Device Interoperability for Learned Image Compression with Weights and Activations Quantization

Learning-based image compression has improved to a level where it can outperform traditional image codecs such as HEVC and VVC in terms of coding performance. In addition to good compression performance, device interoperability is essential for a compression codec to be deployed, i.e., encoding and...

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Veröffentlicht in:Picture Coding Symposium S. 151 - 155
Hauptverfasser: Koyuncu, Esin, Solovyev, Timofey, Alshina, Elena, Kaup, Andre
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 07.12.2022
Schlagworte:
Codecs
Decoding
device interoperability
Entropy
Image coding
Interoperability
learning-based image compression
neural network quantization
Performance evaluation
Quantization (signal)
ISSN:2472-7822
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Abstract Learning-based image compression has improved to a level where it can outperform traditional image codecs such as HEVC and VVC in terms of coding performance. In addition to good compression performance, device interoperability is essential for a compression codec to be deployed, i.e., encoding and decoding on different CPUs or GPUs should be error-free and with negligible performance reduction. In this paper, we present a method to solve the device interoperability problem of a state-of-the-art image compression network. We implement quantization to entropy networks which output entropy parameters. We suggest a simple method which can ensure cross-platform encoding and decoding, and can be implemented quickly with minor performance deviation, of 0.3% BD-rate, from floating point model results.
AbstractList Learning-based image compression has improved to a level where it can outperform traditional image codecs such as HEVC and VVC in terms of coding performance. In addition to good compression performance, device interoperability is essential for a compression codec to be deployed, i.e., encoding and decoding on different CPUs or GPUs should be error-free and with negligible performance reduction. In this paper, we present a method to solve the device interoperability problem of a state-of-the-art image compression network. We implement quantization to entropy networks which output entropy parameters. We suggest a simple method which can ensure cross-platform encoding and decoding, and can be implemented quickly with minor performance deviation, of 0.3% BD-rate, from floating point model results.
Author Alshina, Elena
Koyuncu, Esin
Kaup, Andre
Solovyev, Timofey
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  givenname: Esin
  surname: Koyuncu
  fullname: Koyuncu, Esin
  email: andre.kaup@fau.de
  organization: Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nüurnberg,Erlangen,Germany
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  givenname: Timofey
  surname: Solovyev
  fullname: Solovyev, Timofey
  email: esin.koyuncu@fau.de
  organization: Audiovisual Laboratory, Munich Research Center Huawei Technologies,Munich,Germany
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  givenname: Elena
  surname: Alshina
  fullname: Alshina, Elena
  email: elena.alshina@huawei.com
  organization: Audiovisual Laboratory, Munich Research Center Huawei Technologies,Munich,Germany
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  givenname: Andre
  surname: Kaup
  fullname: Kaup, Andre
  email: solovyev.timofey@huawei.com
  organization: Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nüurnberg,Erlangen,Germany
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Snippet Learning-based image compression has improved to a level where it can outperform traditional image codecs such as HEVC and VVC in terms of coding performance....
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StartPage 151
SubjectTerms Codecs
Decoding
device interoperability
Entropy
Image coding
Interoperability
learning-based image compression
neural network quantization
Performance evaluation
Quantization (signal)
Title Device Interoperability for Learned Image Compression with Weights and Activations Quantization
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