MACHINE LEARNING BASED VIDEO COMPRESSION

Number: 20200053388
Published: 2020-02-13
Filed: 2019-01-29
Assignee: Disney Enterprises, Inc.
Inventors: Schroers; Christopher, Schaub; Simone, Doggett; Erika, McPhillen; Jared, Labrozzi; Scott, Djelouah; Abdelaziz
CPC: H04N19/537; H04N19/436; H04N19/587; H04N19/503; H04N19/54
Verdict: Set aside ML video compression codec, plumbing
Source: Google Patents · FreePatentsOnline

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.

Background

TECHNICAL FIELD

The present disclosure relates generally to video compression.BRIEF SUMMARY OF THE EMBODIMENTS

Embodiments of the present disclosure include systems and methods of compressing video using machine learning. In accordance with the technology described herein, a computer-implemented method for compressing a target video is disclosed. The computer-implemented method may be implemented in a computer system that may include one or more physical computer processors and non-transient electronic storage. The computer-implemented method may include obtaining, from the non-transient electronic storage, the target video. The computer-implemented method may include extracting, with the one or more physical computer processors, one or more frames from the target video. The one or more frames may include one or more of a key frame and a target frame. The computer-implemented method may also include 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.

In embodiments, the displacement of pixels may be between a key frame and/or the target frame.

In embodiments, the computer-implemented method may further include applying, with the one or more physical computer processors, the estimated optical flow to a trained optical flow model to generate a refined optical flow. The trained optical flow model may have been trained by using optical flow training data.

Claims

1. 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. 10. 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. 20. 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.