Huawei Technologies Co., Limited

Marat Ravilevich Gilmutdinov

Elena Alexandrovna Alshina

Dmitry Vadimovich Novikov

Kirill Aleksandrovich Malakhov

Zhijie Zhao

Hu Chen

Nikita Vyacheslavovich Ustiuzhanin

Nickolay Dmitrievich Egorov

A method and device for digital signal processing are provided, particularly relating to video image processing. The device obtains image data comprising a plurality of pixels. The image data comprises a plurality of sequentially captured images. The device estimates, for a target image, a set of backward motion vector fields (backward MVFs) based on the target image, and a first set of images captured before the target image. The device further estimates a set of forward MVFs based on the target image and a second set of images captured after the target image. Depending on the estimating for the target image, the device generates an output image based on a merging procedure of the target image and the first set of images and the set of backward MVFs, and/or the second set of images and the set of forward MVFs.

Devices and methods for digital signal processing

An image processing apparatus processes a color filter mosaic, CFM, image of a scene into a final image of the scene. The image processing apparatus includes processing circuitry configured to implement a neural network. The neural network is configured to process the CFM image into an enhanced CFM image. The processing circuitry is further configured to transform the enhanced CFM image into the final image.

Image processing apparatus and method implementing a neural network to pre-process a CFM image for further process by ISP pipeline

A method for encoding a video image using coding parameters, adapted on the basis of motion of the video image and of an output of a machine-learning based model, wherein the machine-learning based model is input with samples of a block of the video image and motion information of the samples, and along with texture, wherein the machine-learning model segments the video image into regions based on strength of the motion determined from the motion information. An object is detected within the video based on the motion and the texture, and spatial-time coding parameters are determined based on the strength of the motion, and whether or not the detected objects moves.

Machine-learning-based adaptation of coding parameters for video encoding using motion and object detection

Growing community of inventors

Saint Petersburg, Russia

Marat Ravilevich Gilmutdinov