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.

翻译：隐式神经表示（INRs）在图像表示与压缩领域取得了显著成功，但其训练时间和内存开销较大。与此同时，最近的二维高斯泼溅（GS）方法（例如GaussianImage）通过基于图元的高效渲染提供了有前景的替代方案。然而，这些方法需要过量的高斯图元以维持高视觉保真度。为充分挖掘基于GS方法的潜力，本文提出GaussianImage++，该方法利用有限的高斯图元实现了出色的表示与压缩性能。首先，我们引入一种失真驱动的致密化机制，该机制根据信号强度逐步分配高斯图元。其次，我们为每个图元采用上下文感知的高斯滤波器，这些滤波器辅助致密化过程，以根据不同的图像内容优化高斯图元。第三，我们集成了属性分离的可学习标量量化器与量化感知训练，从而实现对图元属性的高效压缩。实验结果验证了本方法的有效性。特别地，GaussianImage++在表示与压缩性能上均优于GaussianImage和基于INRs的COIN方法，同时保持了实时解码与低内存占用的优势。