Abstract: This application provides a chroma block prediction method and apparatus. The method includes: obtaining chroma values of chroma samples; obtaining, luma values of luma samples corresponding to the chroma samples; obtaining, from the luma values, a first luma set and a second luma set; grouping the chroma values into a first chroma set and a second chroma set; determining a scaling coefficient in a linear model based on an average value of luma values in the first luma set, an average value of luma values in the second luma set, an average value of chroma values in the first chroma set, and an average value of chroma values in the second chroma set; determining, based on the scaling coefficient, an offset factor in the linear model; and determining prediction information of the chroma block based on the scaling coefficient and the offset factor.

Abstract: An example of the present disclosure includes a packaging box, comprising a first section including a first aperture to receive a portion of a writing device, and a second section including a second aperture to receive a remainder of the writing device. The packaging box further includes a hinge mechanism coupling the first section to the second section, wherein the first section and second section are rotatable about the hinge mechanism. The packaging box also includes a tip remover including a surface to remove a tip of the writing device responsive to application of a force.

Abstract: Embodiments of the present disclosure include techniques for simulating circuits, such as systems-on-a-chip (SoC). In various embodiments, a circuit being simulated may comprise one or more processors. However, simulation of the circuit using a hardware description language (HDL) may use a virtualized processor to execute firmware instructions rather than simulating the processor using HDL. Certain instructions from the virtualized processor may be sent, during simulation, to the HDL simulation for execution.

Abstract: A system for faster object attribute and/or intent classification may include an machine-learned (ML) architecture that processes temporal sensor data (e.g., multiple instances of sensor data received at different times) and includes a cache in an intermediate layer of the ML architecture. The ML architecture may be capable of classifying an object's intent to enter a roadway, idling near a roadway, or active crossing of a roadway. The ML architecture may additionally or alternatively classify indicator states, such as indications to turn, stop, or the like. Other attributes and/or intentions are discussed herein.

Abstract: A video coding mechanism is disclosed. The mechanism includes receiving a video signal partitioned into a chroma block and a first neighboring luma block. The mechanism also includes encoding prediction information for chroma samples of the chroma block into a bitstream. The prediction information for the chroma samples is determined via intra-prediction based on down-sampled neighboring luma samples. The down-sampled neighboring luma samples are generated from a single row of luma samples from the first neighboring luma block. A bitstream including the prediction information for the chroma samples is transmitted to support decoding and display of the chroma samples.

Abstract: This application provides a chroma block prediction method and apparatus. The method includes: obtaining a maximum luma value and a minimum luma value based on luma samples corresponding to neighboring samples of a target chroma block, and calculating a first difference between the maximum luma value and the minimum luma value; if the first difference is not equal to 0, processing the first difference based on a quantity of significant bits of the first difference and a first preset bit depth to obtain a second difference; and determining, based on a first chroma value, a second chroma value, and the second difference, an intra prediction model parameter corresponding to the target chroma block, and determining prediction information of the target chroma block based on the intra prediction model parameter and luma reconstruction information corresponding to the target chroma block.

Abstract: There is provided methods, systems, and computer-readable media for determining intention of bicycles and other person-wide vehicles. A method comprises: receiving, from one or more sensors of an autonomous vehicle, sensor data relating to an external environment of the autonomous vehicle; detecting, based at least in part on the sensor data, a person-wide vehicle in the external environment proximate to the autonomous vehicle; determining, based at least in part on the sensor data and the person-wide vehicle, image data including the person-wide vehicle; inputting the image data to a machine-learned model; receiving, from the machine-learned model based on the image data, a future intention of the person-wide vehicle; and controlling the autonomous vehicle based at least in part on the future intention of the person-wide vehicle.

Abstract: This application discloses a method for constructing an MPM list, a method for obtaining an intra prediction mode of a chroma block, and an apparatus. In a specific implementation, a MIP mode is used for a current block, and the method for constructing an MPM list includes: obtaining intra prediction mode information of a neighboring block; determining, based on the intra prediction mode information of the neighboring block, whether an intra prediction mode of the neighboring block is the MIP mode; and skipping using the intra prediction mode of the neighboring block for MPM list construction of the current block if the intra prediction mode of the neighboring block is not the MIP mode; or using the intra prediction mode of the neighboring block for MPM list construction of the current block if the intra prediction mode of the neighboring block is the MIP mode.

Abstract: Embodiments of the present disclosure include techniques for memory management. In various embodiments. a memory controller mirrors memory transactions on first data in a memory when the address for the transaction is within a first range. Transactions outside the first range may not be mirrored.

Abstract: Techniques are discussed herein for controlling autonomous vehicles within a driving environment, including generating and using bounding contours associated with objects detected in the environment. Image data may be captured and analyzed to identify and/or classify objects within the environment. Image-based and/or lidar-based techniques may be used to determine depth data associated with the objects, and a bounding contour may be determined based on the object boundaries and associated depth data. An autonomous vehicle may use the bounding contours of objects within the environment to classify the objects, predict the positions, poses, and trajectories of the objects, and determine trajectories and perform other vehicle control actions while safely navigating the environment.

Abstract: Disclosed are an inter prediction method based on an affine prediction mode and a related apparatus. The method includes: obtaining a predicted pixel value of a current affine block; obtaining an LIC flag of the current affine block, where the LIC flag is used to indicate whether local illumination compensation LIC is applied to the current affine block; and when the LIC flag indicates that LIC is applied to the current affine block, updating the predicted pixel value of the current affine block based on an update coefficient for LIC, to obtain an updated predicted pixel value of the current affine block.

