Achieving successful variable bitrate compression with computationally simple
algorithms from a single end-to-end learned image or video compression model
remains a challenge. Many approaches have been proposed, including conditional
auto-encoders, channel-adaptive gains for the latent tensor or uniformly
quantizing all elements of the latent tensor. This paper follows the
traditional approach to vary a single quantization step size to perform uniform
quantization of all latent tensor elements. However, three modifications are
proposed to improve the variable rate compression performance. First, multi
objective optimization is used for (post) training. Second, a
quantization-reconstruction offset is introduced into the quantization
operation. Third, variable rate quantization is used also for the hyper latent.
All these modifications can be made on a pre-trained single-rate compression
model by performing post training. The algorithms are implemented into three
well-known image compression models and the achieved variable rate compression
results indicate negligible or minimal compression performance loss compared to
training multiple models. (Codes will be shared at
this https URL)

通过在预训练的单速率压缩模型上进行后训练，本研究提出了三种改进方法，包括多目标优化、引入量化 - 重建偏移和使用变速率量化。在三个著名的图像压缩模型上实施这些算法后，实现了可变速率压缩，且与训练多个模型相比，压缩性能几乎没有或只有微小的损失。