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About this paper
Srivatsa Prativadibhayankaram, Mahadev Prasad Panda, Jürgen Seiler, Thomas Richter, Heiko Sparenberg, Siegfried Fößel, André Kaup
Researchers from Fraunhofer IIS and Friedrich-Alexander-Universität Erlangen-Nürnberg conducted a systematic study on the impact of RGB, YUV, and LAB color spaces on learned image compression performance. The analysis showed that a single-branch RGB model achieves the highest overall rate-distortion performance, with a 13.14% BD-BR gain in MS-SSIM and 17.96% in CIEDE2000 against VTM, while two-branch YUV and LAB architectures offer complexity reductions and specialized advantages for structural similarity or color accuracy.
Key insights
- The choice of color space significantly influences a learned image codec's rate-distortion performance, model complexity, and how it learns to represent structural versus color features.
- Two-branch architectures in perceptually inspired color spaces (YUV, LAB) enable explicit separation and independent processing of luminance/chrominance components, offering lower complexity and targeted optimization for specific quality metrics compared to a single-branch RGB model.
- Including a perceptually uniform color difference metric like CIEDE2000 in the loss function is highly effective at improving color fidelity in learned image compression, irrespective of the base color space.
Summary by alphaXiv.
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