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.

本文研究了极低比特率下的学习图像压缩（LIC）问题，提出了一种新颖的双流框架 HyrbidFlow，结合了基于连续特征和基于码书的流，以在极低比特率下实现高感知质量和高保真性的图像重建。实验结果表明，与现有的单流基于码书或基于连续特征的 LIC 方法相比，在极低比特率下具有优异的性能。