Abstract: A picture prediction method is provided, which includes: obtaining initial motion information of a current picture block; determining, based on a matching cost criterion, that positions of a pair of reference blocks are a position of a target forward reference block of the current picture block and a position of a target backward reference block of the current picture block, where positions of each pair of reference blocks include a position of a forward reference block and a position of a backward reference block; and for the positions of each pair of reference blocks, a first position offset and a second position offset are in a mirror relationship; and obtaining a predicted value of a pixel value of the current picture block based on a pixel value of the target forward reference block and a pixel value of the target backward reference block.

Abstract: Techniques are described of a linear model prediction mode. A set of reconstructed neighboring luma samples are determined. Two pairs of luma value and chroma value are determined according to N reconstructed neighboring luma samples, N reconstructed neighboring chroma samples, M reconstructed neighboring luma samples, and M reconstructed neighboring chroma samples. The minimum value of the N reconstructed neighboring luma samples is not less than the maximum luma value of the remaining reconstructed neighboring luma samples of the set of reconstructed neighboring luma samples. The maximum value of the M reconstructed neighboring luma samples is not larger than the minimum luma value of the remaining reconstructed neighboring luma samples of the set of reconstructed neighboring luma samples. M>1 and N>1. Linear model parameters are determined based on the two pairs of luma value and chroma value, and a predictive block is determined based on the linear model parameters.

Abstract: The invention provide a rendering method and an electronic device. The method includes: first, obtaining channel feedback information of an encoded transmission channel, where the encoded transmission channel is used to transmit an encoded bitstream corresponding to a rendered image; subsequently, determining, from a plurality of groups of preset rendering input information, rendering input information corresponding to the channel feedback information, where complexity of the plurality of groups of preset rendering input information is different; then, performing rendering based on the rendering input information corresponding to the channel feedback information, to obtain the rendered image, where content complexity of the obtained rendered image is associated with complexity of the rendering input information corresponding to the channel feedback information.

Abstract: An intra prediction method by using cross component liner prediction mode (CCLM), includes: determining a luma block corresponding to a current chroma block; obtaining luma reference samples of the luma block based on determining L available chroma reference samples of the current chroma block, wherein the obtained luma reference samples of the luma block are down-sampled luma reference samples; calculating linear model coefficients based on the luma reference samples and chroma reference samples that correspond to the luma reference samples; and obtaining a prediction for the current chroma block based on the linear model coefficients and values of a down-sampled luma block of the luma block.

Abstract: The present disclosure provides a method of decoding a coded video bitstream, the method comprising: obtaining a picture-level syntax element included in a picture header of the current picture by parsing the video bitstream, wherein the picture-level syntax element is used to specify whether an adaptive loop filter (ALF) is enabled for the current picture; in case that the picture-level syntax element specifies the adaptive loop filter is enabled for the current picture, obtaining a parameter set identifier included in the picture header, wherein the parameter set identifier is used to indicate a first parameter set comprising a set of ALF parameters, and wherein a temporal identifier of the first parameter set is less than or equal to a temporal identifier of the current picture; performing the adaptive loop filter on at least one slice of the current picture based on the first parameter set.

Abstract: A method of decoding a coded video bitstream includes obtaining a sequence parameter set (SPS)-level syntax element from the bitstream, wherein that the SPS-level syntax element equaling a preset value specifies that no video parameter set (VPS) is referred to by a SPS, and wherein the SPS-level syntax element being greater than the preset value specifies that the SPS refers to a VPS. The method further includes obtaining, when the SPS-level syntax element is greater than the preset value, an inter-layer enabled syntax element specifying whether one or more inter-layer reference pictures (ILRPs) are enabled to be used for the inter prediction of one or more coded pictures, and predicting one or more coded pictures based on the value of the inter-layer enabled syntax element.

Abstract: During a computing system boot sequence, reference firmware provided by a computing system component supplies Advanced Configuration and Power Interface (ACPI) code that generates ACPI tables and definition blocks to a bootloader. During a boot sequence, the reference firmware receives an indication from the bootloader which components the reference firmware is to initialize. As part of component initialization performed by the reference firmware, the reference firmware populates hand-off data structures (e.g., hand-off blocks (HOBs)) with ACPI code (AML code) that, when executed by the bootloader, generates and populates ACPI tables (e.g., DSDT and SSDT tables) and definition blocks with information pertinent to the initialization and runtime management of computing system components. Component initialization and runtime configuration workarounds can be implemented in the bootloader incorporating reference firmware updates provided by the component vendor.

Abstract: This application provides a chroma block prediction method and apparatus. The method includes: obtaining a maximum luma value and a minimum luma value based on luma samples corresponding to neighboring samples of a target chroma block, and calculating a first difference between the maximum luma value and the minimum luma value; if the first difference is not equal to 0, processing the first difference based on a quantity of significant bits of the first difference and a first preset bit depth to obtain a second difference; and determining, based on a first chroma value, a second chroma value, and the second difference, an intra prediction model parameter corresponding to the target chroma block, and determining prediction information of the target chroma block based on the intra prediction model parameter and luma reconstruction information corresponding to the target chroma block.

Abstract: A system for faster object attribute and/or intent classification may include an machine-learned (ML) architecture that processes temporal sensor data (e.g., multiple instances of sensor data received at different times) and includes a cache in an intermediate layer of the ML architecture. The ML architecture may be capable of classifying an object's intent to enter a roadway, idling near a roadway, or active crossing of a roadway. The ML architecture may additionally or alternatively classify indicator states, such as indications to turn, stop, or the like. Other attributes and/or intentions are discussed herein.