Mellanox Technologies, Ltd.

Pointgrab Ltd.

Eyal Frishman

Dotan David Levi

Assaf Weissman

Ohad Markus

Shie Mannor

Dror Porat

Gal Chechik

Assaf Hallak

Eshed Ram

Ora Zackay

Jonathan Laserson

Gilboa Levy

Moshe Nakash

Mark Ginzburg

In one embodiment, a system includes a hardware accelerator to receive video data of multiple video frames, divide each of the video frames into respective blocks, compute encoding assist data including at least one video encoding parameter type for each of the respective blocks of each of the video frames, and store respective portions of the encoding assist data across respective database tables, and an interface to provide the respective database tables to video encoding software running on a processor.

Hardware accelerated video encoding

A system includes a processing device to receive video content, metadata related to the video content, and a target bit rate for encoding the video content. The processing device further detects a content type of the video content based on the metadata and encodes hardware to perform frame encoding on the video content. The system further includes a controller coupled between the processing device and the encoding hardware. The controller is programmed with machine instructions to generate first QP values on a per-frame basis using a frame machine learning model with a first plurality of weights. The first plurality of weights depends at least in part on the content type and the target bit rate. The controller further provides the first QP values to the encoding hardware for rate control of the frame encoding.

Content-aware, machine-learning-based rate control

A system includes a processing device to receive a video content, a quality metric, and a target bit rate for encoding the video content. The system includes encoding hardware to perform frame encoding on the video content and a controller coupled between the processing device and the encoding hardware. The controller is programmed with machine instructions to generate first QP values on a per-frame basis using a frame machine learning model with a first plurality of weights. The first plurality of weights depends at least in part on the quality metric and the target bit rate. The controller is further programmed to provide the first QP values to the encoding hardware for rate control of the frame encoding.

Quality-metric-agnostic rate control

A method and system for determining occupancy in a space, include determining presence of an occupant in a space based a signal from a PIR sensor monitoring the space and on analysis of an image of the space. Assigning different weights to the PIR signal and image analysis enables controlling a device in the space differently.

Method and system for detecting occupancy in a space

Growing community of inventors

Hod Hasharon, Israel

Eyal Frishman