Western Digital Technologies, Inc.

Daniel Bai

Quang Le

Kuok San Ho

Xiaoyong Liu

Rajeev Nagabhirava

Computing devices, such as mobile computing devices, have access to one or more image sensors that can capture images and video with multiple subjects. Some of these subjects may vary in priority for various tasks. It may be desired to increase or decrease the compression on each subject in order to more efficiently store the image data. Low-power, fast-response machine learning logic can be configured to allow for the generation of a plurality of inference data. Inference data can be associated with the type, motion and/or priority of the subjects as desired. This inference data can be utilized along with other subject data to generate one or more variable compression regions within the image data. The image data can be subsequently processed to compress different areas of the image based on a desired application. The variably compressed image can reduce file sizes and allow for more efficient storage and processing.

Low-power fast-response machine learning variable image compression

Computing devices, such as mobile computing devices, have access to one or more image sensors that can capture images with multiple subjects. Some of these subjects may be known to the user capturing an image with the image sensor. The user may prefer to have the captured image data be optimized around the known subjects. Low-power, fast-response machine learning logic can be configured to allow for the generation of a plurality of inference data. This inference data can be utilized along with other sensor data, such as a motion sensor, for the generation of one or more image sensor configuration changes that may be implemented to optimize the subsequent capture of image data. This cycle of image data analysis, image sensor optimization, and subsequent capture can continue multiple times until a threshold of optimization or time is met. The captured image data optimized around the known subjects is then stored.

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Freemont, CA, United States of America

Daniel Bai