Controlling Rate, Distortion, and Realism: Towards a Single Comprehensive Neural Image Compression Model
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| Format: | Preprint |
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2024
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| _version_ | 1866914813153640448 |
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| author | Iwai, Shoma Miyazaki, Tomo Omachi, Shinichiro |
| author_facet | Iwai, Shoma Miyazaki, Tomo Omachi, Shinichiro |
| contents | In recent years, neural network-driven image compression (NIC) has gained significant attention. Some works adopt deep generative models such as GANs and diffusion models to enhance perceptual quality (realism). A critical obstacle of these generative NIC methods is that each model is optimized for a single bit rate. Consequently, multiple models are required to compress images to different bit rates, which is impractical for real-world applications. To tackle this issue, we propose a variable-rate generative NIC model. Specifically, we explore several discriminator designs tailored for the variable-rate approach and introduce a novel adversarial loss. Moreover, by incorporating the newly proposed multi-realism technique, our method allows the users to adjust the bit rate, distortion, and realism with a single model, achieving ultra-controllability. Unlike existing variable-rate generative NIC models, our method matches or surpasses the performance of state-of-the-art single-rate generative NIC models while covering a wide range of bit rates using just one model. Code will be available at https://github.com/iwa-shi/CRDR |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_16817 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Controlling Rate, Distortion, and Realism: Towards a Single Comprehensive Neural Image Compression Model Iwai, Shoma Miyazaki, Tomo Omachi, Shinichiro Computer Vision and Pattern Recognition Image and Video Processing In recent years, neural network-driven image compression (NIC) has gained significant attention. Some works adopt deep generative models such as GANs and diffusion models to enhance perceptual quality (realism). A critical obstacle of these generative NIC methods is that each model is optimized for a single bit rate. Consequently, multiple models are required to compress images to different bit rates, which is impractical for real-world applications. To tackle this issue, we propose a variable-rate generative NIC model. Specifically, we explore several discriminator designs tailored for the variable-rate approach and introduce a novel adversarial loss. Moreover, by incorporating the newly proposed multi-realism technique, our method allows the users to adjust the bit rate, distortion, and realism with a single model, achieving ultra-controllability. Unlike existing variable-rate generative NIC models, our method matches or surpasses the performance of state-of-the-art single-rate generative NIC models while covering a wide range of bit rates using just one model. Code will be available at https://github.com/iwa-shi/CRDR |
| title | Controlling Rate, Distortion, and Realism: Towards a Single Comprehensive Neural Image Compression Model |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2405.16817 |