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(NTC), which over the past few years have become competitive with the best linear transform codecs for images, and have superseded them in terms of rate-distortion performance under established perceptual qu...
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| Vydáno v: | IEEE journal of selected topics in signal processing Ročník 15; číslo 2; s. 339 - 353 |
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| Hlavní autoři: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1932-4553, 1941-0484 |
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| Abstract | 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(NTC), which over the past few years have become competitive with the best linear transform codecs for images, and have superseded them in terms of rate-distortion performance under established perceptual quality metrics such as MS-SSIM.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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| AbstractList | 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(NTC), which over the past few years have become competitive with the best linear transform codecs for images, and have superseded them in terms of rate–distortion performance under established perceptual quality metrics such as MS-SSIM.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. |
| Author | Toderici, George Singh, Saurabh Agustsson, Eirikur Minnen, David Chou, Philip A. Balle, Jona Johnston, Nick Hwang, Sung Jin |
| Author_xml | – sequence: 1 givenname: Jona orcidid: 0000-0003-0769-8985 surname: Balle fullname: Balle, Jona email: jballe@google.com organization: Google Research, Mountain View, CA, USA – sequence: 2 givenname: Philip A. orcidid: 0000-0002-7242-0210 surname: Chou fullname: Chou, Philip A. email: philchou@google.com organization: Google Research, Mountain View, CA, USA – sequence: 3 givenname: David surname: Minnen fullname: Minnen, David email: dminnen@google.com organization: Google Research, Mountain View, CA, USA – sequence: 4 givenname: Saurabh surname: Singh fullname: Singh, Saurabh email: saurabhsingh@google.com organization: Google Research, Mountain View, CA, USA – sequence: 5 givenname: Nick surname: Johnston fullname: Johnston, Nick email: nickj@google.com organization: Google Research, Mountain View, CA, USA – sequence: 6 givenname: Eirikur surname: Agustsson fullname: Agustsson, Eirikur email: eirikur@google.com organization: Google Research, Mountain View, CA, USA – sequence: 7 givenname: Sung Jin surname: Hwang fullname: Hwang, Sung Jin email: sjhwang@google.com organization: Google Research, Mountain View, CA, USA – sequence: 8 givenname: George surname: Toderici fullname: Toderici, George email: gtoderici@google.com organization: Google Research, Mountain View, CA, USA |
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| Snippet | 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... |
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| SubjectTerms | 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 |
| Title | Nonlinear Transform Coding |
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