Nonlinear Transform Coding

We review a class of methods that can be collected under the name nonlinear transform coding (NTC), which over the past few years have become competitive with the best linear transform codecs for images, and have superseded them in terms of rate-distortion performance under established perceptual qu...

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Bibliographic Details
Published in:IEEE journal of selected topics in signal processing Vol. 15; no. 2; pp. 339 - 353
Main Authors: Balle, Jona, Chou, Philip A., Minnen, David, Singh, Saurabh, Johnston, Nick, Agustsson, Eirikur, Hwang, Sung Jin, Toderici, George
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
Codec
Coding
data compression
Distortion
Empirical analysis
Entropy
Image coding
Linear transformations
machine learning
Optimization
Optimization techniques
rate-distortion
source coding
Stochastic processes
transform coding
Transforms
unsupervised learning
Vector quantization
ISSN:1932-4553, 1941-0484
Online Access:Get full text
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Summary:We review a class of methods that can be collected under the name nonlinear transform coding (NTC), which over the past few years have become competitive with the best linear transform codecs for images, and have superseded them in terms of rate-distortion performance under established perceptual quality metrics such as MS-SSIM. We assess the empirical rate-distortion performance of NTC with the help of simple example sources, for which the optimal performance of a vector quantizer is easier to estimate than with natural data sources. To this end, we introduce a novel variant of entropy-constrained vector quantization. We provide an analysis of various forms of stochastic optimization techniques for NTC models; review architectures of transforms based on artificial neural networks, as well as learned entropy models; and provide a direct comparison of a number of methods to parameterize the rate-distortion trade-off of nonlinear transforms, introducing a simplified one.
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ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2020.3034501