Abstract
Deep variational autoencoders for image and video compression have gained significant attraction in the recent years, due to their potential to offer competitive or better compression rates compared to the decades long traditional codecs such as AVC, HEVC or VVC. However, because of complexity and energy consumption, these approaches are still far away from practical usage in industry. More recently, implicit neural representation (INR) based codecs have emerged, and have lower complexity and energy usage to classical approaches at decoding. However, their performances are not in par at the moment with state-of-the-art methods. In this research, we first show that INR based image codec has a lower complexity than VAE based approaches, then we propose several improvements for INR-based image codec and outperformed baseline model by a large margin.
Abstract (translated)
图像和视频压缩的深度学习变分自编码在过去几年中吸引了广泛关注,因为它们相较于数十年来传统的编解码器,可以提供更具竞争力或更好的压缩率。然而,由于复杂性和能源消耗,这些方法仍然远远未被 industry 实际应用。最近,基于隐含神经网络表示(INR)的编解码器出现了,其在解码时的复杂性和能源消耗比传统的方法低得多。然而,目前其性能尚未达到先进方法的水平。在这项研究中,我们首先表明,基于 INR 的图像编解码器的复杂性比 VAE based 的方法低得多,然后我们提出了多个改进,以优化基于 INR 的图像编解码器,并显著优于基准模型。
URL
https://arxiv.org/abs/2303.03028
