Content Adaptive Optimization for Neural Data Compression

Number: 20200366914
Published: 2020-11-19
Filed: 2019-05-15
Assignee: Disney Enterprises, Inc.
Inventors: Schroers; Christopher, Meierhans; Simon, Campos; Joaquim, McPhillen; Jared, Djelouah; Abdelaziz, Doggett; Erika Varis, Labrozzi; Scott, Xue; Yuanyi
CPC: G06N3/09; G06N7/01; G06N3/045; H04N19/513; G06N3/0455; H04N19/90; G06N3/02; H04N19/42; G06N3/0495; H04N19/186
Verdict: Set aside neural video compression/codec, generic plumbing
Source: Google Patents · FreePatentsOnline

Abstract

A data processing system includes a computing platform having a hardware processor and a memory storing a data compression software code. The hardware processor executes the data compression software code to receive a series of compression input data and encode a first compression input data of the series to a latent space representation of the first compression input data. The data compression software code further decodes the latent space representation to produce an input space representation of the first compression input data corresponding to the latent space representation, and generates f refined latent values for re-encoding the first compression input data based on a comparison of the first compression input data with its input space representation. The data compression software code then re-encodes the first compression input data using the refined latent values to produce a first compressed data corresponding to the first compression input data.

Background

BACKGROUND

A significant fraction of Internet traffic involves the transmission of video content, and that fraction will likely continue to increase into the foreseeable future. Because image compression is at the core of substantially all video coding approaches, improvements in the compression of image data are expected to have a significant and beneficial impact on the transmission of video as well. Traditional approaches to performing image compression have utilized compression codecs that rely on hand-crafting of individual components. More recently, several neural network based approaches for image compression have been developed.

In conventional neural network based approaches to image compression, a rate-distortion objective function is typically optimized over a corpus of images in order to find functions for encoding and decoding that are parameterized by a neural network. Once this optimization is complete, the training phase for the neural network is concluded and the encoder function is stored at a sender, while the decoder function is stored at a receiver.SUMMARY

There are provided systems and methods for performing content adaptive optimization for neural data compression, substantially as shown in and/or described in connection with at least one of the figures, and as set forth more completely in the claims.

Claims

1. A data processing system comprising: a computing platform including a hardware processor and a system memory storing a data compression software code, a trained neural encoder and a trained neural decoder; the hardware processor configured to execute the data compression software code to: receive a plurality of compression input data; encode, using the trained neural encoder, a first compression input data of the plurality of compression input data to a latent space representation of the first compression input data; decode, using the trained neural decoder, the latent space representation of the first compression input data to produce an input space representation of the first compression input data corresponding to the latent space representation of the first compression input data; generate first compression input data refined latent values based on a comparison of the first compression input data with the input space representation; and re-encode, using the trained neural encoder, the first compression input data using the first compression input data refined latent values to produce a first compressed data corresponding to the first compression input data. 13. A method for use by a data processing system including a computing platform having a hardware processor and a system memory storing a data compression software code, a trained neural encoder and a trained neural decoder, the method comprising: receiving, by the data compression software code executed by the hardware processor, a plurality of compression input data; encoding, using the trained neural encoder by the data compression software code executed by the hardware processor, a first compression input data of the plurality of compression input data to a latent space representation of the first compression input data; decoding, using the trained neural decoder by the data compression software code executed by the hardware processor, the latent space representation of the first compression input data to produce an input space representation of the first compression input data corresponding to the latent space representation of the first compression input data; generating, by the data compression software code executed by the hardware processor, first compression input data refined latent values based on a comparison of the first compression input data with the input space representation; and re-encoding, using the trained neural encoder by the data compression software code executed by the hardware processor, the first compression input data using the first compression input data refined latent values to produce a first compressed data corresponding to the first compression input data.