Qualcomm Incorporated

Stone Yun

Alireza Shoa Hassani Lashdan

Darren Gnanapragasam

David Hansen

Haoping Xu

Jeffrey Kar Fai Wong

Sivakumar Chidambaram

Hooshmand Shokri Razaghi

Techniques and systems are provided for image processing. For instance, a process can include generating motion vectors representing differences between a past image of a video and a current image of a video, wherein the current image occurs after the past image in the video, wherein the motion vectors are generated using a first machine learning model; applying the motion vectors to a third image to generate a warped third image; generating blending weights based on a comparison between the warped third image and the current image, wherein the blending weights are generated using a second machine learning model; upscaling the current image to generate an upscaled current image, wherein the upscaled current image is upscaled by a third machine learning model; combining the upscaled current image and a warped fourth image using the blending weights to generate an upscaled output image; and outputting the upscaled output image.

Machine learning based temporal stabilization of images

Technologies are provided for processing data in neural networks. An example method can include processing, by each layer of a neural network, a row in a first stripe of a data input, the row being processed sequentially in a horizontal direction and according to a layer-by-layer sequence; after processing the row, processing, by each layer, subsequent rows in the first stripe on a row-by-row basis, each subsequent row being processed sequentially in the horizontal direction and according to the layer-by-layer sequence; generating an output stripe based on the processing of the row and subsequent rows; processing, by each layer, a second stripe of the data input, each row in the second stripe being processed in the horizontal direction and according to the layer-by-layer sequence, wherein rows in the second stripe are processed on a row-by-row basis; and generating another output stripe based on the processing of the second stripe.

Processing data in pixel-to-pixel neural networks

A device includes a memory and one or more processors. The memory is configured to store an image enhancement network of an image enhancer. The one or more processors are configured to predict an image compression quality of an image of a stream of images. The one or more processors are also configured to configure the image enhancer based on the image compression quality. The one or more processors are further configured to process, using the image enhancement network of the configured image enhancer, the image to generate an enhanced image.

Configurable image enhancement

A device includes a memory and one or more processors. The memory is configured to store instructions. The one or more processors are configured to execute the instructions to apply a neural network to a first image to generate an enhanced image. The one or more processors are also configured to execute the instructions to adjust at least a portion of a high-frequency component of the enhanced image based on a motion compensation operation to generate an adjusted high-frequency image component. The one or more processors are further configured to execute the instructions to combine a low-frequency component of the enhanced image and the adjusted high-frequency image component to generate an adjusted enhanced image.

Motion compensation for neural network enhanced images

A device includes a memory configured to store an adapted network that is configured to generate a modified image based on a single image. The device includes a processor configured to obtain, from a stream of video data, a first distorted image depicting an object, and to provide the first distorted image to the adapted network to generate a first modified image. The processor is configured to obtain, from the stream of video data, a second distorted image depicting the object, and to provide the second distorted image to the adapted network to generate a second modified image. The object is distorted differently in the second distorted image than in the first distorted image. The processor is configured to generate a video output including the first modified image and the second modified image without visible artifacts due to distortion differences between the first distorted image and the second distorted image.

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Markham, Canada

Stone Yun