithm and an application thereof. A method for controlling an electronic apparatus according to the disclosure includes receiving image data and information associated with a filter set that is applied to an artificial intelligence model for upscaling the image data from an external server; decoding the image data; upscaling the decoded image data using a first artificial intelligence model that is obtained based on the information associated with the filter set; and providing the upscaled image data for output.

A better compromise between encoding complexity and achievable rate distortion ratio, and/or to achieve a better rate distortion ratio is achieved by using multitree sub-divisioning not only in order to subdivide a continuous area, namely the sample array, into leaf regions, but using the intermediate regions also to share coding parameters among the corresponding collocated leaf blocks. By this measure, coding procedures performed in tiles—leaf regions—locally, may be associated with coding parameters individually without having to, however, explicitly transmit the whole coding parameters for each leaf region separately. Rather, similarities may effectively exploited by using the multitree subdivision.

An apparatus and method for multi-adapter and/or multi-pass encoding on dual graphics processors. For example, one embodiment of a processor comprises: a central processor integrated on a first die, the central processor comprising a plurality of cores to execute instructions and process data; an first graphics processor integrated on the first die, the first graphics processor comprising media processing circuitry to perform one or more preliminary lookahead operations on video content to generate lookahead statistics; an interconnect to couple the first graphics processor to a lookahead buffer, the first graphics processor to transmit the lookahead statistics over the interconnect to the lookahead buffer; wherein the lookahead statistics are to be used by a second graphics processor to encode the video content to generate encoded video.

Systems, apparatuses, and methods for reducing latency for wireless virtual and augmented reality applications are disclosed. A virtual reality (VR) or augmented reality (AR) system includes a transmitter rendering, encoding, and sending video frames to a receiver coupled to a head-mounted display (HMD). In one scenario, rather than waiting until the entire frame is encoded before sending the frame to the receiver, the transmitter sends an encoded left-eye portion to the receiver while the right-eye portion is being encoded. In another scenario, the frame is partitioned into a plurality of slices, and each slice is encoded and then sent to the receiver while the next slice is being encoded. In a further scenario, each slice is being encoded while the next slice is being rendered. In a still further scenario, each slice is prepared for presentation by the receiver while the next slice is being decoded by the receiver.

A better compromise between encoding complexity and achievable rate distortion ratio, and/or to achieve a better rate distortion ratio is achieved by using multitree sub-divisioning not only in order to subdivide a continuous area, namely the sample array, into leaf regions, but using the intermediate regions also to share coding parameters among the corresponding collocated leaf blocks. By this measure, coding procedures performed in tiles—leaf regions—locally, may be associated with coding parameters individually without having to, however, explicitly transmit the whole coding parameters for each leaf region separately. Rather, similarities may effectively exploited by using the multitree subdivision.

Video compression and decompression techniques are disclosed that provide improved bandwidth control for video compression and decompression systems. In particular, video coding and decoding techniques quantize input video in multiple dimensions. According to these techniques, pixel residuals may be generated from a comparison of an array of input data to an array of prediction data. The pixel residuals may be quantized in a first dimension. After the quantization, the quantized pixel residuals may be transformed to an array of transform coefficients. The transform coefficients may be quantized in a second dimension and entropy coded. Decoding techniques invert these processes. In still other embodiments, multiple quantizers may be provided upstream of the transform stage, either in parallel or in cascade, which provide greater flexibility to video coders to quantize data in different dimensions in an effort to balance the competing interest in compression efficiency and quality of reconstructed video.

The disclosure relates to an artificial intelligence (AI) system that uses a machine learning algorithm and an application thereof. A method for controlling an electronic apparatus according to the disclosure includes receiving image data and information associated with a filter set that is applied to an artificial intelligence model for upscaling the image data from an external server; decoding the image data; upscaling the decoded image data using a first artificial intelligence model that is obtained based on the information associated with the filter set; and providing the upscaled image data for output.

Systems, apparatuses, and methods for reducing latency for wireless virtual and augmented reality applications are disclosed. A virtual reality (VR) or augmented reality (AR) system includes a transmitter rendering, encoding, and sending video frames to a receiver coupled to a head-mounted display (HMD). In one scenario, rather than waiting until the entire frame is encoded before sending the frame to the receiver, the transmitter sends an encoded left-eye portion to the receiver while the right-eye portion is being encoded. In another scenario, the frame is partitioned into a plurality of slices, and each slice is encoded and then sent to the receiver while the next slice is being encoded. In a further scenario, each slice is being encoded while the next slice is being rendered. In a still further scenario, each slice is prepared for presentation by the receiver while the next slice is being decoded by the receiver.

A better compromise between encoding complexity and achievable rate distortion ratio, and/or to achieve a better rate distortion ratio is achieved by using multitree sub-divisioning not only in order to subdivide a continuous area, namely the sample array, into leaf regions, but using the intermediate regions also to share coding parameters among the corresponding collocated leaf blocks. By this measure, coding procedures performed in tiles—leaf regions—locally, may be associated with coding parameters individually without having to, however, explicitly transmit the whole coding parameters for each leaf region separately. Rather, similarities may effectively exploited by using the multitree subdivision.

A better compromise between encoding complexity and achievable rate distortion ratio, and/or to achieve a better rate distortion ratio is achieved by using multitree sub-divisioning not only in order to subdivide a continuous area, namely the sample array, into leaf regions, but using the intermediate regions also to share coding parameters among the corresponding collocated leaf blocks. By this measure, coding procedures performed in tiles—leaf regions—locally, may be associated with coding parameters individually without having to, however, explicitly transmit the whole coding parameters for each leaf region separately. Rather, similarities may effectively exploited by using the multitree subdivision.