Mellanox Technologies, Ltd.

Adasky, Ltd.

Qualcomm Technologies, Inc.

Beamr Imaging Ltd.

Assaf Weissman

Dotan David Levi

Ohad Markus

Oleg Kuybeda

Eyal Madar

Eyal Frishman

Noam Bloch

Shie Mannor

Dror Porat

Aviad Raveh

Ariel Naftali Cohen

Gal Chechik

Kobi Pines

Erez Yaacov

Uri Gadot

Assaf Hallak

Eshed Ram

Tamar Shoham

Dror Gill

Pia Zobel

Pavel Titkov

Nikolay Terterov

Alexey Mitkovets

Alexey Martemyanov

Alexander Zheludkov

A system including an acceleration device including input circuitry configured, for each of a first plurality of video frames to be encoded, to receive an input including at least one raw video frame and at least one reference frame, and to divide each of the first plurality of video frames to be encoded into a second plurality of blocks, and similarity computation circuitry configured, for each one of the first plurality of video frame to be encoded: for each block of the second plurality of blocks, to produce a score of result blocks based on similarity of each block in each frame to be encoded to every block of the reference frame, an AC energy coefficient, and a displacement vector. Related apparatus and methods are also provided.

Application acceleration

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 video coding system including an acceleration device including input circuitry configured, for each of a first plurality of video frames to be encoded, to receive an input including at least one raw video frame and at least one reference frame, and to divide each of the first plurality of video frames to be encoded into a second plurality of blocks, and similarity computation circuitry configured, for each one of the first plurality of video frame to be encoded: for each the block of the second plurality of blocks, to produce an intra-prediction hint and an intra-prediction direction. Related apparatus and methods are also provided.

Video coding system

A system and method for correcting oversaturated pixels in far-infrared (FIR) images captured by a shutterless FIR camera, the method comprising: capturing thermal images by a FIR sensor in the shutterless FIR camera; processing the thermal images, by the shutterless FIR camera to determine pixel value and at least a shutterless sunburn correction, wherein the shutterless sunburn correction removes oversaturated pixels based on pixel-by-pixel analysis of the thermal image; and sending the processed thermal images to an output device.

Techniques for correcting oversaturated pixels in shutterless FIR cameras

A system including an acceleration device including input circuitry configured, for each of a first plurality of video frames to be encoded, to receive an input including at least one raw video frame and at least one reference frame, and to divide each of the first plurality of video frames to be encoded into a second plurality of blocks, and similarity computation circuitry configured, for each one of the first plurality of video frame to be encoded: for each the block of the second plurality of blocks, to produce a score of result blocks based on similarity of each the block in each frame to be encoded to every block of the reference frame, and a displacement vector. Related apparatus and methods are also provided.

Application accelerator

A video processor includes a memory and a processor. The processor is coupled to memory and is configured to store in the memory (i) multiple raw frames belonging to a Group of Pictures (GOP) to be processed, and (ii) one or more reference frames. The processor is further configured to select for multiple target blocks having a same block-location in respective raw frames associated with a common reference frame, a common search region in the common reference frame, and before selecting another search region, to apply at least two motion estimation operations using at least two of the target blocks and the common search region, to estimate respective at least two Motion Vectors (MVs).

Efficient video motion estimation by reusing a reference search region

A system and method for correcting oversaturated pixels in far-infrared (FIR) images captured by a shutterless FIR camera. The method includes identifying oversaturated pixels based on a pixel value analysis of an input image; creating an oversaturated pixel mask of the input image, where the oversaturated pixel mask includes the identified oversaturated pixels and excludes pixels not identified as oversaturated pixels; and correcting the oversaturated pixels of the current image using the oversaturated pixel mask, based on a scene-based nonuniformity correction (SBNC).

A system and method for correcting nonuniformity in far-infrared (FIR) images captured by a shutterless FIR camera. The method includes determining a noise of a current image based on updating a noise estimate of a previous image with a noise estimate of a current image; determining a weight mask matrix of the current image, where the weight matrix includes high values corresponding to pixels of the current image in which noise estimation is facilitated, and low values corresponding to pixels of the current image in which noise estimation is inhibited; applying the weight mask matrix to the current image; and correcting the nonuniformity of the current image incrementally based on the determined noise of current image and the applied weight mask matrix.

Techniques for scene-based nonuniformity correction in shutterless FIR cameras

A method, apparatus, and manufacture for generating an HDR image is provided. An original image is received from an HDR interlaced sensor that includes at least two fields captured with different exposures. The fields are separated from each other to provide separate images, and each of the separate images is upscaled. Next, blending is performed on each of the upscaled separate images to generate a high-dynamic range image, and ghost identification is performed on the high-dynamic range image. Subsequently, detail identification is performed on the high-dynamic range image. The detail identification includes identifying areas in the non-ghost areas of the high-dynamic range image that have details, and modifying the high-dynamic image by replacing each of the areas identified to have details with the corresponding area from the original image.

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Moreshet, Israel

Assaf Weissman