Abstract: When encoding/decoding a current block of a current picture using intra block copy (“BC”) prediction, the location of a reference block is constrained so that it can be entirely within an inner search area of the current picture or entirely within an outer search area of the current picture, but cannot overlap both the inner search area and the outer search area. In some hardware-based implementations, on-chip memory buffers sample values of the inner search area, and off-chip memory buffers sample values of the outer search area. By enforcing this constraint on the location of the reference block, an encoder/decoder can avoid memory access operations that are split between on-chip memory and off-chip memory when retrieving the sample values of the reference block. At the same time, a reference block close to the current block may be used for intra BC prediction, helping compression efficiency.

Abstract: Techniques and solutions are described for encoding and decoding signals, such as audio data. Disclosed innovations can find particular use in speech coding applications, such as for real time communications. Using a neural network, contextual coding can be used to encode latent features for a current frame using a prediction from reconstructed latent features of past frames as a context. An extractor learns a residual-like feature based on such prediction and latent features of the current frame obtained using an encoder. The residual-like feature is then quantized. At a decoder portion of a coding framework, the quantized feature is dequantized and then combined with a prediction from prior reconstructed latent features to provide reconstructed features of a current frame, which can then be processed by a decoder to provide a reconstructed signal.

Abstract: A format for use in encoding moving image data, comprising: a sequence of frames including plurality of the frames in which at least a region is encoded using motion estimation; a respective set of motion vector values representing motion vectors of the motion estimation for each respective one of these frames or each respective one of one or more regions within each of such frames; and at least one respective indicator associated with each of the respective frames or regions, indicating whether the respective motion vector values of the respective frame or region are encoded at a first resolution or a second resolution.

Abstract: A format for use in encoding moving image data, comprising: a sequence of frames including plurality of the frames in which at least a region is encoded using motion estimation; a respective set of motion vector values representing motion vectors of the motion estimation for each respective one of these frames or each respective one of one or more regions within each of such frames; and at least one respective indicator associated with each of the respective frames or regions, indicating whether the respective motion vector values of the respective frame or region are encoded at a first resolution or a second resolution.

Abstract: Apparatus and methods are disclosed for using machine learning models with private and public domains. Operations can be applied to transform input to a machine learning model in a private domain that is kept secret or otherwise made unavailable to third parties. In one example of the disclosed technology, a method includes applying a private transform to produce transformed input, providing the transformed input to a machine learning model that was trained using a training set modified by the private transform, and generating inferences with the machine learning model using the transformed input. Examples of suitable transforms that can be employed include matrix multiplication, time or spatial domain to frequency domains, and partitioning a neural network model such that an input and at least one hidden layer form part of the private domain, while the remaining layers form part of the public domain.

Abstract: Implementations of the subject matter described herein provide a solution for rate control based on reinforcement learning. In this solution, an encoding state of a video encoder is determined, the encoding state being associated with encoding of a first video unit by the video encoder. An encoding parameter associated with rate control in the video encoder is determining by a reinforcement learning model and based on the encoding state of the video encoder. A second video unit different from the first video unit is encoded based on the encoding parameter. In this way, it is possible to achieve a better quality of experience (QOE) for real time communication with computation overhead being reduced.

Abstract: Implementations of the subject matter described herein provide a solution for rate control based on reinforcement learning. In this solution, an encoding state of a video encoder is determined, the encoding state being associated with encoding of a first video unit by the video encoder. An encoding parameter associated with rate control in the video encoder is determined by a reinforcement learning model and based on the encoding state of the video encoder. A second video unit different from the first video unit is encoded based on the encoding parameter. In this way, it is possible to achieve a better quality of experience (QOE) for real time communication with computation overhead being reduced.

Abstract: When encoding/decoding a current block of a current picture using intra block copy (“BC”) prediction, the location of a reference block is constrained so that it can be entirely within an inner search area of the current picture or entirely within an outer search area of the current picture, but cannot overlap both the inner search area and the outer search area. In some hardware-based implementations, on-chip memory buffers sample values of the inner search area, and off-chip memory buffers sample values of the outer search area. By enforcing this constraint on the location of the reference block, an encoder/decoder can avoid memory access operations that are split between on-chip memory and off-chip memory when retrieving the sample values of the reference block. At the same time, a reference block close to the current block may be used for intra BC prediction, helping compression efficiency.

Abstract: When encoding/decoding a current block of a current picture using intra block copy (“BC”) prediction, the location of a reference block is constrained so that it can be entirely within an inner search area of the current picture or entirely within an outer search area of the current picture, but cannot overlap both the inner search area and the outer search area. In some hardware-based implementations, on-chip memory buffers sample values of the inner search area, and off-chip memory buffers sample values of the outer search area. By enforcing this constraint on the location of the reference block, an encoder/decoder can avoid memory access operations that are split between on-chip memory and off-chip memory when retrieving the sample values of the reference block. At the same time, a reference block close to the current block may be used for intra BC prediction, helping compression efficiency.

Abstract: Innovations in adaptive encoding of screen content based on motion type are described. For example, a video encoder system receives a current picture of a video sequence. The video encoder system determines a current motion type for the video sequence and, based at least in part on the current motion type, sets one or more encoding parameters. Then, the video encoder system encodes the current picture according to the encoding parameter(s). The innovations can be used in real-time encoding scenarios when encoding screen content for a screen sharing application, desktop conferencing application, or other application. In some cases, the innovations allow a video encoder system to adapt compression to different characteristics of screen content at different times within the same video sequence.

