GaussianImage++: 通过2D高斯散射提升图像表示与压缩
GaussianImage++: Boosted Image Representation and Compression with 2D Gaussian Splatting
AI总结：
GaussianImage++通过有限的高斯原始体实现高效的图像表示与压缩，结合驱动扭曲的密集化机制和上下文感知滤波器，提升性能并降低内存消耗。
摘要：
隐式神经表示（INRs）在图像表示和压缩中取得了显著成功，但它们需要大量的训练时间和内存。同时，最近的2D高斯散射（GS）方法（例如GaussianImage）通过高效的原始基渲染提供了有前途的替代方案。然而，这些方法需要大量的高斯原始体来维持高质量的视觉保真度。为了利用GS方法的潜力，我们提出了GaussianImage++，它利用有限的高斯原始体来实现出色的表示和压缩性能。首先，我们引入了一种驱动扭曲的密集化机制。它根据信号强度逐步分配高斯原始体。其次，我们为每个原始体采用上下文感知的高斯滤波器，这有助于密集化以优化高斯原始体，根据变化的图像内容进行优化。第三，我们集成了属性分离的可学习标量量化器和量化感知训练，使原始体属性的压缩变得高效。实验结果证明了我们方法的有效性。特别是，GaussianImage++在表示和压缩性能上优于GaussianImage和基于INRs的COIN，同时保持了实时解码和低内存使用。
英文摘要：
Implicit neural representations (INRs) have achieved remarkable success in image representation and compression, but they require substantial training time and memory. Meanwhile, recent 2D Gaussian Splatting (GS) methods (\textit{e.g.}, GaussianImage) offer promising alternatives through efficient primitive-based rendering. However, these methods require excessive Gaussian primitives to maintain high visual fidelity. To exploit the potential of GS-based approaches, we present GaussianImage++, which utilizes limited Gaussian primitives to achieve impressive representation and compression performance. Firstly, we introduce a distortion-driven densification mechanism. It progressively allocates Gaussian primitives according to signal intensity. Secondly, we employ context-aware Gaussian filters for each primitive, which assist in the densification to optimize Gaussian primitives based on varying image content. Thirdly, we integrate attribute-separated learnable scalar quantizers and quantization-aware training, enabling efficient compression of primitive attributes. Experimental results demonstrate the effectiveness of our method. In particular, GaussianImage++ outperforms GaussianImage and INRs-based COIN in representation and compression performance while maintaining real-time decoding and low memory usage.
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