Abstract: An apparatus includes a processor configured to determine a first distribution associated with an artificial agent based on behavior associated with the artificial agent and a second distribution based on behavior of a user. The processor is further configured to generate a human-likeness similarity measurement by comparing the first distribution to the second distribution and modify the behavior of the artificial agent in response to the similarity measurement failing to satisfy a similarity threshold.

Abstract: Virtual Reality (VR) processing devices and methods are provided for transmitting user feedback information comprising at least one of user position information and user orientation information, receiving encoded audio-video (AN) data, which is generated based on the transmitted user feedback information, separating the A/V data into video data and audio data corresponding to a portion of a next frame of a sequence of frames of the video data to be displayed, decoding the portion of a next frame of the video data and the corresponding audio data, providing the audio data for aural presentation and controlling the portion of the next frame of the video data to be displayed in synchronization with the corresponding audio data.

Abstract: An encoding method is provided which includes receiving a plurality of images, obtaining values of elements in a portion of the images, sorting the elements according to different values of the elements, sorting the elements according to a number of occurrences of the different values and encoding the elements using a subset of the different values having corresponding numbers of occurrences that are higher than corresponding numbers of occurrences of other values. Examples also include a processing device and method for use with palette mode encoding in which the elements are a portion of pixels in images and the values are color values of the portion of pixels in the images.

Abstract: Calculating, for each frame of a plurality of frames, a corresponding quality value; calculating, for each frame of the plurality of frames, based on one or more visual attributes of a frame, a weight for the corresponding quality value of the frame; calculating an aggregate quality value for the plurality of frames based on the weight and the corresponding quality value for each frame of the plurality of frames; and providing an assessment of the plurality of frames based on the aggregate quality value for the plurality of frames.

Abstract: A processing device for executing a machine learning neural network operation includes memory and a processor. The processor is configured to receive input data at a layer of the machine learning neural network operation, receive a plurality of sorted filters to be applied to the input data, apply the plurality of sorted filters to the input data to produce a plurality of different feature maps, compress the plurality of different feature maps according to a similarity of the feature maps relative to each other and store the plurality of different feature maps in the memory.

Abstract: Virtual Reality (VR) processing devices and methods are provided for transmitting user feedback information comprising at least one of user position information and user orientation information, receiving encoded audio-video (A/V) data, which is generated based on the transmitted user feedback information, separating the A/V data into video data and audio data corresponding to a portion of a next frame of a sequence of frames of the video data to be displayed, decoding the portion of a next frame of the video data and the corresponding audio data, providing the audio data for aural presentation and controlling the portion of the next frame of the video data to be displayed in synchronization with the corresponding audio data.

Abstract: A processing device for executing a machine learning neural network operation includes memory and a processor. The processor is configured to receive input data at a layer of the machine learning neural network operation, receive a plurality of sorted filters to be applied to the input data, apply the plurality of sorted filters to the input data to produce a plurality of different feature maps, compress the plurality of different feature maps according to a sparsity of the feature maps and store the plurality of different feature maps in the memory.

Abstract: Methods and devices are provided for encoding video. By using co-sited gradient and variance values to detect text and line in frames of the video. A processor is configured to receive a plurality of frames of video, determine, for a portion of a frame, a variance of the portion of the frame and a gradient of the portion of the frame and encode, using one of a plurality of different encoding qualities, the portion of the frame based on the gradient and the variance of the portion of the frame. Encoding is performed at both the sub-frame level and frame level. The portion of the frame is classified into one of a plurality of categories based on the gradient and variance and encoded based on the category.

Abstract: Methods and apparatus provide cloud-based video encoding that generates encoded video data by one or more encoders in a cloud platform for a plurality of cloud encoding sessions. The methods and apparatus generate operational improvement tradeoff data in response to operational encoding metrics associated with the one or more encoders and change operational characteristics of the one or more encoders for at least one of the cloud encoding sessions based on the operational improvement tradeoff data.

Abstract: An apparatus includes a processor and a collision detection unit operatively coupled to the processor. The collision detection unit is configured to process, using a machine learning model, one or more parameters associated with a ray cast in virtual environment comprising an object. The machine learning model is configured to approximate a mesh representing the object. The collision detection unit is further configured to determine if the ray collides with the object based on processing the one or more parameters. In response to determining if the ray collides with the object, the collision detection unit is configured to generate collision data associated with the ray and the object.

Abstract: Systems, apparatuses, and methods for creating human-like non-player character (NPC) behavior with reinforcement learning (RL) are disclosed. An artificial intelligence (AI) engine creates a NPC that has seamless movement when accompanying a player controlled by a user playing a video game. The AI engine is RL-trained to stay close to the player but not get in the player's way while acting in a human-like manner. Also, the AI engine is RL-trained to evaluate the quality of information that is received over time from other AI engines and then to act on the evaluated information quality. Each AI agent is trained to evaluate the other AI agents and determine whether another AI agent is a friend or a foe. In some cases, groups of AI agents collaborate together to either help or hinder the player. The capabilities of each AI agent are independent from the capabilities of other AI agents.

