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This work proposes a post-processing pipeline that augments an RVQ-based compressor with a U-Net trained to predict and correct pixel-space residuals between the original volume and its RVQ reconstruction, and is the first pipeline to combine latent-space RVQ, explicit pixel-space residual correction via a deep spatial...
CAESAR-LDAR is presented, an error-controlled multivariate learned compressor that augments a shared CAESAR-V backbone with two complementary mechanisms: a trainable orthogonal transform that reorganizes dependence across aligned latent channels, and a causal autoregressive hierarchical prior that captures local spatia...
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