Related papers: Streaming Neural Images

Streaming Neural Images

URL: http://arxiv.org/abs/2409.17134v1
Date: Wed, 25 Sep 2024 17:51:20 GMT
Title: Streaming Neural Images
Authors: Marcos V. Conde, Andy Bigos, Radu Timofte,
Abstract summary: Implicit Neural Representations (INRs) are a novel paradigm for signal representation that have attracted considerable interest for image compression. In this work, we explore the critical yet overlooked limiting factors of INRs, such as computational cost, unstable performance, and robustness.
Score: 56.41827271721955
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: Implicit Neural Representations (INRs) are a novel paradigm for signal representation that have attracted considerable interest for image compression. INRs offer unprecedented advantages in signal resolution and memory efficiency, enabling new possibilities for compression techniques. However, the existing limitations of INRs for image compression have not been sufficiently addressed in the literature. In this work, we explore the critical yet overlooked limiting factors of INRs, such as computational cost, unstable performance, and robustness. Through extensive experiments and empirical analysis, we provide a deeper and more nuanced understanding of implicit neural image compression methods such as Fourier Feature Networks and Siren. Our work also offers valuable insights for future research in this area.

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