DUALF-C: Disentangled Light Field Compression with Entropy-Aware Bitstream Generation

Recent advancements in learned light field (LF) image compression highlight the advantages of modeling spatial and angular redundancies using deep generative models. Among these, Variational Autoencoders (VAEs) have shown strong potential in learning compact latent representations of LF data. However, many existing approaches rely on simple uniform quantization and basic entropy coding, which limits compression efficiency in practical applications. This paper introduces DUALF-C, a lightweight and retraining-free compression pipeline that augments pretrained VAE-based LF compression models using structured post-encoding transformations. The proposed framework integrates bitplane slicing, latent channel reordering, non-uniform quantization, and patch-based vector quantization to improve bitrate efficiency while preserving reconstruction quality. Experimental evaluations demonstrate that DUALF-C significantly reduces bit-per-pixel (BPP) without degrading image quality, making it a practical solution for bandwidth-constrained immersive imaging systems.