HybridFlow: Infusing Continuity into Masked Codebook for Extreme Low-Bitrate Image Compression
<|reference_start|>HybridFlow: Infusing Continuity into Masked Codebook for Extreme Low-Bitrate Image Compression: This paper investigates the challenging problem of learned image compression (LIC) with extreme low bitrates. Previous LIC methods based on transmitting quantized continuous features often yield blurry and noisy reconstruction due to the severe quantization loss. While previous LIC methods based on learned codebooks that discretize visual space usually give poor-fidelity reconstruction due to the insufficient representation power of limited codewords in capturing faithful details. We propose a novel dual-stream framework, HyrbidFlow, which combines the continuous-feature-based and codebook-based streams to achieve both high perceptual quality and high fidelity under extreme low bitrates. The codebook-based stream benefits from the high-quality learned codebook priors to provide high quality and clarity in reconstructed images. The continuous feature stream targets at maintaining fidelity details. To achieve the ultra low bitrate, a masked token-based transformer is further proposed, where we only transmit a masked portion of codeword indices and recover the missing indices through token generation guided by information from the continuous feature stream. We also develop a bridging correction network to merge the two streams in pixel decoding for final image reconstruction, where the continuous stream features rectify biases of the codebook-based pixel decoder to impose reconstructed fidelity details. Experimental results demonstrate superior performance across several datasets under extremely low bitrates, compared with existing single-stream codebook-based or continuous-feature-based LIC methods.<|reference_end|>
@article{lu2024hybridflow:,
    title={HybridFlow: Infusing Continuity into Masked Codebook for Extreme
  Low-Bitrate Image Compression},
    author={Lei Lu, Yanyue Xie, Wei Jiang, Wei Wang, Xue Lin, Yanzhi Wang},
    journal={arXiv preprint arXiv:2404.13372},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13372},
    primaryClass={eess.IV cs.CV}
}