STanH : Parametric Quantization for Variable Rate Learned Image Compression
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910646016147456 |
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| author | Presta, Alberto Tartaglione, Enzo Fiandrotti, Attilio Grangetto, Marco |
| author_facet | Presta, Alberto Tartaglione, Enzo Fiandrotti, Attilio Grangetto, Marco |
| contents | In end-to-end learned image compression, encoder and decoder are jointly trained to minimize a $R + λD$ cost function, where $λ$ controls the trade-off between rate of the quantized latent representation and image quality. Unfortunately, a distinct encoder-decoder pair with millions of parameters must be trained for each $λ$, hence the need to switch encoders and to store multiple encoders and decoders on the user device for every target rate. This paper proposes to exploit a differentiable quantizer designed around a parametric sum of hyperbolic tangents, called STanH , that relaxes the step-wise quantization function. STanH is implemented as a differentiable activation layer with learnable quantization parameters that can be plugged into a pre-trained fixed rate model and refined to achieve different target bitrates. Experimental results show that our method enables variable rate coding with comparable efficiency to the state-of-the-art, yet with significant savings in terms of ease of deployment, training time, and storage costs |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00557 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | STanH : Parametric Quantization for Variable Rate Learned Image Compression Presta, Alberto Tartaglione, Enzo Fiandrotti, Attilio Grangetto, Marco Computer Vision and Pattern Recognition Multimedia In end-to-end learned image compression, encoder and decoder are jointly trained to minimize a $R + λD$ cost function, where $λ$ controls the trade-off between rate of the quantized latent representation and image quality. Unfortunately, a distinct encoder-decoder pair with millions of parameters must be trained for each $λ$, hence the need to switch encoders and to store multiple encoders and decoders on the user device for every target rate. This paper proposes to exploit a differentiable quantizer designed around a parametric sum of hyperbolic tangents, called STanH , that relaxes the step-wise quantization function. STanH is implemented as a differentiable activation layer with learnable quantization parameters that can be plugged into a pre-trained fixed rate model and refined to achieve different target bitrates. Experimental results show that our method enables variable rate coding with comparable efficiency to the state-of-the-art, yet with significant savings in terms of ease of deployment, training time, and storage costs |
| title | STanH : Parametric Quantization for Variable Rate Learned Image Compression |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2410.00557 |