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Bridging the Gap Between Implicit and Explicit Representation for Efficient Image Compression
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DOI：10.1109/tcsvt.2026.3687408.png)
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Abstract
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Neural Image Compression (NIC) has achieved superior compression performance by modeling images as implicit feature representations, yet its practical deployment is severely hindered by computational overhead. Recently, GaussianImage was proposed as a computationally efficient explicit image representation paradigm, which renders images from 2D Gaussians. However, it suffers from inferior compression performance relative to mainstream NIC frameworks, mainly due to low compressibility and representation capability of explicit Gaussian parameters. To this end, we propose Pixel-Aligned Generalized 2D Splatting (PA-G2DS) as a computation-efficient and compression-friendly image representation format. Specifically, we deploy a learnable rendering function with implicit coefficients to enhance the reconstructed image quality and improve the compression ratio over explicit Gaussian coefficients. Incorporating the proposed PA-G2DS as a computationally efficient decoder, we further develop a suite of image codecs optimized for either compression ratios or flexible deployment scenarios. Experiments prove that the proposed codecs could achieve 30ms compression latency and millisecond-level decompression latency, reducing the performance and efficiency gap between implicit and explicit image representation. Furthermore, the proposed codec opens potential applications for NIC such as JPEG-like sequential decompression and random-access during decompression.
Keywords:
Image compression
Gaussian splatting
efficient coding
Journal
IF:
11.1
Papers:
873
Citations:
3.1W
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