Machine learning based video compression
AssigneeDisney Enterprises, Inc.
InventorsChristopher Schroers, Simone Schaub, Erika Doggett, Jared McPhillen, Scott Labrozzi, Abdelaziz Djelouah
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Abstract
Systems and methods are disclosed for compressing a target video. A computer-implemented method may use a computer system that include one or more physical computer processors and non-transient electronic storage. The computer-implemented method may include: obtaining the target video, extracting one or more frames from the target video, and generating an estimated optical flow based on a displacement of pixels between the one or more frames. The one or more frames may include one or more of a key frame and a target frame.
Claims20
A computer-implemented method for compressing a target video, the method being implemented in a computer system that includes one or more physical computer processors and non-transient electronic storage, comprising:
- obtaining, from the non-transient electronic storage, the target video;
- extracting, with the one or more physical computer processors, one or more frames from the target video, wherein the one or more frames comprise one or more of a key frame and a target frame;
- and
- generating, with the one or more physical computer processors, an estimated optical flow based on a displacement of pixels between the one or more frames.
A system comprising:
- non-transient electronic storage;
- and
- one or more physical computer processors configured by machine-readable instructions to:
- obtain, from the non-transient electronic storage, the target video;
- extract, with the one or more physical computer processors, one or more frames from the target video, wherein the one or more frames comprise one or more of a key frame and a target frame;
- and
- generate, with the one or more physical computer processors, an estimated optical flow based on a displacement of pixels between the one or more frames.
A non-transitory computer-readable medium having executable instructions stored thereon that, when executed by one or more physical computer processors, cause the one or more physical computer processors to perform operations of:
- obtaining the target video;
- extracting one or more frames from the target video, wherein the one or more frames comprise one or more of a key frame and a target frame;
- generating an estimated optical flow based on a displacement of pixels between the one or more frames;
- and
- applying the estimated optical flow to a trained optical flow model to generate a refined optical flow, the trained optical flow model having been trained by using optical flow training data, wherein the optical flow training data comprises (i) optical flow data, (ii) a corresponding residual, (iii) a corresponding warped frame, and (iv) a corresponding target frame.
Dependencies are inferred from the claim text.