Bridging the Gap Between Image Coding for Machines and Humans
Image coding for machines (ICM) aims at reducing the bitrate required to represent an image while minimizing the drop in machine vision analysis accuracy. In many use cases, such as surveillance, it is also important that the visual quality is not drastically deteriorated by the compression process....
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| Veröffentlicht in: | Proceedings - International Conference on Image Processing S. 3411 - 3415 |
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16.10.2022
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| ISSN: | 2381-8549 |
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| Abstract | Image coding for machines (ICM) aims at reducing the bitrate required to represent an image while minimizing the drop in machine vision analysis accuracy. In many use cases, such as surveillance, it is also important that the visual quality is not drastically deteriorated by the compression process. Recent works on using neural network (NN) based ICM codecs have shown significant coding gains against traditional methods; however, the decompressed images, especially at low bitrates, often contain checkerboard artifacts. We propose an effective decoder finetuning scheme based on adversarial training to significantly enhance the visual quality of ICM codecs, while preserving the machine analysis accuracy, without adding extra bitcost or parameters at the inference phase. The results show complete removal of the checkerboard artifacts at the negligible cost of −1.6% relative change in task performance score. In the cases where some amount of artifacts is tolerable, such as when machine consumption is the primary target, this technique can enhance both pixel-fidelity and feature-fidelity scores without losing task performance. |
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| AbstractList | Image coding for machines (ICM) aims at reducing the bitrate required to represent an image while minimizing the drop in machine vision analysis accuracy. In many use cases, such as surveillance, it is also important that the visual quality is not drastically deteriorated by the compression process. Recent works on using neural network (NN) based ICM codecs have shown significant coding gains against traditional methods; however, the decompressed images, especially at low bitrates, often contain checkerboard artifacts. We propose an effective decoder finetuning scheme based on adversarial training to significantly enhance the visual quality of ICM codecs, while preserving the machine analysis accuracy, without adding extra bitcost or parameters at the inference phase. The results show complete removal of the checkerboard artifacts at the negligible cost of −1.6% relative change in task performance score. In the cases where some amount of artifacts is tolerable, such as when machine consumption is the primary target, this technique can enhance both pixel-fidelity and feature-fidelity scores without losing task performance. |
| Author | Cricri, Francesco Rahtu, Esa Aksu, Emre Le, Nam Rezazadegan Tavakoli, Hamed Zhang, Honglei Youvalari, Ramin G. Hannuksela, Miska M. |
| Author_xml | – sequence: 1 givenname: Nam surname: Le fullname: Le, Nam organization: Tampere University – sequence: 2 givenname: Honglei surname: Zhang fullname: Zhang, Honglei organization: Nokia Technologies – sequence: 3 givenname: Francesco surname: Cricri fullname: Cricri, Francesco organization: Nokia Technologies – sequence: 4 givenname: Ramin G. surname: Youvalari fullname: Youvalari, Ramin G. organization: Nokia Technologies – sequence: 5 givenname: Hamed surname: Rezazadegan Tavakoli fullname: Rezazadegan Tavakoli, Hamed organization: Nokia Technologies – sequence: 6 givenname: Emre surname: Aksu fullname: Aksu, Emre organization: Nokia Technologies – sequence: 7 givenname: Miska M. surname: Hannuksela fullname: Hannuksela, Miska M. organization: Nokia Technologies – sequence: 8 givenname: Esa surname: Rahtu fullname: Rahtu, Esa organization: Tampere University |
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| Snippet | Image coding for machines (ICM) aims at reducing the bitrate required to represent an image while minimizing the drop in machine vision analysis accuracy. In... |
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| SubjectTerms | Codecs finetuning GANs Image coding Image coding for machines Machine vision Measurement Surveillance Training VCM Visualization |
| Title | Bridging the Gap Between Image Coding for Machines and Humans |
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