Abstract: Apparatus and methods are disclosed for using machine learning models with private and public domains. Operations can be applied to transform input to a machine learning model in a private domain that is kept secret or otherwise made unavailable to third parties. In one example of the disclosed technology, a method includes applying a private transform to produce transformed input, providing the transformed input to a machine learning model that was trained using a training set modified by the private transform, and generating inferences with the machine learning model using the transformed input. Examples of suitable transforms that can be employed include matrix multiplication, time or spatial domain to frequency domains, and partitioning a neural network model such that an input and at least one hidden layer form part of the private domain, while the remaining layers form part of the public domain.

Abstract: Implementations of the subject matter described herein provide a solution for rate control based on reinforcement learning. In this solution, an encoding state of a video encoder is determined, the encoding state being associated with encoding of a first video unit by the video encoder. An encoding parameter associated with rate control in the video encoder is determined by a reinforcement learning model and based on the encoding state of the video encoder. A second video unit different from the first video unit is encoded based on the encoding parameter. In this way, it is possible to achieve a better quality of experience (QOE) for real time communication with computation overhead being reduced.

Abstract: Apparatus and methods are disclosed for using machine learning models with private and public domains. Operations can be applied to transform input to a machine learning model in a private domain that is kept secret or otherwise made unavailable to third parties. In one example of the disclosed technology, a method includes applying a private transform to produce transformed input, providing the transformed input to a machine learning model that was trained using a training set modified by the private transform, and generating inferences with the machine learning model using the transformed input. Examples of suitable transforms that can be employed include matrix multiplication, time or spatial domain to frequency domains, and partitioning a neural network model such that an input and at least one hidden layer form part of the private domain, while the remaining layers form part of the public domain.

Abstract: Techniques are described for efficiently encoding video data by skipping evaluation of certain encoding modes based on various evaluation criteria. In some solutions, intra-block evaluation is performed in a specific order during encoding, and depending on encoding cost calculations of potential intra-block encoding modes, evaluation of some of the potential modes can be skipped. In some solutions, some encoding modes can be skipped depending on whether blocks are simple (e.g., simple vertical, simple horizontal, or both) or non-simple. In some solutions, various criteria are applied to determine whether chroma-from-luma mode evaluation can be skipped. The various solutions can be used independently and/or in combination.

Abstract: An electronic device selectively coupled to a first charger and/or a second charger includes a power supply interface, a first comparator, a second comparator, a controller, a first switch circuit, and a second switch circuit. The power supply interface receives a first input voltage and a second input voltage. The first comparator compares the first input voltage with a first reference voltage, so as to generate a first comparison voltage. The second comparator compares the second input voltage with a second reference voltage, so as to generate a second comparison voltage. The controller generates a first control voltage and a second control voltage according to the first comparison voltage and the second comparison voltage. The first switch circuit is selectively enabled or disabled according to the first control voltage. The second switch circuit is selectively enabled or disabled according to the second control voltage.

Abstract: Innovations in adaptive encoding of screen content based on motion type are described. For example, a video encoder system receives a current picture of a video sequence. The video encoder system determines a current motion type for the video sequence and, based at least in part on the current motion type, sets one or more encoding parameters. Then, the video encoder system encodes the current picture according to the encoding parameter(s). The innovations can be used in real-time encoding scenarios when encoding screen content for a screen sharing application, desktop conferencing application, or other application. In some cases, the innovations allow a video encoder system to adapt compression to different characteristics of screen content at different times within the same video sequence.

Abstract: Innovations in allocation of bit rate between video streams using machine learning are described. For example, a controller of a video encoder system receives first feedback values that indicate results of encoding part of a first video sequence (e.g., screen content). The controller also receives second feedback values that indicate results of encoding part of a second video sequence (e.g., camera video content). A machine learning model accepts, as inputs, the first feedback values and second feedback values. The machine learning model produces, as output, a reallocation parameter. The controller determines a first target bit rate and a second target bit rate using the reallocation parameter. A first video encoder encodes one or more pictures of the first video sequence at the first target bit rate, and a second video encoder encodes one or more pictures of the second video sequence at the second target bit rate.

Abstract: Techniques are described for efficiently encoding video data by skipping evaluation of certain encoding modes based on various evaluation criteria. In some solutions, intra-block evaluation is performed in a specific order during encoding, and depending on encoding cost calculations of potential intra-block encoding modes, evaluation of some of the potential modes can be skipped. In some solutions, some encoding modes can be skipped depending on whether blocks are simple (e.g., simple vertical, simple horizontal, or both) or non-simple. In some solutions, various criteria are applied to determine whether chroma-from-luma mode evaluation can be skipped. The various solutions can be used independently and/or in combination.

Abstract: A format for use in encoding moving image data, comprising: a sequence of frames including plurality of the frames in which at least a region is encoded using motion estimation; a respective set of motion vector values representing motion vectors of the motion estimation for each respective one of these frames or each respective one of one or more regions within each of such frames; and at least one respective indicator associated with each of the respective frames or regions, indicating whether the respective motion vector values of the respective frame or region are encoded at a first resolution or a second resolution.

Abstract: When encoding/decoding a current block of a current picture using intra block copy (“BC”) prediction, the location of a reference block is constrained so that it can be entirely within an inner search area of the current picture or entirely within an outer search area of the current picture, but cannot overlap both the inner search area and the outer search area. In some hardware-based implementations, on-chip memory buffers sample values of the inner search area, and off-chip memory buffers sample values of the outer search area. By enforcing this constraint on the location of the reference block, an encoder/decoder can avoid memory access operations that are split between on-chip memory and off-chip memory when retrieving the sample values of the reference block. At the same time, a reference block close to the current block may be used for intra BC prediction, helping compression efficiency.