GaussianImage++: Boosted Image Representation and Compression with 2D Gaussian Splatting

• URL: http://arxiv.org/abs/2512.19108v2
• Date: Tue, 30 Dec 2025 12:59:41 GMT
• Title: GaussianImage++: Boosted Image Representation and Compression with 2D Gaussian Splatting
• Authors: Tiantian Li, Xinjie Zhang, Xingtong Ge, Tongda Xu, Dailan He, Jun Zhang, Yan Wang,
• Abstract summary: Implicit neural representations (INRs) have achieved remarkable success in image representation and compression.<n>Recent 2D Gaussian Splatting (GS) methods offer promising alternatives through efficient primitive-based rendering.<n>We present GaussianImage++, which utilizes limited Gaussian primitives to achieve impressive representation and compression performance.
• Score: 29.353296699942117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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