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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| Vydané v: | Picture Coding Symposium s. 151 - 155 |
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| Hlavní autori: | , , , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
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IEEE
07.12.2022
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| 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. |
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| 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 |
| Author_xml | – sequence: 1 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 – sequence: 2 givenname: Timofey surname: Solovyev fullname: Solovyev, Timofey email: esin.koyuncu@fau.de organization: Audiovisual Laboratory, Munich Research Center Huawei Technologies,Munich,Germany – sequence: 3 givenname: Elena surname: Alshina fullname: Alshina, Elena email: elena.alshina@huawei.com organization: Audiovisual Laboratory, Munich Research Center Huawei Technologies,Munich,Germany – sequence: 4 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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