Abstract: Methods and apparatus provide cloud-based video encoding that generates encoded video data by one or more encoders in a cloud platform for a plurality of cloud encoding sessions. The methods and apparatus generate operational improvement tradeoff data in response to operational encoding metrics associated with the one or more encoders and change operational characteristics of the one or more encoders for at least one of the cloud encoding sessions based on the operational improvement tradeoff data.

Abstract: Methods and devices are provided for encoding video. By using co-sited gradient and variance values to detect text and line in frames of the video. A processor is configured to receive a plurality of frames of video, determine, for a portion of a frame, a variance of the portion of the frame and a gradient of the portion of the frame and encode, using one of a plurality of different encoding qualities, the portion of the frame based on the gradient and the variance of the portion of the frame. Encoding is performed at both the sub-frame level and frame level. The portion of the frame is classified into one of a plurality of categories based on the gradient and variance and encoded based on the category.

Abstract: A processing apparatus and video encoding method are provided which include receiving a portion of a video sequence and determining complexities for blocks of pixels of the portion of the video sequence. Quantization parameter values for corresponding blocks of pixels are selected based on complexities of the corresponding blocks and visually perceived coding artifacts of the corresponding blocks produced by the quantization parameter values. The blocks of pixels are encoded, using the selected quantization parameter values. The blocks of pixels are decoded and the portion of the video sequence is provided for display.

Abstract: Mapping machine learning activation data to a representative value palette, including: selecting, from a plurality of activation values of a model execution, a plurality of representative values; identifying, for each activation value of the plurality of activation values, a representative value of the plurality of representative values; calculating, for each activation value of the plurality of activation values, a corresponding residual value as a difference between an activation value and a corresponding representative value; and storing, for each activation value of the plurality of activation values, the corresponding residual value and an index of the corresponding representative value.

Abstract: Systems, apparatuses, and methods for performing adaptive audio mixing are disclosed. A trained neural network dynamically selects and mixes pre-recorded, human-composed music stems that are composed as mutually compatible sets. Stem and track selection, volume mixing, filtering, dynamic compression, acoustical/reverberant characteristics, segues, tempo, beat-matching and crossfading parameters generated by the neural network are inferred from the game scene characteristics and other dynamically changing factors. The trained neural network selects an artist's pre-recorded stems and mixes the stems in real-time in unique ways to dynamically adjust and modify background music based on factors such as game scenario, the unique storyline of the player, scene elements, the player's profile, interest, and performance, adjustments made to game controls (e.g., music volume), number of viewers, received comments, player's popularity, player's native language, player's presence, and/or other factors.

Abstract: Systems, apparatuses, and methods for generating a model for determining a quantization strength to use when encoding video frames are disclosed. A pre-encoder performs multiple encoding passes using different quantization strengths on a portion or the entirety of one or more pre-processed video frames. The pre-encoder captures the bit-size of the encoded output for each of the multiple encoding passes. Then, based on the multiple encoding passes, the pre-encoder generates a model for mapping bit-size to quantization strength for encoding video frames or portion(s) thereof. When the encoder begins the final encoding pass for one or more given video frames or any portion(s) thereof, the encoder uses the model to map a preferred bit-size to a given quantization strength. The encoder uses the given quantization strength when encoding the given video frame(s) or frame portion(s) to meet a specified bit-rate for the encoded bitstream.

Abstract: Systems, apparatuses, and methods for reducing latency when consuming an encoded video bitstream in real-time are disclosed. A video encoder encodes a video bitstream and writes chunks of the encoded bitstream to a bitstream buffer. Prior to the encoder completing the encoding of an entire frame, or an entire slice of a frame, a consumer module consumes encoded chunks of the bitstream. In one implementation, to enable pipelining of the consumption with the encoding, the encoder updates a buffer write pointer with an indication of the amount of data that has been written to the bitstream buffer. The consumer module retrieves encoded data from the bitstream buffer up to the location indicated by the buffer write pointer. In this way, the consumer module is able to access and consume encoded video data prior to the encoder finishing encoding an entire frame or an entire slice of the frame.

Abstract: An encoding method is provided which includes receiving a plurality of images, obtaining values of elements in a portion of the images, sorting the elements according to different values of the elements, sorting the elements according to a number of occurrences of the different values and encoding the elements using a subset of the different values having corresponding numbers of occurrences that are higher than corresponding numbers of occurrences of other values. Examples also include a processing device and method for use with palette mode encoding in which the elements are a portion of pixels in images and the values are color values of the portion of pixels in the images.

Abstract: Calculating, for each frame of a plurality of frames, a corresponding quality value; calculating, for each frame of the plurality of frames, based on one or more visual attributes of a frame, a weight for the corresponding quality value of the frame; calculating an aggregate quality value for the plurality of frames based on the weight and the corresponding quality value for each frame of the plurality of frames; and providing an assessment of the plurality of frames based on the aggregate quality value for the plurality of frames.